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11 Posts authored by: Justin Norris Expert

This year was the first summit for Marketo users since the Adobe acquisition in 2018. For me, it brought home the reality that Marketo is no longer a standalone product but a single app within a much larger structure of product “clouds.”


adobe clouds


Although Marketo is no longer the sole focus of its own multi-day event, the union with Adobe could bring benefits to Marketo users who are willing to expand their portfolio of Adobe products. It’s not hard to imagine how less well-developed Marketo features could be replaced by integrations with much more mature and robust Adobe products — for example, Ad Bridge by Ad Cloud or Web Personalization by Adobe Target.


That being said, there are many Adobe products for Marketo users to understand and make sense of. It can feel bewildering, and I find it takes time to build a mental map of how all the products fit together. Some of the non-Marketo content in the keynotes was useful for showcasing what these products can do and is worth a watch for the Experience Cloud-curious.


Let’s dive into some of the new Marketo-specific features and integrations discussed at Summit.


Safe Harbour: This post does not represent any kind of official “roadmap.” It’s just one person’s observations based on attending keynotes and sessions. Given the increased size of Summit this year, I can’t promise it will be comprehensive. The features mentioned may be at various stages of development, or even still just ideas, and sessions did not offer specific details on timelines.



Branding Changes

While not technically a product feature, the branding changes we saw at Summit were significant.


Marketo Engage

Marketo was referred to as “Marketo Engage” throughout keynotes and on signage in the expo hall. It’s clear that “Marketo” as a single-word brand is going to disappear.


Marketo Sales Engage (Marketo’s sales enablement/automation tool) was described as Marketo Sales Connect.


Adobe Sensei

One component of the Adobe platform mentioned frequently in conjunction with Marketo (I mean...Marketo Engage) was Adobe Sensei. Sensei refers to a set of machine-learning / artificial-intelligence capabilities found throughout the Adobe product suite.


The branding creates an impression that Sensei is a single, unified platform layer embedded in many places. However, I also saw pre-existing Marketo features that leverage machine learning described as “Adobe Sensei.” My impression is that this branding describes any machine learning component across the Adobe product family and may or may not indicate a common underlying technology on the back-end.


Microsoft + LinkedIn Partnership

Steve Lucas announced a new partnership between Adobe, Microsoft, and (Microsoft-owned) LinkedIn called the Account-Based Experience (ABX) Initiative.

The announcement was high-level. However, some of the examples of how this initiative might impact marketers include:


  • The ability to hydrate profiles in Marketo Engage or Microsoft Dynamics CRM (DCRM) with real-time data to better identify account-based buying teams. (Aside: when did “hydrating a profile” become a thing? Are profiles plants?)
  • The ability to align account data with LinkedIn information. Steve emphasized that LinkedIn data remained on LinkedIn but indicated we would be able to “align” first-party data with LinkedIn signals in some way.
  • The ability to message people on LinkedIn directly without needing to log in to the LinkedIn interface.

The details on this are all still a bit sketchy. However, my take-away is that Microsoft’s purchase of LinkedIn is going to start to bear fruit for DCRM and Adobe customers in some way. I don’t see Salesforce users being invited to this party.

Read more at the Adobe news release.


Marketing Activities

Unless otherwise noted, all new UI-related features appear to be available only in Marketo Sky.


Event Program Registration Cap

The built-in capability to cap registrants for event programs is much-requested functionality that otherwise requires some complex workarounds to achieve.


With this feature, users can set the registration limit for an event or webinar on a per-program basis. The feature will also waitlist people automatically once the cap has been reached and allow you to specify a fallback page to use when the registration limit has been reached.




If using this feature, I recommend that a “Wait List” landing page should become a standard include for tokenized webinar/event program templates, along with registration and thank you pages.


Event Program Goals

This feature allows users to specify goals for both registration and attendance. Marketo Engage will track progress against goals and even proactively notify you if it detects a risk of not meeting your goals, so you can take appropriate action. (See more detail in predictive suggestions below.)


One screenshot shown at Summit depicted goal progress tracking that appeared on the My Marketo homescreen, which seems like a potentially valuable use of that real estate.



Predictions and Recommended Actions

Machine learning is clearly going to be an ongoing presence in the marketing stack, in a way that does more than offer token homage to a trend. Nearly every substantive feature announcement had a dash of Adobe Sensei in it somewhere. And a prime example of machine learning being woven into everyday marketing activities is the “predictions and recommended actions” feature.


Once you define your goal, machine learning will predict how likely each invitee or program member is to register or attend the event and determines how likely you are to meet your goals.



If it predicts you will fall short, Marketo Engage will recommend actions to help you reach your targets. For example, it can identify people similar to the smart list audience via lookalike modelling and suggest you invite those people.



In one session, the presenter even showed a hypothetical user receiving a goal-related warning via the Marketo Moments app and then, from within the mobile app, triggering an ad campaign to boost registration. I suspect this type of application is much further away from becoming reality (I wasn’t aware Marketo Moments was even still supported), but “smart list expansion” type recommendations seem quite achievable and something we might see in the not-too-distant future.



Marketo refers to the machine-learning technology as “glass box” rather than black box — meaning they intend to be transparent about which factors are being evaluated and factored in to recommendations. I believe the efficacy of these recommendations will depend on whether the right signals are being included in the algorithm.


Predictive Smart List Filters

Predictive smart list filters allow you to proactively define audiences using machine learning. Instead of constructing smart lists with reference to static lead properties or past behaviors, this feature would enable users to select audiences based on predicted likelihood to take a particular action. The threshold is configurable as part of the filter constraint.


You can also select an audience using a lookalike filter, based on an audience that achieved a particular status in another program.



“Sensei” models thousands of signals to pick the right audience, and one PM noted this resulted in significant performance improvements in some early experiments.


Program Member Custom Fields

This is another long-awaited and potentially game-changing feature. It extends the Marketo Engage data-model to include additional custom dimensions on the program member — similar to custom fields on the campaign member object in Salesforce.


The classic use case for this is capturing “chicken or fish” type meal choices when someone registers for an event, but it could be used to store any type of data point that describes a property related to the person/program junction.


Another important use case is storing UTM parameters related to a form fill at the program level. This paves the way for more robust offer/channel modelling in Marketo.



It remains to be seen how this data will be exposed in Marketo, and how this element of the feature is executed will determine its ultimate value. If data is only visible in the program members tab or accessible via smart list constraints on triggers and filters, it will be valuable but limited.


If the data is exposed in reports and can be synced to equivalent fields on the campaign member in Salesforce, the applications will be much more wide-ranging.


PM Badsah Mukherji is still defining requirements for this feature, so please share your thoughts with him on LinkedIn to help steer it in the right direction.


Journey View

Journey view in a smart campaign would show an at-a-glance view of how the smart campaign will work.


Sky’s the Limit

One main takeaway for me is that it’s going to be increasingly difficult to ignore Marketo Sky.


I’ve personally not explored it in depth. It’s not that I don’t like all the new features; it’s a combination of sticking to what’s comfortable and an assortment of perceived / reported bugs or limitations.


However, Sky is clearly the future. It’s where all the new features are. I do want to use those features and help my clients do the same. So I personally plan to spend more time stress-testing Sky and identifying where it makes sense to use in production.


Platform and Performance Improvements

Trigger Campaigns

In 2018, Marketo launched accelerated trigger campaigns, which reportedly scaled processing speed by 5-10x. We also now have the “priority override” feature for smart campaigns (Sky only). This allows the user to define processing priority manually on a campaign-by-campaign basis.


Both of these features aim at improving overall system performance when it comes to triggered smart campaigns. However, there can still be issues in high-volume instances based on Marketo’s processing logic, which will continually privilege higher priority items that enter the queue over lower priority ones.


This logic makes sense on the surface, but in an environment where the queue is constantly full of high-priority items, those lower priority campaigns may experience unacceptable delays.


So this year, Marketo plans to release a new feature that also takes into account time-in-queue, ensuring that even low-priority items don’t “starve” in the queue.


Batch Campaigns

Marketo is planning functionality to process batch email programs in parallelized chunks. For example, instead of processing a list of email recipients in sequential order, Marketo will break the group into chunks that can be processed simultaneously. This will speed up email sending and may eliminate the need to use “head start” functionality for large / complex instances.


Smart List “Contains” Optimization

Use of “contains” in smart list filters is a well-known performance killer. This optimization allows faster performance in some cases where contains might be necessary to achieve a particular goal — for example, checking an email address against a list of domains.


Marketo will improve performance here by generating a table of domains that is pre-indexed. When you include the “@” symbol at the beginning of the domain in the smart list, it will enable this optimization and improve query performance.


CRM Integrations

Marketo plans to switch the Microsoft Dynamics integration to the REST API and introduce several new flow actions — Create Task and Change Owner. This is a welcome step, bringing the DCRM integration a bit closer to parity with the SFDC integration. There is still a wide gap, but given the strong partnership between Adobe and Microsoft, I expect it will continue to shrink.


Salesforce users will also receive some performance improvements through various optimizations.


Account-Based Experiences (ABX)

Marketo heavily promoted its capability to deliver “account-based experiences” or ABX. This isn’t a single feature but rather appears to refer to a series of new and existing features that together could support an end-to-end ABM capability at scale.


For example, an ABX journey could start in the Account Profiling tool (formerly AccountAI). This is a recently released feature that I haven’t used, but it seems like a useful way to leverage look-alike modelling to do your account planning inside Marketo Engage.


Marketo purports to examine your best customers and compare them against an external database of 256 million companies to find ideal target accounts, which are graded A-D.


Ad Integrations

Marketo’s AdBridge was launched in 2015 but, in my experience, has been of limited practical use. The feature has not changed substantially since first launched and still requires significant manual effort to add/remove people from ad platform audiences.


This year Marketo featured some new integration capabilities for advertisers, which I assume will become upgrades to or a replacement for AdBridge. Exact functionality was unclear to me, but featured improvements include new integrations with Adobe Ad Cloud, DemandBase, LiveRamp, and LinkedIn, all of which appear to allow more seamless access to those platforms from within the Marketo interface.


Marketo Sales Apps

Summit highlighted a number of potential improvements for Marketo Sales Engage (formerly ToutApp).


First off, the Sales Engage app, which allows sales users to place prospects into automated “cadences”, was rebranded as Marketo Sales Connect as described above. Furthermore, screenshots showed this tool within a dedicated region of Marketo called “Marketo Sales Apps,” suggesting it may be one of multiple sales-focused applications in the future.


Additional possible improvements include:


  • Triggered Sales Hand-Offs: Marketers can automate sales hand-offs using a smart campaign trigger to automatically put someone in a sales campaign.
  • Dashboard of all Prospects in Cadences: Sales users can monitor who is coming in and who is generated by Marketing.
  • Central Task List: Sales users have a central task list to manage all workflow items assigned to them.
  • Target Prospect List: Sales users have a target list of people (who may not be in a cadence) from which they can click on a person and see what they’ve done and an exact preview of what email content the person looked at. This feature seemed very useful — potentially a long-awaited Marketo Sales Insight replacement, if it could be embedded in CRM.

  • Recommended Templates: Sales users can access AI-recommended templates when composing a message.
  • Feedback on Marketing Assets: Sales users can send feedback to the marketing team about assets and what’s resonating with the audience — a great way to close the feedback loop between front-line BDRs and the content creation team.
  • Performance Data on Marketing Assets: Performance data from sales campaigns appears on a dashboard of a smart campaign showing MQL-to-positive response ratio for that piece of content as well as seller feedback. This idea seemed interesting to me, although it was unclear how the ratio would be calculated.


Design Studio

Marketo Sky is slated to feature a number of improvements to Design Studio.


  • Adobe Experience Manager integration with Marketo: Import digital assets directly from AEM to asset editors. This integration would be good news for users of AEM who don’t want to duplicate their digital assets across two spaces.

  • Adobe Image Editor: Perform light image editing (crop, resize, etc.) inside the asset editor interface.

  • New Design Studio Design in Marketo Sky: The home screen will provide quick links to recent items and key areas.
  • Journey Automation for Assets: Design studio could contain machine-learning driven recommendations to guide marketers on where to use an asset. For example, when you upload a white paper, the system would identify it as a white paper and make suggestions for which campaigns or audiences it should go to.

    This last feature seemed a bit unusual, as presumably a marketing team should have answered these questions long before the asset was completed. But I may not have captured all the details.

Drift Partnership

Marketo announced a new partnership with Drift (conversational marketing / chatbot tool) as part of its ABX initiative.


The announcement was brief, so it was unclear what new functionality this partnership would bring. However, based on the press release on Drift’s website, the integration appears to enable better personalization and segmentation of Drift experiences based on Marketo data. For example, Drift could be configured to share a relevant piece of content or fast track the lead directly to a named account representative.


More Resources

Adobe has already published all the sessions from Summit online. If you’d like to dive deeper into product roadmap, here are the original sessions:



These sessions are also the sources for most of the screenshots in this post.


Cross-posted from the Perkuto blog.

Sometimes I think asking, “Which attribution model do you prefer and why?” would be a great (marketing) conversation starter. From single-touch to complex regression-based analysis, some marketers are passionate about a particular method while others are still contemplating which is the best option. The topic sparks an interesting discussion.


Of course, all models are simplified approximations of an infinitely complex reality, and, no attribution model is perfect. Attribution models attempt to estimate the influence of your various marketing campaigns on human behavior that is unpredictable, irrational and fluid in nature. There’s no way of actually knowing that your white paper or webinar was responsible for 33% of the purchasing decision and therefore should receive a third of the credit. But, even with the flaws of attribution, applying the appropriate model, understanding the data it’s generating, and applying the directional insights will help you make better marketing decisions.


In this post, we’ll explore the different models and why you might use each one.


Getting Started with Attribution


Before settling in on a particular attribution model, assessing your needs and being realistic about what you want to accomplish will assist in your model decision.


  • What questions are you trying to answer? Contribution to revenue, pipeline value, understanding your sales cycle from the first touch to deal closed, which efforts are most impactful, why leads aren’t converting to sales—what do you need answered to sharpen your marketing plan and align reporting with your organization’s objectives?
  • Is your sales cycle simple or complex? Do you have a lot of marketing efforts or only a few?
  • What’s attainable for your organization? Do you have the appropriate tools in place? Are you just starting with attribution or are you more experienced?


Once you know what you want to achieve, then you can select a model that’s appropriate for you. (And I should note, unless otherwise stated below, all models discussed are as defined within Bizible’s platform.)


First-Touch Model


Stemming from the philosophy that a sale cannot happen if a customer doesn’t know you exist, a first-touch model applies 100% of attribution credit to the first tracked marketing interaction, which may occur before the person even enters your marketing database. The model itself is simple, and data analysis is less complicated. In a simple sales cycle with a quick or transactional sale, it’s very easy to see marketing effectiveness and contribution to revenue. The challenge with a first-touch model is data collection, because you need a way to capture and store the anonymous first touch and then associate it with the person when they eventually enter your lead database. You can solve for this challenge with custom tracking script and Bizible tracks this out of the box.


In more complex sales cycles, first-touch attribution acknowledges the brand awareness stage, highlighting which of your early marketing efforts were most successful at attracting new customers to your product or service. If you seek to gain insight into top-of-funnel activity, then a first-touch model can be useful in providing answers. If you want to know marketing influence in later stages of your sales cycle, a first-touch model falls short as it only tells part of the story by overvaluing early-stage efforts and ignoring subsequent campaigns.


Lead Creation Model


Going beyond brand awareness, a lead creation model attributes 100% credit at the point a customer is interested enough to provide contact information and essentially, becomes a “lead.” For example, if a customer visits your website three times and on the fourth occasion, completes a form for more information, the marketing effort that drove the fourth visit would receive 100% of revenue credit. The philosophy here is the campaign that converted a prospect to a lead is the most significant. Many organizations often start with a lead creation model because it provides an excellent introduction to attribution and the set-up is relatively straightforward.


Like first-touch, this single touch, simple model does not provide a good representation of longer and more complex sales cycles; for that, you need a multi-touch model.


Evenly-Split/Linear Model


A Linear or Evenly-Split model gives equal weight to every touchpoint with the rationale that every marketing effort is essential to moving a prospect through the sales pipeline. The challenge with this model is it oversimplifies the marketing process and fails to take into account the context of when the interaction occurred when giving credit.


For example, let’s say a person enters your database, consumes a few blog posts and then - a few months later - attends a VIP dinner and soon after is added to a new opportunity. With an evenly-split model, the casual content consumption that did not occur in proximity to any meaningful funnel event would get the same amount of credit as the high-touch dinner that likely made a much bigger impact on the sale. If you relied on this model exclusively, you might easily draw some inaccurate conclusions about relative channel importance.


Nevertheless, a Linear model can still provide some insight into which marketing programs are impactful. If you are tracking attribution using Marketo and Revenue Explorer, this is the only multi-touch option available.


U-Shaped Model


U-Shape is a simple multi-touch model that distributes credit between the early-stage touches to provide a more balanced view of which channels are generating new names in your database. In this multi-touch model, 50% of the weight is assigned to the  first touch and 50% to the lead creation touch. The philosophy behind it is to emphasize lead generation while also sharing credit between the various touches required to grow your database. For this reason, I prefer it over either a First-Touch or Lead Creation single-touch model for evaluating lead generation activities.


W-Shaped Model


W-Shaped Model


A W-shaped model is very similar to a U-shaped model except it acknowledges a third milestone, opportunity creation. Each primary stage of the sales cycle, first touch, lead creation and opportunity creation, is attributed with 30% of revenue and the remaining 10% is split between the other touchpoints. A W-shaped model is one of the most popular attribution models as it gives marketers a well-rounded view of the marketing campaigns leading up to the opportunity creation stage.


What’s missing in a W-Shaped model is insight into any activities that occur after the opportunity is created. For example, let’s say you organize a special event for customers and later stage prospects and then several opportunities close soon after. With a W-Shaped model, the significant investment in this event wouldn’t receive any credit.


Full-Path Model


Full Path Model


Similar to the W-shaped model, a full-path model also acknowledges major milestones in the sales cycle, now extending all the way through the revenue stage. Each significant stage receives 22.5% of the credit with the remaining 10% spread across touchpoints in between.  The Full-Path model is obviously more complete than the W-Shaped model and is arguably more sensibly-weighted than an evenly-split / linear model, as the touchpoints that occurred in nearest proximity to important funnel events get a much higher percentage of credit. This can produce reports that better reflect the “actual” impact of these important activities while still giving credit to everything.  For businesses with a complex sales cycle who want full visibility, a Full-Path model is a smart choice and remains easy and simple to implement.


Custom Model


A more advanced multi-touch option within Bizible is the Custom model.  With this model, you can define custom stages in the sales cycle in addition to those included in the Full-Path model—a common one to add is an “MQL” stage. You can then define your own percentage weightings for each stage based on your unique business model. Notice in this example, that the product demo stage is now receiving 10% credit, demonstrating the perceived significance of this event in the sales cycle.


This model offers more flexibility and requires some extra configuration. Its relative freedom also brings a certain level of risk, as the marketer might have inaccurate assumptions about the relative weightings that the different stages should receive and thereby create misleading distortions in the model.


Companies may want to run a Full-Path model first, then as knowledge of their unique sales process deepens, transition to a Custom model to achieve a more tailored approach.


Custom model


Machine-Learning Model


This model uses the same stages as the Custom model, but in this case, the machine makes recommendations for weighting credit between the various stages, representing the relative importance to winning a deal based on three criteria:


  • Predictiveness: the correlation between stage progression and whether the deal will close
  • Ease/Difficulty: high conversion rate implies less importance in the customer journey
  • Uniqueness: if a touchpoint is shared with multiple stages, the credit is shared, too


The algorithm is not random—Bizible based it on millions of touchpoints and buyer journeys. You can use the insights from the Machine-Learning model to refine and alter your Custom model, ultimately producing a machine-learning influenced model that incorporates human insights specific to your organization.


Tactic-Weighted Model


In a Tactic-Weighted model, credit is allocated based on the importance of the specific marketing tactics involved. For example, attending a webinar may get more credit than downloading an e-book, and attending a prospect VIP dinner may get even more.


This type of model—or one that blends it with a position-based model defined above—makes a lot of sense to many marketers, who intuitively know that spending four hours at a high-touch event naturally carries more weight than casually perusing web content.


This is an advanced model that is not available out of the box in any platform I’m aware of, but is something an analytically-mature organization could engineer within a BI tool.


That’s a Lot of Models!


One of the nice things about Marketo’s acquisition of Bizible, is marketers now have more model options to choose from, single-touch to multi-touch, simple to complex. To some, the options may seem overwhelming. My advice: take an inventory of your needs and start with what’s attainable. Remember, you can always transition to a new model as your knowledge and understanding grows. No model is perfect, but attribution will help you gain insight into your customer journey and the relative influence of your marketing efforts.


In my next post, I’ll address how to leverage your attribution data to fine-tune your marketing strategy.


Want a deeper dive?


I'll be presenting a webinar, Bizible Essentials for Marketo Users on July 10 at 1:00 ET . We’ll explore the differences between Revenue Explorer and Bizible, the solutions Bizible offers and the impact on your daily operations. Reserve your seat here.


And to go completely meta, here's a Bizible report showing the registrations by channel for the Bizible webinar so far. This offer-by-channel report is really easy to produce in Bizible, and I'll describe how at the webinar.



Attribution tools are to a marketer what a compass is to a hiker—both provide direction in your journey and guide your next steps. We’re familiar with current Marketo compasses but now Marketo has embedded a new GPS: Bizible. How does Bizible compare to the Marketo compass you’re currently using, and more importantly, how will it impact your daily operations?


Attribution Review


If you’re accustomed to tracking attribution using Marketo and Revenue Explorer, Bizible represents a significant change. In my last post, I covered the primary functions of attribution tools: capturing data, modeling data and reporting on that data. In this post, I’ll focus on each function, to illustrate the variances of the tools as well as provide common applications to emphasize the impact on your operations.


Data Capture


The scenario: You want to track what marketing initiatives are bringing people into your database (ie, “Lead Source”).


The process with Marketo: Most marketers can track an offer (ex: a content asset) in Marketo fairly easily using a combination of the form and/or landing page. However, tracking a channel is much harder, especially when it involves digital channels.

To obtain channel information in Marketo you’ll need to use UTM parameters on your landing page along with web referrer data to deduce organic channels. For example:


  • utm_medium = paid-social: channel = paid social
  • Web referrer contains “” and no UTM parameters: channel = organic search


Of course, you also need to ensure all links are tagged correctly.


Next, you need to get that data into Marketo. You might be asking, what about using hidden form fields to capture these parameters? Certainly possible, but what if people don’t fill out a form on the first page they visit?


To solve, you’ll have to implement your own tracking script to capture the data, convert it to cookies that persist as the visitor jumps from page to page, ensure all forms have hidden fields to capture UTM and referrer values and finally, pass these values to fields on the person object, which can then trigger adding the person to an appropriate tracking program. Moreover, scripts must be flawlessly written, to avoid failure in certain browsers or Marketo logic fails. Many marketing organizations use these methods very successfully, but there is, never-the-less, complexity and potential for error with this approach.


The process with Bizible: There is little configuration to start capturing data with Bizible. Since Bizible has its own tracking script, tracking is simplified, especially for digital activity.


What’s happening behind the scenes: when someone fills out a form, Bizible detects and logs the URL of the form completion page from which you would deduce the offer. Where Bizible excels is in tracking channels. Bizible automatically detects and stores the UTMs and web referrer data associated with the session, without having to set custom cookies or modify your forms. Finally, because Bizible has direct API connections with ad platforms like Adwords, Bing, and Facebook ads, it automatically pulls ad and cost detail from these platforms, without any manual tagging required on your part.


A Functional Comparison:





Munchkin tracking script captures web activity. Marketo also logs form fills and email interactions. Referrer/UTM data stored but only in activity log. Custom script required to make this data accessible at the field level.

Bizible tracking script tracks web activity and form fills as well as referrer and UTM data, which is associated with touchpoints for easy reporting. Tracks full clickstream data via Bizible Data Warehouse product, giving it the detail of a full-featured web analytics solution.


Easy to track; typically uses a combination of a form plus landing page to identify offer, which can trigger the addition of a person to the corresponding program.

Detects and logs the URL of the form completed; offer is deduced based on URL.


Harder to track, especially digital; requires custom script to convert UTM/referrer data to cookies and form management to ensure data is mapped to fields on the person record in Marketo.

Automatically detects and stores UTM parameters and web referrer data, without creating custom cookies or modifying forms. Direct API connections with ad platforms capture ad and cost data without manual tagging. Platforms currently supported: Adwords, Bing, Facebook Ads.


Data Modelling


The scenario: You’re launching a multi-faceted marketing campaign consisting of many offers (blog posts, webinars, ebooks, etc.) across numerous channels and your boss requires a report showing which channels are driving the most engagement with particular offers. In your preparations, you must also plan for the “human factor”— people who interact multiple times with a single channel.


The process with Marketo: Your set-up revolves around Marketo’s unit of attribution, the program, which represents a single marketing initiative you want to track. You can create as many programs as you like, and stages indicate the level of interaction with each program and if that interaction was successful. Programs correspond to a Salesforce campaign (if you track this in Salesforce). How do you capture the relationship between offers and channels?


Your options are less than ideal:


  1. Create separate programs for offers and channels: This allows you to capture each marketing asset and traffic source the person engages with, but there is no connection between them. Also, if someone engages with the same channel (e.g., organic Twitter) twice, you can’t track the second interaction, because a person can only be a member of a program once.

  2. Create a program for every offer + channel combo. Ex: ABC Ebook + social; ABC Ebook + paid search…and the list goes on. Prepare yourself for a mountain of work and huge task list to maintain.

    programs set up to track offer + channel data

  3. Copy UTM values to the campaign member, which also moves reporting to Salesforce (as you cannot store this additional metadata related to a specific program status in Marketo). This option gives you a more flexible model but requires extra configuration and custom code in Salesforce. Additionally, there are some challenges to tracking costs for ROI and reporting by channels with this method, as you no longer have a distinct campaign to represent the channel.

campaign member example


The process with Bizible: Bizible uses “touchpoints” (or the marketing interaction between a person, offer, and channel) as the unit of attribution and captures every web visit, form fill, and offline touchpoint, grouping them into channels or subchannels that you create. Theoretically, unlimited touchpoints are possible.


The process is straightforward as Bizible creates touchpoints automatically with little to set-up or maintain. Also nice, all interaction data, both offer and channel, are stored on the same record. As a result, there is greater flexibility in reporting options, including changing channel groupings midstream and reprocessing of all data without information loss.


The downside? You’ll have less rich metadata about offers, because the form URL isn’t as precise as the metadata associated with Marketo programs. For this reason, I recommend that you maintain offer programs in tandem in Marketo, which you would likely need anyway to send fulfillment emails, etc.


Bizible Touchpoint Example


A Functional Comparison:





Unit of attribution is the program, a single marketing initiative which corresponds conceptually to a Salesforce Campaign.

Unit of attribution is the Touchpoint, a single marketing interaction between a person, an offer, and channel, stored in a single record.


One person can be a member of many programs, but only once per program or campaign. Stages indicate the level of interaction with the program and whether that interaction was successful. Program structure can be as granular as you want, but this becomes hard to manage when tracking offer + channel combinations.

Touchpoints are unlimited; touchpoints are captured for every web visit and logged in CRM for a person’s first anonymous visit, every subsequent form fill, and for offline touchpoints when synced from a campaign, even when repeat interactions with the same offer and channel occur. Channels are assigned to touchpoints dynamically based on business rules, eliminating the need to maintain a set of pre-existing campaigns for tracking purposes.


A program can have various types of metadata to add additional dimensions, ex: region, product line, type of marketing initiative, etc. but program tags are limited to a fixed set of values, limiting their usefulness.

Touchpoint data is set based on UTM parameters, providing more flexibility to describe channels according to your prefered taxonomy. Channel and subchannels group touchpoints according to your configuration, which can be altered and data reprocessed. Metadata about offers is limited, as only the form URL is captured.


Data Reporting and Visualization


The scenario: You’ve completed your marketing campaign, and now it’s time for the fun part— reporting on the results. Your CMO is excited to see how each effort performed and eager to know which was the most profitable...


See how Data Visualization and Reporting compare on Perkuto’s website.

If there’s one area marketers get hung up on more often than not, it’s reporting and attribution.


Technology is an important part of the solution. But even with the right tools at hand, attribution efforts can fail for many reasons - poor/inconsistent implementation, lack of process, dirty data, and the list goes on.


Marketo recently acquired  Bizible, a leading marketing analytics and performance management software (see here for a recap of that), which fills a major gap in Marketo’s functionality. Bizible delivers robust and easy-to-use marketing attribution capabilities and I suspect will ultimately replace Marketo's RCE product (which is showing its age).


In a subsequent post, I’ll cover the unique features of Bizible, seen from a Marketo-centric perspective; but to lay some groundwork, let’s first define what exactly “attribution” is and how it works, because this term is often (mis)used in a confusing variety of ways.


Understanding Marketing Attribution


Simply stated, marketing attribution is the process of determining which of your marketing efforts is driving the outcomes you want, like revenue. As a byproduct, attribution tools also enable you to optimize marketing campaigns, resource allocations, and your marketing budget. How do they work?


Technology aside, all attribution systems essentially examine the intersection of three related datasets:

  1. Marketing Efforts: all the fantastic marketing campaigns you launched
  2. Audience Engagement: the people (prospects, customers, etc.) who engaged with your efforts
  3. Performance Outcomes: the results of your efforts. Usually, this is revenue or pipeline value but could also be a metric like MQLs.

Marketing Efforts, Performance Outcomes, and Audience Engagement

When people engage with your marketing efforts then take the desired action, such as buying your product, we attribute some of that credit back to the marketing effort with which they interacted. The attribution methodology can be simple or tremendously complex, but all are based on this underlying theme.


Understanding Attribution Tools


In order for attribution tools to tie the three datasets together in a meaningful way, three primary functions must take place:

  1. Capture data
  2. Model data
  3. Visualize and report on data

To expand further:


Data Capture

Behind the scenes, your marketing attribution tool is tracking your efforts, engagement and outcomes. Marketing efforts and outcomes are fairly easy to keep an eye on since they are commonly recorded in your marketing automation and CRM systems (e.g., programs and opportunities). Engagement tracking, however, is another story and is where many marketing departments have gaps. The biggest challenge? Marketers need to worry about tracking both channels and offers. To clarify our terms:


Channels are the marketing tactics that drive engagement: paid search campaigns, SEO, paid and organic social, trade show booths—you get the idea.

Offers are what people engage with: ebooks, white papers, videos, web forms, webinars—and the list goes on.


(These definitions are indebted to Josh Hill, who provides further insights into channels and offers in this post.)


Most companies do fairly well with tracking offers but stumble with tracking channels, and that’s not surprising. Tracking channels is significantly more difficult as it typically requires tagging your digital activities with UTM parameters, translating those parameters from website visits into cookies, and then incorporating that data into your marketing automation and CRM systems.


Even with Marketo, this process requires a fair amount of setup and skill and typically requires a skilled web developer. And unfortunately, many teams who try to track channels usually fall short—either the required configuration is not done effectively or it’s not done at all.


Data Modelling

OK—data captured...check. Now your attribution tool has to store your data in a way that allows for meaningful reporting. For marketing engagement, the simplest way to accomplish this is by adding fields to the person object in order to capture the data you want to report on—lead source is a prime example. The challenge, of course, is this is a “flat” data model, making it very difficult to capture and report on multiple interactions (and multiple dimensions of those interactions) with any sort of flexibility—the data model is simply too limited.


Let’s step our model up a notch—you could use another object to represent each of your marketing efforts and then connect people to that object when they engage with your marketing.


This is what the Marketo program (or Salesforce campaign) represents: a person is connected to the marketing initiative via a junction object. (ex: campaign member status or Marketo program status). This extension of the data model opens the door to true multi-touch attribution reporting, because you can now reflect multiple people, engaging with multiple marketing efforts, resulting in multiple outcomes. 


And while this model is quite flexible, it does have its limitations—for example, a person can only be added to a program once, but what if someone engages with the same channel multiple times? We'll address some of these limitations (and how to circumvent them) in another post.



Visualizing and Reporting on Data

An example data flow for capturing, modelling, and reporting on data in Marketo. This program structure is relatively simple but suffers from some of the limitations described above. 


Visualizing and Reporting on Data


Now we get to the fun part: the final step in the attribution process is to visualize and report on your results. This involves calculating credit for your performance outcomes (ex: opportunity revenue) and assigning it to your marketing efforts based on the marketing engagement of the people involved in that opportunity.


There are many different methodologies for calculating attribution, often called “models.” A few of the more common models include “first touch” (all credit to the very first marketing engagement),” last touch” (all credit to the most recent marketing engagement), and “even split” (credit divided equally amongst all touches.) We’ll delve deeper into these and other types of models plus why you might choose one over another in a future post.


Cross-posted from the Perkuto blog.

It’s an annual tradition at Marketing Nation Summit for Marketo to announce new features and product updates.


Similarly, Perkuto's post-Summit practice is to provide a summary of the discussions as well as commentary on the potential impact for your marketing operations. It’s our way of keeping you informed while keeping it real. So grab a cup of coffee — this post is a little longer than most but worth the time to read in its entirety.


Marketo Product Updates on Deck for 2018 - An Overview

This year, Marketo defined five main product priorities plus announced key enhancements in each area. The 30,000-foot overview:

  • UX: Garnishing audience “ooh’s” and “ah’s,” Marketo’s unveiled its new next-generation user interface, “Marketo Sky.” Entering into open beta this month, Sky is visually appealing and contains some highly-requested productivity enhancements.
  • Analytics: In a calculated (pun intended) and much-needed move, Marketo shored up its attribution and analytics capabilities with the acquisition of Bizible, a leading Marketing Performance Management solution.
  • AI: “AI” is the buzzword in marketing circles right now. In a timely move, Marketo announced new machine-learning capabilities for identifying your ideal customer and finding look-alike audiences (AudienceAI).
  • Marketing/Sales Alignment: In 2017, Marketo announced the ToutApp acquisition. In 2018, ToutApp is being rebranded as “Marketo Sales Engage” with tighter integration to the Marketo platform.
  • Platform: Marketo continues the ongoing quest for scalability, with additional changes to improve campaign throughput and speed plus a plan for improved LaunchPoint integrations.

Diving Deep: UX

Marketo’s next-generation user interface has been a long time coming; customers should expect an open beta version available in May 2018. More than just a cosmetic re-skinning, the new interface is a complete overhaul of Marketo’s front-end, using modern web technologies and providing a stronger foundation for continued development. Seasoned Marketo admins will especially relish the productivity improvements, including:

Saved Rules

Saved rules are collections of triggers/filters or flow steps that can be preserved and made accessible to your users. For example, you might have a specific set of smart list filters you use to email all customers of “product A,” including customer status, product entitlement, record viability, plus advanced logic to tie them together — you get the idea.

As we all know, (and likely have experienced at some point), these filters are easy to mess up. With the new interface, the admin can create a rule called “Customers of Product A” and drag that rule onto the canvas causing your previously saved filters/triggers to materialize. Currently, the saved rules feature operates globally, although there was a discussion of private vs. public rules possibly in the future. And in case you’re wondering, the same logic can also be applied to flow steps. Overall, the saved rules feature will save marketing operations staff time and error-proof the process. Well done, Marketo.



marketo saved rules

A saved rule and a list of filters it contains.


Mass Approvals and Activations

Are you regularly cloning your program templates? If so, you’ll be happy with this one. (If you aren’t, you should!) The mass approval and activation feature simplifies and streamlines the process of deploying programs by displaying a list of assets along with an easy way to approve or activate them in bulk. This is another substantial improvement and time-saving enhancement, not to mention relief from tedious and unnecessary clicks.


Approve multiple smart campaigns with a single action.


A pop-up notifications menu tells you when your mass action is complete.


Asset Expiration

Never manually update stale or outdated pages again! The new Sky will provide a way to give assets an expiration date, or in the case of smart campaigns, a deactivation date. Rest assured that visitors cannot access outdated pages (ex: a registration page for a past event) and lighten the load on your system by not keeping triggers active longer than necessary. Best of all, you’ll never need to backtrack work as landing pages can have a default or page-specific fall-back to show after the expiry date.



Easily set an expiration date for multiple assets.


My Token Updates

My Tokens are the key to scalable and efficient operations in Marketo. Sky introduces some token-related improvements, many of them targeted at power users with heavily-tokenized programs, including:

  • Token foldering: The ability to organize your tokens with sub-folders.

  • Token searchability: New keyword search capabilities within programs and folders.

  • No renaming: System prevents you from silently breaking your token references by restricting token renaming. This is useful but also annoying when you make a spelling mistake.
  • Token cloning: The alternative to renaming — cloning the token and renaming it.

  • See where tokens are used: A new context menu showing the usage and location of tokens, although the icon to access it is somewhat counter-intuitive at present as it looks like a "refresh" button. Currently this feature only supports tokens located in email header fields and rich text regions, limiting its usefulness, but hopefully this will become more comprehensive over time.

  • New token types: A simplified process to defining links and images as variables. Say goodbye to the previous process of inserting within text/rich text tokens; Sky’s new token types create an experience similar to the link/image dialogues inside of assets as well as ensure link tracking works correctly.

    Example of an image token dialogue, which allows you to pull directly from Design Studio files.

Global Search

Improving system organization, Sky is planned to include a global search box with an index spanning the entire platform, making every asset within reach from a single spot. The index not only captures the name of the asset but also the asset labels. Global search is a good improvement although there are limitations of the search function, include searching the content of the assets themselves. Perhaps we’ll see this addition down the road.





Labels are a new kind of metadata, which can apply to both programs and assets. Functioning similarly to “tags” on a blog post, labels enable freeform and unstructured metadata to sort, organize, and search your assets. There won’t be any validation around labels, meaning it will be up to Marketo admins to define and create clear taxonomies and conventions appropriate for their users and requirements, but overall, the addition of labels combined with global search is a powerful enhancement.




Labels offer a lot of flexibility, but make sure you develop a label strategy to make them consistent and therefore useful.

Other UX Improvements

Marketo’s list of updates doesn’t stop here. Of the other updates mentioned, some are currently in the beta while others are on the horizon:

  • My Marketo Homepage: Marketo will finally have a solid “homepage” experience when you log in, displaying familiar navigational tiles as well as dashboard-style widgets highlighting key metrics or system data. Marketo has wisely made this customizable (as no two companies would want the same thing) but which options are available is still TBD.

  • Filter the Tree: Reminiscent of a beefed-up Campaign Inspector except more readily accessible, users can filter the tree by date range, asset type, label (etc.) to limit the scope of what’s in focus when working in marketing activities.

  • Contextual help: Contextual information and guidance via help prompts throughout the interface.

  • Revamped Program Detail Page: More information, all in one place: in addition to mass approvals in bulk, the program summary page offers a revamped program schedule providing a full calendar experience and easier access to related information.
  • Revamped Smart Campaign Detail Page: Resolving previous UI inconsistencies and enhancing ease of use, admins can access a grid of campaign members and perform single flow actions just as you would in other person grids.

  • Revamped Asset Detail Page: Expect more accessible “used by” menus and buttons for primary actions. Additionally, the draft and approved version of an asset are now combined in a single node (as opposed to the draft being a child of the approved asset) with separate sets of action buttons to manipulate each version.

  • Revamped Landing Page Detail Page: The URL is now clickable and can be copied with a single click for easier access. PURL enablement is also more conveniently exposed on the detail page.

  • New Iconography: Introducing the lightning bolt for triggered campaigns!

  • Folder Level Permissions: Another long-awaited feature, it appears Marketo Sky will now make this possible.
  • Cookie Opt-In: Although not part of the current beta, Marketo intends to offer native cookie opt-in functionality in response to GDPR. There are, however, no immediate plans to support other requested GDPR features, such as person anonymization.
  • More Agile Release Cycle: Marketo Sky enhancements (and bug fixes) will no longer be tied to quarterly release cycles but will be shipped independently, possibly every few weeks.


Other Miscellaneous Good Stuff

Not strictly related to UX, Marketo announced a few other improvements in the UX session, which are mainly self-explanatory.


  • Add a CC Contact to Email Sends
  • Continuous Audience Sync for AdBridge: The existing AdBridge integration is pretty lame, requiring manual intervention. With continuous sync, it becomes much more useful.
  • Secure Tracking Links in Emails
  • Native Form Recaptcha Integration

The Need for More Powerful Analytics within Marketo

As I’m sure you’ve heard by now, Marketo purchased Bizible, the most significant acquisition in its history. Both a strategic and bold move, the addition of Bizible transforms what was previously a weak area of the Marketo platform into an area of strength.

When I first became a Marketo customer, I expected (perhaps naively) that Marketo would have a basic level of digital analytics built in, similar to Google Analytics. Unfortunately, this capability doesn’t exist with out-of-the-box in Marketo, a sore spot for many new customers.

While it’s possible to build, there’s a lot of heavy lifting required — custom JavaScript, form modifications, and many programs to track activities at the level of granularity you want. And adding to user pain, Marketo’s standard reports are just that — basic and not very customizable, limiting the ability to report on the data you capture.

Marketo does offer other advanced reporting solutions, but neither provide the full-featured marketing analytics that many companies need.

Advanced Reporting (a.k.a RCE to old-timers) offers some very useful pivot-table style analysis, but this area of the product has been unimproved for years, has limited customizability (for example, you are confined to a single multi-touch attribution model), and is very slow on large data sets.

Then there’s the new Marketing Performance Insights (MPI) tool, which seems more like a role-based dashboard for easy reporting on a selective group of KPIs - very useful, but not a full-featured marketing analytics solution.


The Significance of the Bizible Acquisition

Marketo can now justifiably claim a leadership position in marketing performance management. Marketo customers who adopt Bizible gain immediate access to better data and better reporting — and with greater ease. Bizible offers the following advantages:

  • Channel Tracking: Bizible is the only attribution tool I’m aware of that captures attribution data on the front end. It tracks digital channels out-of-the-box using a Google Analytics-style taxonomy that is familiar to almost all digital marketers.
  • It “Just Works”: Based on the half-dozen Bizible implementations I’ve participated in, the time-to-value is remarkably short. You can start collecting attribution data within a day by installing a managed package and placing a simple script on your web properties. A basic install requires no complex coding, no updates to forms, and there's no need for hundreds of attribution programs or campaigns.
  • Flexibility and Customizability: Bizible easily accommodates more complex requirements via multiple attribution models, ranging from simple to completely custom. And, you can even view all your Bizible data in a warehouse and integrate it within a broader business intelligence infrastructure.

Screenshot of Bizible's new "Discover" interface, also announced at Summit.



Bizible’s Impact on Marketo Customers

Will the acquisition of Bizible be an analytics game-changer for Marketo or another potentially-useful product add-on that is used by a relatively small percentage of the customer base? The answer, I believe, will primarily depend on cost.


At present, Bizible pricing is reportedly remaining about the same, which means there’s little immediate value for Marketo customers. Organizations interested in using Bizible will continue to do so with the only difference being where you send your payment.


However, if Marketo introduces a pricing model that makes acceptance a no-brainer — perhaps generous license discounts for Bizible adopters in exchange for longer overall Marketo contract lengths — then we might see a vast base of Marketo + Bizible users emerge. There's also the possibility for tighter integration between the two platforms, of course.


AI Enhancements

Artificial Intelligence (AI) is the trend of the year, and Marketo is proactively responding. Marketo’s AI upgrades extend beyond a single product and include embedding machine-learning capabilities into an increasing number of areas across the platform. Current and planned features help users to find perfect customers (ICP Marketing), expand campaign audiences (AudienceAI) and enhance content relevance for customers based on prior content consumption patterns and topical interest. (ContentAI). Let’s take a deeper look at each one.


ICP Marketing


What it is: ICP (Ideal Customer Profile) helps you understand the audience you should be targeting by creating a profile of your “perfect” customer. Under the hood, it will be powered by Mintigo. With ICP, you’ll be able to feed a set of customers to analyze (either a smart list or static list) into a predictive model, which will then surface a set of customer attributes. After creating the model, you can fine-tune it, suppressing attributes that you believe are irrelevant or noise by assigning them a lower weighting. Finally, once your profile is defined, the model can assign your accounts a letter grade from A-D depending on how closely they match your ICP which also improves targeting and ABM prioritization.


What does it mean for customers? This type of look-alike profiling is already commonly available from predictive vendors, including Mintigo. It’s unclear if ICP introduces any capabilities that aren’t currently available from predictive vendors, and if a separate Mintigo subscription will be required. However, it’s possible that ICP represents a move to tighter native integration — perhaps one that doesn’t consume REST calls — not to mention other benefits that aren’t available with a third-party deployment. At this stage, we don’t have enough information to be sure.




What it is: While ICP Marketing focuses on defining the ideal customer for your business, AudienceAI goes a level deeper to help you discover look-alike audiences at the program level. To begin, identify your audience for a campaign. AudienceAI will then suggest similar records within the database to consider, charting attribute similarities between the original and expanded audience. After you run the campaign, reports will provide a performance break-out between the original and extended audience, allowing you isolate and analyze the results as well as qualify the net lift received.

What does it mean for customers?
Like most things AI, I suspect this feature is garbage in/garbage out, meaning that if your campaign is poorly targeted in the first place, the AudienceAI suggestions will be limited or even flawed. I also wonder if you can place overriding constraints on the expanded audience. For example, you probably don’t want to invite North American prospects to your event in the Netherlands, no matter how similar they are in other respects. That being said, if you are following engagement marketing best practices and sending carefully targeted messages to smaller segments, this could be a beneficial way to expand the reach of your campaigns without sacrificing relevance.





What it is
: ContentAI is a rebranded amalgam of predictive web content (an outgrowth of real-time personalization) and predictive email content. The feature will crawl and index your web content and then display it in defined widget areas on your website or in emails based on user interest and content consumption patterns.


What does it mean for customers? ContentAI is not a new Marketo feature, but it did receive renewed emphasis at Summit. Expect it to be promoted widely in the coming year.


***Updated*** AI Lists

What it is
: AI lists will automatically analyze the membership of a static list and provide insights on its members. Clicking on the "Clusters" button under list actions will initiate the analysis.


cluster button.jpg


Marketo will give access to a number of characteristics that you can evaluate when looking for similarities and the ability to exclude some that might not be relevant.


cluster analysis.jpg


After the analysis is complete, Marketo will display a list of "clusters," which are populations in the list membership that Marketo has grouped together based on common characteristics.



You can then go into each cluster and see details about what makes them similar.


cluster detail.jpg


What does it mean for customers? AI lists is an interesting addition, providing access to the intelligence of machine learning directly within the Marketo UI. This feature could enable marketers to better understand their audiences, surface interesting segments they hadn't considered before, build better targeted campaigns, and improve personalization.


It would be nice to see a broader range of attributes available for the cluster analysis, as I can imagine there will be important business-specific traits that companies would want to include.


Fully Adaptive Campaigns

The ultimate vision for AI in marketing automation encompasses fully adaptive campaigns and the ability to pre-select a person’s next touch automatically across multiple channels. We’ve heard this concept before at both Summit 2016 and 2017. Are we any closer to fruition? Perhaps, although I expect it will be several years before it becomes a reality, given the complexity of the task.


Sales and Marketing Alignment

As mentioned earlier, ToutApp is being rebranding as “Marketo Sales Engage” (MSE) with tighter integration to the Marketo platform. What can you expect?


Live Feed

MSE features a “live feed” of recent updates relevant to sales. Essentially, a salesperson can dock this feed at the side of their screen and see real-time updates of new prioritized leads, lead actions across web and email, and other sales and marketing touches. Think of it as a revamped “Best Bets” view from Marketo Sales Insight (MSI) except in a better container.



MSE also includes the ability for sales to put marketing leads into automated nurtures (called “Playbooks”) as well as assistance in writing semi-personalized, templated emails.


What does it mean for customers?

One of the challenges I’ve perceived with the ToutApp acquisition is that there hasn’t been a compelling reason for users of competing solutions in the category to make a switch.


However, the introduction of a consolidated live feed that includes both sales and marketing data in one place is intriguing. Data disintegration is frustrating to sales; they need a simple, clear place to get all their insights. Pitching MSE as a master sales enablement tool may be a key selling point, especially if Bizible touchpoint data is integrated. Looking ahead, MSE could also be the natural successor to the aging MSI product, which has been stagnant for years. Perhaps existing MSI seats could be swapped for MSE licenses? Just an idea, Marketo.


Platform Scale and Performance Improvements

For many years, Marketo’s platform has been on an extended journey to increase the scale and performance of its underlying platform, dating back to the Orion Project in 2016. In Q1 2018, we saw the launch of “Campaign V2,” intended to improve the speed of trigger execution. The rollout is underway now, continuing throughout the year.


Immediately following on the heels of Campaign V2, “Campaign V3” is also in the works, designed to improve the speed of batch campaign execution. In practical terms, this means that (for example) if you send an email to 1 million people, that audience will be chunked up and processed in parallel, enabling your emails to deliver significantly faster.


Campaign Performance Troubleshooting

Marketo is also shifting campaign control into the hands of the user with the goal of improving campaign performance. Features announced:


  • Campaign priority control: Allows the user to determine the priority of campaigns without the use of hacks to game the system. The engine will also automatically boost priorities of flow steps that are lingering in the queue.
  • Visualize campaign relationships: Complex campaign interrelationships (especially when using the Request Campaign flow step) can be challenging to visualize. Marketo plans to introduce a visual tool to see these relationships better, which one PM compared to a “transit map.”
  • Identify Campaign Hotspots: This sounds like an auditing tool that will allow you to zoom in on “expensive” flow steps and identify problems that are slowing down the system. Common culprits are flow steps with many conditional choices; with the campaign hotspots tool, you’ll know for sure.

CRM Integration

We all know, a smooth CRM integration is essential and have experienced the pains when it is not. These CRM-sync improvements may seem obscure, but they will make life significantly easier for companies with complex environments or who are migrating to a new CRM.


  • Disconnect and reconnect CRM: Traditionally, CRM integration via a native connector has been permanent. Integrating with a different CRM using a native connector required migrating to a new Marketo instance, a painful and costly process. With this new tool, you’ll be able to disconnect your instance and reconnect to your new CRM safely.
  • Custom timestamp for sync: Currently, the Salesforce (SFDC) integration looks at the standard SystemModStamp field to determine whether a record has changed and if syncing is necessary. The system has its flaws as some SFDC organizations are connected to external systems that generate a lot of noisy updates. Marketo’s new feature would allow admins to specify a custom ModStamp field controlled by custom business logic, to ensure the sync only inspects records that have a meaningful change.
  • Sandbox refresh support: If you have a Marketo sandbox integrated with a Salesforce sandbox, you know refreshing the SFDC sandbox can cause problems. The good news: Marketo is actively addressing this use case to provide better support in the future.

LaunchPoint Integrations

LaunchPoint is growing up! Expect the integration ecosystem to mature in some very significant ways, bringing Marketo closer to a Salesforce AppExchange-style environment — all welcome changes for LaunchPoint integrators and their customers. What will the updates address?


  • One-Click Installs: LaunchPoint partners will be able to configure a package of configuration changes in a partner sandbox environment and package them together. LaunchPoint customers will then be able to easily install this managed package into their instance, immediately creating all required fields, objects, and other configuration changes. The net result? This enhancement will greatly reduce the time-to-value for LaunchPoint partner and customer integrations.
  • Partner Flow Actions: Partner Flow Actions (PFAs) provide an easy way for integrators to create new first-class actions into your arsenal of flow steps. They are essentially nicely branded webhooks you can insert into a LaunchPoint package to remove the complexity and expose the configurability in the familiar flow canvas of a smart campaign. For example, instead of configuring a webhook to send a physical gift item to a prospect, you can now use the “Send PFL Item” flow — a much more user-friendly step for most users.

  • Partner Triggers/Filters: This feature functions the same as a PFA but for triggers and filters.


The Marketo product team has a lot to juggle. Scaling the platform for enterprise use, paying off technical debt, keeping on top of the latest trends, aligning the roadmap with revenue goals, and keeping a (sometimes demanding) group of power users happy — it’s not easy to balance those priorities.


For long-time customers, there’s much to celebrate in this roadmap. Marketo Sky, once it has its kinks ironed out, will deliver greater productivity and also hopefully increase the velocity for future improvements. Platform enhancements are a rising tide that lifts all boats. And a more mature LaunchPoint ecosystem is vital because it will continue to crowdsource innovation to the many smart integration partners building on the platform. All that’s good stuff.


On the other hand, many of the other new features just announced may seem tantalizingly out-of-reach for customers who can’t afford big increases in contract value to get shiny new toys. Long-time users regularly voice concerns about the percentage of new features that appear as paid add-ons as opposed to improvements to the core product.


Marketo needs to pursue a balanced strategy, and it’s a delicate negotiation between delighting passionate customers who use Marketo daily and want it to improve continually vs. introducing new product lines that support Marketo’s ongoing growth.

What’s your reaction to this year’s roadmap? Please chime in with your thoughts!


(Cross-posted from the Perkuto blog. For a retrospective of previous Summit product discussions, see our posts from 2015, 2016, and 2017.)

As all of us working in Marketing Ops know, it's a challenge keeping up with the pace of change in our profession: new tech pops up every day, processes evolve, and the latest "best practices" are born (and sometimes die out just as fast). Stack & Flow is a podcast to help MOPS and MarTech pros meet that challenge.


Stack & Flow is a bit like a Cole's Notes for our discipline, looking at all the pieces that make up the sales/marketing stack, examining how they fit together, and covering the news, trends, and emerging practices shaping our world -- and all in a format that is easy to digest during your commute. (It's sure easier then trying to memorize Scott Brinker's MarTech landscape diagram . )


I like that hosts Sean and John are both practitioners themselves and have been selecting guests who work every day in the trenches and have lots of hands on expertise, including many folks in our own Marketo community. I recommend subscribing and checking out the back catalogue, especially these recent episodes with Marketo Champions Jessica Cross and Jeff Canada:


Jessica Cross - Aligning the Stack with the Customer Lifecycle

Jeff Canada - Getting Personal All the Way from Top of Funnel to Advocacy


I had the pleasure of appearing in the latest episode, which is available here, and with Sean's permission I am sharing the transcript below. Topics covered include:

  • The state of B2B advertising
  • Tech stack dysfunction and the need for unified governance
  • Is MOPS from Mars and SOPS from Venus, or will these functions converge?
  • How marketers are learning to stop worrying and love the API
  • Building out custom apps that sit on top of your core stack for extended functionality

Read the Transcript

John J. Wall: Hello and welcome to Stack & Flow. I’m John Wall.

Sean Zinsmeister: I’m Sean Zinsmeister.

John: Today our guest is Justin Norris. He’s a solutions architect at Perkuto. Justin thanks for joining us.

Justin Norris: Thanks guys. Good to be here.

John: All right. In the news today, Sean, you had a couple of articles and a few things talking about B2B advertising trends. What are you watching over there?

Sean: I’ve gotten a chance to chat with a bunch of different people just from the community about what they’re seeing from B2B advertising as well as a few analysts as well. It’s tough to drive correlations or draw correlations rather between the rise in ad blocking or if people are just not engaging with it. A lot of people are starting to see very sharp diminishing returns, especially from an acquisition standpoint on just regular B2B display ads.

Now that being said, what is interesting is now more people moving over to take advantage of the custom audience tools. This is some of the stuff, John, that Chris Penn has talked about with you over on the Marketing Over Coffee show where you have Facebook, Twitter and LinkedIn and even Google now that will actually let you exports from a different system. Then upload a custom list of email addresses or accounts, kind of match them and use that as your sort of targeting system.

I think a lot of people are starting to use that as they dive into these ABM strategies and really want to be able to hone in on how they’re exercising their ad budget.

Justin, I’m curious, because you obviously get to touch a lot of different types of clients on things like that. Anything that you’re seeing that’s interesting on the B2B ad side? Does that seem in line with what you’re seeing, or just curious if there’s anything new there?

Justin: Well there’s two thoughts that I have about that. We mostly help our clients in terms of their marketing technology stack and their operations which are not typically managing ad spend or anything like that.

The ways in which what you’re saying makes sense to me, I think there’s two challenges that companies continue to face. One of them is a lot of companies don’t yet have a basic ability to demonstrate ROI on ad spend. I think there’s huge gaps in terms of the ability to track and store data about and model the influence of these different channels. I think that could be an obstacle to continuing to invest in them.

I think that the promised land of convergence between martech and adtech isn’t fully there yet. It’s something that we’re hearing about for some time.

To your point, Sean, about being able to export custom audiences and move them over, there’s still a lot of manual steps. Even Marketo’s integration between some ad tools in your marketing automation platform, it’s still very manual. I think the level of automation and the ability to combine insights and execution around ad spend haven’t fully crystalized for a lot of companies, and that’s something else that I could see influencing that.

Sean: I think that there’s a lot of good points there, Justin, and I think that the other thing that I’m seeing is that … Especially as we enter into a new year cycle and budgets start to be scrutinized a little bit more. I think that being able to justify to your CMO or whoever is pulling the purse strings, as it were, around your marketing spend, being able to justify an overall ‘halo effect’ I think is the feel good marketing starts to be a really tough sell to make a business case for.

Versus I think that there’s a lot of people that don’t need any more convincing that retargeting belongs as an evergreen piece in your marketing mix. I think that it’s going to be interesting to see where the budgets continue to play out for sure.

John: How about as far as rolling it across to B2C too? Do you guys see the same kind of thing, diminishing returns in generic advertising? Or is it more just that people are getting more advanced? They’re doing better retargeting? They’re doing better whitelisting of their ad spend and they just don’t have the same problems? It’s not just the spray and pray people actually doing a better job.

Justin: From my perspective, we don’t have a ton of B2C clients. I think the vast majority are in the B2B space. I don’t have a ton of comment on that.

Something that might be interesting to look at, and stop me if we’ll tackle this a little bit, but something that might be interesting to look at more to the contrary of the idea of decreasing B2B ad revenues is the whole thing of ABM and outbound being cool again. The notion of targeted display, account targeted display being like an air cover type of process for an ABM strategy.

Maybe this does get at something that you were driving at, John, it could just be that people are being a bit more targeted, a bit more smart in how they’re deploying ads rather than being so broad based because they’re more focused that is creating a more efficient spend.

Sean: I think you’re spot on with that too. I also think, John, that there’s a big difference between the performance marketer that’s looking at, say, retargeting as a tried and true tool to say, “I run an ecommerce solution, okay, or a marketplace model where certain tactics likea cart abandonment retargeting and looking at following people all the way down through a, you know, a non-touch sales process makes a lot more sense than some sort of a multi-touch ABM process, which is a little bit harder to build those types of attribution models.”

I think that there’s an evolving mindset there as well where you can’t really be looking at the same metrics as you would be, especially from a B2C side or from a performance marketer that you would need to be from an ABM B2B mindset.

Justin, zooming out a little bit, tell us a little bit more about Perkuto and the work that you’re doing for those folks over there, for those who don’t know?

Justin: Sure. Perkuto is a marketing operations consultancy. We help clients build and manage their sales and marketing tech stacks. Also manage their marketing operations in terms of building out their capabilities, campaign operations, manage services, kind of all that fun stuff.

My role as a solutions architect is really to help clients who are looking to design new functionality or new capabilities, whether that’s bringing on new technologies or rolling out new capabilities within the components of the stack that they already have.It is a very tailor made role for me. I started out in-house doing sales or marketing operations at a tech startup, and I was a marketing jack of all trades. Really was drawn towards being able to build systems and string together different technologies to do cool things. That was the thing that I was always gravitating towards even though I came from a marketing background, and not necessarily a tech background.

This is kind of a tailor made role for me because that is what I do. People bring their toughest challenges, their requirements that seem very difficult to fulfill, and we look at how we solve for that using technology. It’s a ton of fun and it is a great space to be in right now.

Sean: No, absolutely. Justin, I’m curious, when you start to look at some of the clients that you get to work with, do you think that some of the success of Perkuto can be attributed to you guys are filling a gap and a need for the expertise that they’re not able to hire internally? Or is it more that they have some best practices in place, but they just want to be able to polish and move things to the next level? Curious if there’s any sort of commonalities that you see between clients about like what leads them to work with a group like Perkuto.

Justin: I think there’s a mix, but I think it is definitely biased towards the first scenario where we are technology rich and we are expertise poor as an industry or across B2B in general. They say that marketing operations is about people process and platforms or people process and tools. I think tools has gotten the lion’s share of the attention and love, and it’s certainly more bright and shiny and interesting.

What you then have is you have all this technology, this huge overhead occurring yearly subscription spend and customers that have implemented it poorly or have implemented it insufficiently. Don’t know how to get all the value out of it that they were promised during sales cycles or that they believe can be achieved.That’s a big part of the business because particularly our agency focuses exclusively on Marketo. Marketo has experienced a ton of growth, and we also work with Salesforce where Marketo is kind of the common thing that unites all of our customers.

There’s not enough people. Every client we work with has also … Very often, most clients we work with are very often trying to find somebody in-house as well to manage their system on the inside to work with us, and they’re very difficult to find. There’s one breed of client that’s like that, and then there’s another breed where people have internal talent. They are mature. They are looking for help to either take something to the next level, so reevaluate it, move themselves to the next phase of the marketing automation maturity roadmap, or to do some interesting special project.

Like they have a particular use case, whether it’s … We could talk about this a little bit more perhaps further on, but building some custom application to extend the capabilities. Stitching together different tools in interesting ways or integrating data from products from external systems and doing something more sophisticated. I personally really enjoy working on those projects. Those are kind of the two flavors that I tend to see.

Sean: What are the main buckets where you see a lot of the stitching going together? For us in recent episodes having talked to people, we know that the sales operations stuff has really increased. There’s a lot of sales tools that are coming into the mix and a lot of integration points there. Is that on the top of the list or are there other stitching together you see that’s a lot more common?

Justin: Yeah, that’s a big part of it. This is a really interesting subject and at the root of it … You guys probably remember maybe a few years ago, it was still a subject of contention. I remember like reading posts on David Raab’s blog about will the future be like where you buy your clout and you have … Like you buy Adobe’s suite of tools or you buy Salesforce’s suite of tools? Or will it be a future of best of breed where you buy the tool that you think is the best for your requirements in a specific category, and you plug them in together?

I think I would love to hear if either of you would dispute this, but I think that best of breed feels to me has indisputably won the day in terms of the format wars of how people will build their martech stacks.Interoperability is a crucial component of that. A tool that only works in isolation; it doesn’t plug into the rest of your stack. It probably feels rather inconceivable to us right now. It’s sort of become table stakes.

Where this runs into problems, speaking to your point, John, is we have an interoperable stack, but we don’t have in many cases unified governance of that stack. There is an issue with sales ops, marketing ops buying their own tools that have overlapping, but not entirely the same functionality. The probably classic example of this is like marketing is messaging people at mile a minute out of their MAP platform.

Meanwhile sales has got their new or their SalesLoft subscription or their Yesware subscription, and they are messaging people that way. Sales is becoming their own mini communication automation coordinators. There’s a real potential for conflict there.

We don’t … Haven’t really done as many projects about that. I think that is just an emerging area of dysfunction that needs to be addressed within a lot of enterprises. A lot of the stitching together that we’ve done is more along the data collection point of view where companies have different tools that have different outbound or customer touching capabilities. Say video marketing, content hubs, tactile marketing or postcards, letters, physical goods.

They want to be able to stitch them together and automate that process and coordinate it from one central platform, which typically is Marketo and collect the data on the results back into one central platform so that they can report on it. This is an interesting challenge in some cases, but it’s getting easier and easier to do when you have an approach and a model for how it all fits together. It’s not a future that’s very far away for companies, but it is something that they, we find, tend to need help planning a strategy for how all those pieces need to fit together.

Sean: Well, Justin, one of the things we were kicking around in the pre-show before is this idea of revenue operations, which, on the Infer side and people who are looking at predictive analytics and those types of solutions, and especially looking at data reporting, forecasting, things like that. This seems to be maybe one uniting front that I’m starting to see pop up in more organizations.

I’m curious, is revenue operations the great uniter of marketing ops and sales operations? Or does it feel more like something that’s more of a sales ops with a different name type of thing? I’m curious, what are you seeing on the revenue operations side?

Justin: It’s a concept that is gaining in its relevance and currency. I think the whole ABM craze has a lot to do with this because if we move to a world where we have common strategy for generating revenue that isn’t marketing-led and then hand off to sales, but it’s marketing and sales working together. Then you need to manage this in a more unified way.

I think it’s something that people are talking about more, and I’m sure in a few very forward looking companies, this is more of a reality. The actual market out there I don’t think has nearly caught up to that, and we see everything from real division, real dysfunction where you can’t get a new field in the CRM implemented very easily. Something that would seem to be as simple as that, but it’s a real problem because, “Oh no, we’re marketing ops, sales ops controls to CRM. We can’t go there.”

To a point where probably the next step along that maturity is more like a council model where they’re still functionally independent, but we have cross functional meetings and people getting together. Actively trying to align their operations to a place where some companies have like a federated model where it actually is the same entity. We do it that way internally here at Perkuto. We’re still relatively small, so it’s easier to do that in a smaller organization. I’ve yet to see a really big organization that’s doing it really well, but it could just be that I haven’t heard about it.

I think it is a natural place for it to go, and at the same time, I think you could say contra to that, and I’m curious what you guys have seen or think about it that there are still some very natural dividing lines like territory management or compensation operation. Stuff that sales ops just has to handle it that marketing ops doesn’t fully do. Does that still need to be split out or can it still just be managed within a unified function?

Sean: Yeah, I think that part of that has been companies who really want to establish the CRO role in terms of like what does a chief revenue officer actually own? I do think that that role to me feels more like a sales centric role. I would also argue that some of the other dividing lines that I’m starting to see as well are lines of demarcation that are being drawn on the demand gen side where demand gen is now looking more like a sales development side.

I’ve definitely seen this happen more, especially with ABM and outbound becoming more popular trends for some businesses, especially in the B2B realm. Brute force sales development tactics are just the way that they’re going to break through, and the way that they’ll have the most calculable and also predictive return, if you’re looking at it from a budgetary standpoint.

I do think that there are some things that, like you said, I think territory planning is a great example that will tend to more fall on the line of sales rather than this hybrid role. I do think there’s another interesting trend to see the CRO positions on the rise and some of these even bigger companies are starting to see it more and more. It’ll be interesting to see whether that looks like more of a CMO type of role where they’re taking on some more of those responsibilities or more of a sales role. I sort of see it as more of a chief sales role, if you will. Yeah, interesting to see where that heads in the new year in particular.

John: Justin, how about as far as tool stacks that you guys work with? Obviously, Marketo is a commonality for you across all your clients, and I imagine Salesforce is probably present the majority of times. What about other tools that are in the stack? What are the trends as far as what are the other hot things to patch in there where people have seen success?

Justin: I would say the biggest additional piece of technology that we are called upon to bring into play is something to do with attribution and reporting. We have a lot of customers that are using Marketo’s own advanced analytics module. Sometimes that’s still contained within Marketo, but in a lot of other cases, people want to do reporting out of Salesforce more in a third party tool. The native capabilities of CRM are just insufficient.

We work a lot with Bizible. They are a partner of ours. Another tool that I’m a big fan of is called Path to Scale, which is a lifecycle modeling and attribution tool that lives inside Salesforce. That would be the number one because it’s still, as I mentioned before, one of the biggest gaps for a lot of companies and something that they need. We typically generally bring in an external solution for that.

Predictive is a part of it as well. I know that we have at least one common customer using Infer and a few other customers using other platforms. Predictive is a piece. I think people are predictive curious. I still feel like a surprisingly small percentage of companies are at the level of maturity where they’re ready to invest in a tool like that, which surprises me because I would have thought we would have been a bit further along in that direction right now in terms of penetration into the market.

Then ABM is becoming another big one. Marketo launched their big ABM module in the summer, and we’ve had a lot of people talking about that. They’ve priced it to be very enterprise focused, but also tools like lean data. Engagio has really emerged. We have a few common customers there, and that is one of the tools that I’m also very excited about in terms of where I see ABM heading in a more mature direction.

Sean: What do you think, looking down, even if you had to look at a couple years … It was interesting. I was running to some Google Trends reports, and I wanted to compare the hype of inbound marketing and sort of what that looked like against ABM. It actually pales in comparison about the delta between the two about how hot inbound marketing still is. ABM certainly has sort of taken off in its own right.

I think if I had to offer an opinion, I think that ABM really finds its place into your marketing mix. By that I mean as a diversified strategy from both a technological standpoint, but it’s also a sales and marketing … a go to marketing strategy as well where you don’t … I don’t like this idea of companies throwing out a leads-based model because in many regards marketing’s job is to supply leads to fill those accounts. You also can use marketing to have that upmarket strategy as well.

I’m curious, do you see that portfolio approach coming out of the normalization as the hype dies down from some of the things? Because I don’t know that anybody is really saying anything new about ABM at the moment. I think that they are looking for frameworks to help drive these strategies, which I think is why we’ve seen the rise of some of these technologies. I’m curious about what you think the output is going to look.

Justin: I’m glad you asked that. I’ve been a bit ABM skeptical from the beginning. In essence, part of that I think is I have a bit of a contrarian streak. When I saw it taking off in such a … I’m going to call it a faddish way, my internal skepticism gets kicked off a little bit. I don’t think that’s fully warranted. I think that’s unquestionable that there’s something happening, but I think we’re also seeing part of like a pendulum of inbound marketing is everything and don’t interrupt your customers. Let them come to you when they’re searching. There’s some truth in that.

Then the other swing of the pendulum is don’t just collect all of these random points of inbound interest that may not even be relevant to you. Go and decide who you want to sell to and then go and find them, meet them where they are. Fish with spears, not with nets. There’s truths in both, and anybody that is like … Not marketing; that’s 2015, and it’s all ABM now. It’s like you can’t take such a black and white mindset I think.

I think, like anything, the tenets of what are valuable in each strategy will stick around, and the dross will fall away. I think what I would sort of predict going into next year is that 2016 was the year of ABM really gaining a lot of currency in the mainstream consciousness. People feeling like, “Wow, this is really … You know, not necessarily implementing yet, but feeling like kind of ABM guilt.” Like I should be doing ABM if it’s something that’s relevant to you as a company and looking into tools. Maybe buying some tools, but not necessarily having a coherent strategy around it or a real understanding of what it means.

The most forward thinking conceptualization of what ABM could actually be, practically speaking, what is actually real about this beyond just I’m going to target accounts rather than generate leads is something that was articulated to me at least by Glen Lipka who is over at Engagio who built a product at Marketo initially and is now helping Jon Miller to build Engagio.

There are kind of foundational metaphors, this idea of a play, which is like a football play where you’ve got a football team. You have 11 people on one team, 11 people on the other team, and then you develop a play of how you’re going to approach a situation. Similarly, the thinking is like, “All right, we’ve got a range of roles on … on the marketing or sales side on the company that’s trying to go out and get business.”

There’s a range of roles that need to be involved in an ABM strategy. There’s also a range of roles over on the customer side, the prospect side that we need to talk to. How do we orchestrate all those processes, not in a completely automated way, but in a sort of automation assisted way?

Their PlayMaker tool I feel is one of the most forward thinking ABM tools that’s there today, and represents a real path forward in terms of operationalizing ABM and something real. You could do something similar without Engagio with that concept and the way that they’ve developed to assist companies in doing it. I think is where ABM has to go if it’s not just going to be something that dies off and people are like, “Ah, there’s nothing … nothing to this.” That’s how I see it actually becoming operational.

John: How about as far as best practices then? Are there any things that you see that most of your clients are doing wrong when you show up and things that you have to get them on track so that they can just function better in the future?

Justin: I think basic data and tracking is still a real challenge for a lot of companies when it comes back to reporting and things like that. The ability to capture clear and consistent data across multiple touch points for all names that are entering your database. There’s a lack of consistency in taxonomies. I’m sure this is something you guys probably see out in your necks of the woods as well, and the impact that this makes and just prioritizing technology over process.

Having tools in place and feeling like the tools are supposed to be solving our problems, but not having internally a process for, “You know, this is how we deploy a campaign. This is the … the three or four data points that need to be present, you know, in all of our links. These are hidden fields that need to be in our forms. These are scripts that need to be running here and there.”

These processes don’t exist in a lot of companies. I think people hire us sometimes to help with tools, but a lot of what we tend to end up talking about is process. Because it’s just an area where it hasn’t matured as rapidly as people have been able to buy technology.

Sean: In terms of the flow part of the sort of Stack & Flow idea, I’m curious, another thing that we were chatting about was again this interest in rise of APIs and connections and building these stacks. Are you seeing more client interest in taking advantage of APIs, or at least demanding that there is an API option available from the technologies that they select? Is this becoming more of a must have versus a nice to have?

Justin: Yeah, unquestionably, API connectivity has become something that we would consider table stakes with a new tool. I think that there’s developed a greater comfort level for marketers who are not necessarily technical marketers, but who want to extend their reach to feel okay about dealing with an API, and like that isn’t such a scary thing anymore.

The most simple expression of this that we see in our practice is a web hook. Marketo has the capability to call web books. Basically just posting a request to an external service and having that external service do something else or get data back. One of the companies that I think has done a very good job capitalizing on this is a company like Clearbit, which, to my mind, sort of developed as an API first tool where you present a very lightweight service that is accessible by an API. There’s a big license to buy. There isn’t a big implementation to do. You just send some data over here and then you get some data back. The more you use it, it will scale and the costs will increase in a variable way.

We’re seeing lots of tools like this that work in that way. Even some friends of mine in the Marketo community recently launched a new plug-in that lets you through Webhook basically pass any arbitrary JavaScript over to their service. It will perform calculations, whatever you need, whatever JavaScript can do. Even calling other external services from within that virtual environment and then passing the results back to you.

Basically, it’s like the functionalization, if you’re going to take like a computer programming mindset. Functionalization of all these capabilities where you right now have tools in your toolkit that you can just, “All right, I’m just going to call over here. Get this tool to perform this function. Bring back some data and then I’m going to act on that.” If you think about it, tools like predictive work in a similar way. We pass our data over to them. We get back predictive insights and then we drive it through our system of execution, whether that’s passing somebody over to sales, nurturing them in a different way, sending them different communications, all of that kind of stuff. That’s number one.

Then number two is something that I’ve just personally been seeing an explosion of very recently, and this could just be an anomaly or a blip. It’s just customers like more comfortable building these custom interfaces on top of the tools that they’re using. Customers that are not content to say, “All right, this is a limitation of Marketo and so that’s just it.” Or, “I need to buy another tool,” but say, “All right, let’s … Let’s invest in building not a completely custom application, but a … Let’s build an interface that lets us through the API, tap in and, you know, do more dynamic and … and targeted and customized email marketing that Marketo could do on its own.” Or, “Let’s build a tool that lets our customers talk to our other customers or lets our user group leaders …” If you’re a company that has a user group program, lets them create their own user group programs inside our Marketo instance, but without having to give them access.

Then we see tools like one of my favorite Salesforce applications is called Skuid. I don’t know if either of you guys have heard about it, but it basically lets you create custom interfaces within Salesforce in a completely drag and drop way. No code, no official force. It’s really amazing. They just launched a new feature that basically lets you use their interface building capabilities on top of any enterprise data source. You can basically bring your different data sources together, build your own interface on top of it, create your own application to do whatever you want with very little technical knowledge and skill required.

I think that’s the future, making that more accessible to people and letting them create their own applications to do what they want to do that they don’t want to wait for a native vendor to build that one feature. They go build it themselves.

Sean: How about for the upcoming year? Are there any tools or technologies that you’re watching to come around over the next 12 months? Things even for your own stack or stuff that you are excited to roll out to clients?

Justin: I think it’s a lot of the things that we’ve covered already. I think ABM just will continue to pick up with a lot of the companies that are inclined in that direction. People that have been looking will adopt technologies. People that have adopted will be seeking to implement, re-implement them or become more mature in them. I think this will also bridge the gap between what’s currently considered like sales automation. Does this become subsumed in the ABM category or are those tools hop on board and coexist alongside marketing automation and alongside onsite ABM?

Multichannel, we’re seeing a bit more interest in SMS. It’s not always fully relevant to all B2B customers, but I think more people are getting into mobile and looking at B2B applications of mobile and what does that mean. Data remains huge. Companies have data issues, particularly with ABM that lead to account patching, surprisingly, is a huge thing in like lead routing. We’ve put together stacks would need like three to four different tools just to achieve a lead routing outcome that that customer wanted.

Tools that can help kind of deduplicate, normalize data. We work with companies like RingLead and ReachForce and LeanData, and then that can match together records and help form the concept of an account more cleanly within systems. Those are also tools that I think are going to be really important.

John: Justin, if someone wants to follow up with you or find out more about Perkuto, what’s the best way to get in touch?

Justin: Well they can go to our website, That’s P-E-R-K-U-T-O dot com, and I’m always happy to get an email at justin at perkuto d0t com.

John: Sean, how about if folks want to learn more about Infer and what else have you got going on?

Sean: The best way to find me is just Google Sean Zinsmeister. You can find all the good stuff that I’ve been writing about. I think Q4 for me right now has been all about going back to the writing board and getting my thoughts out there in terms of what’s coming and things like that. You can always find my latest things there, and of course If you always want to find me on Twitter @SZinsmeister or LinkedIn is a great way to get a hold of me.

John: All right, that’s great. You can find out more from me over at We’ve got a couple of episodes on artificial intelligence that have been pretty hot, and we’ll be doing our year-end wrap up. That’ll do it for us for now. Thanks for listening and we’ll see you in the stacks.

If you spend long enough building in Marketo, you will very likely encounter scenarios where things don’t happen they way you’d expect.


You may have two different smart campaigns - which are in themselves perfectly functional - produce a bad result because they didn’t execute in the right order.


I would say 90% of the issues I’ve had in Marketo have been some variant on this simple theme of order of operations.


Order of operations is often the difference between a stable, predictable, and effective Marketo instance and a disorderly chaotic mess. If you aren’t constantly thinking about and controlling the order that things happen, your Marketo systems will eventually break, no matter how much cool stuff you build.


Do you have a race condition?

(You may want to ask your doctor...)


A race condition is when a successful outcome of a process (e..g, a smart campaign) is dependent upon some other process being completed first, but those processes occur asynchronously and in an uncontrolled way.


When the processes don’t occur in the right order, then the dependant process fails.


Symptoms of a race condition include:


  • Leads being misrouted in your CRM because they were synced before key data values were written to the lead record.
  • Alerts being sent to no one because they fired before a sales owner was assigned.
  • Lead Lifecycle Stages getting overwritten because a lead qualified for multiple lifecycle campaigns at the same time.
  • People in your office talk about how “Marketo is broken”.
  • Your life as a Marketo Admin becomes like:


01 More Dupes.jpg

(This actually happened to me.)


Fortunately, race conditions are both preventable and treatable. Here’s how.

Tactics for Controlling Order of Operations

The list of tactics below isn’t definitive, but it covers the most obvious ways to control order of operations and the ones I’ve found to be most useful.


All these methods need to be used within a well-designed system in order to work well, but that’s a subject for another post.


1) Keep Your Actions in a Single Smart Campaign

The simplest way to control the order of a series of flow steps is to keep them all in a single smart campaign.



  • Simple and effective.
  • Order of operations for flow steps in a single smart campaign is basically guaranteed. A lead moves from one flow step to another in sequence.



  • This approach is suited only for very simple scenarios, with a few actions that always go together in a linear sequence. Once you introduce multiple conditions and more advanced logic, this approach breaks down.
  • Long lists of flow steps that do lots of different things are hard to understand.


When to Use

If you have a few simple activities you are trying to chain together in a straightforward linear flow, this method works great.


Example: Form Auto-Responder

When a lead fills out a form, you want to add the lead to a Salesforce campaign, send the lead a thank-you email, and then send an alert to distribution list. All these steps happen in quick sequence and have no other dependencies outside the campaign. So based on these requirements, a single smart campaign works very well.


Eg 1 - Single Campaign - Smart List.jpg


Eg 1 - Single Campaign - Flow.jpg


2) Wait Step

A wait step allows you to pause your lead in a flow for a designated period of time before doing something else. While your flow is waiting, it gives a chance for other stuff to happen elsewhere in your system.


Wait steps are the most commonly used (and misused) method for controlling order of operations that I’ve seen.


Unfortunately, wait steps are still basically just guesses. A wait step waits however long you tell it to, and has no awareness of what you might be waiting for. Systems are unpredictable - depending on conditions, your wait step might be too long or too short.



  • Wait steps are simple and easy to understand.
  • Wait steps are precise when all you want is to pause a process for a specific period of time, irrespective of what else might be happening in the system.



  • If your other process is not specifically time-bound, there is no guarantee that the wait step will be long enough. It’s better than nothing, but definitely not bulletproof.
  • If a wait step proves too short, people tend to make them longer and longer to provide more security. Unfortunately, this means that your process gets slowed down to the lowest common denominator and you lose velocity the rest of the time.
  • Campaigns with wait steps are deprioritized in the campaign queue.


When to Use

Wait steps are generally not a robust method for coordinating asynchronous processes with no clearly defined time interval.


This doesn’t mean never use them in these scenarios (sometimes the complexity of doing otherwise is impractical). However I would make them my last resort.


To my mind, there is really just one scenario where a wait step is always a suitable option: when you want your process to wait for a defined period of time (no matter what else is happening).


Example: Free Trial Emails

A lead initiates a free trial of a product. Your campaign sends a welcome email, then waits 30 days while the lead tries out the product. At the end of the 30 day period, the flow checks if the lead has upgraded their trial, and if not, sends an upgrade offer.


In this case, a wait step works just fine because we know the trial is exactly 30 days and we are checking their current state after 30 days before sending the next message.


Eg 2 - Wait Step - Smart List.jpg


Eg 2 - Wait Step - Flow.jpg


3) Request Campaign

The Request Campaign flow step (and its companion, the Campaign Is Requested trigger) are oddly polarizing in our community. I find this feature inspires either passionate enthusiasm or antagonism among Marketo users, depending on who you ask.


I’m a fan of Request Campaign. I find it the most direct and logical way of having one campaign trigger another. It provides enormous flexibility in how you structure your campaigns and makes it very easy to route people through operational programs multiple times.


On the flip side, you have many people state that this trigger can slow down your Marketo instance.


Now, I haven’t seen any hard and fast evidence or official statement that the Campaign Is Requested trigger requires more processing time than any other trigger on a one-for-one basis. To be honest, I suspect that part of the issue is that Request Campaign can lead to more “trigger-happy” design patterns, causing an overall increase in the volume of trigger campaigns. And that definitely can decrease overall performance, regardless of the trigger you use.


However, even assuming it is a bit more resource intensive, it doesn’t mean we can’t use this trigger; in some scenarios, it is still the most efficient tool for the job considering all the factors involved.


The main thing is to use it judiciously, which is a rule to apply to all trigger campaigns in general.



  • Request campaign is a very logical and bulletproof way to ensure one campaign triggers another.
  • Flexible and extensible for a wide variety of situations.
  • Quick and easy to deploy for ad-hoc use.
  • Allows for centralization of repeatedly used functions, which is easier to maintain.
  • No limit on how often a campaign can be requested for any one lead.



  • Potential performance impact at higher scale.
  • Some people find it harder to work with and troubleshoot.


When to Use

I use Request Campaign in scenarios where I need one campaign to immediately trigger another and other methods are too clunky. I think it’s especially suitable where you have a central operational campaign that you need to repeatedly trigger from a wide variety of places -- i.e. a high ratio of flow steps requesting a single trigger.


Example: Centralized MQL Processing

You have a series of multiple flow actions that need to happen when someone fills out a fast track form, all of which terminate in a sync to Salesforce. You need to ensure that the Salesforce sync happens only once all the previous flow actions are complete.


Furthermore, your demand gen team is super form-happy, and you have over 100 unique form placements to deal with.


You could recreate those flow steps 100 times in each form program followed by 100 “Sync to SFDC” flow steps, but this is brutal to maintain if anything changes and makes it impossible to coordinate your sync with other processes.


Instead, create a single MQL processing campaign in your lifecycle program that includes a sync flow step. Now you can now request it from anywhere you want, maintain one version of the process, and control when and how someone enters that flow.


This uses only one Campaign Is Requested trigger and ensures that your process works even if a lead fills out the same form multiple times.


Eg 3 - Request Campaign - Smart List.jpg


Eg 3 - Request Campaign - Flow.jpg


4) Static Lists

Static lists can be used in almost the exact same way as Request Campaign. Instead of a Request Campaign flow step, use an Add to List flow step. Instead of a Campaign Is Requested Trigger, use the Added to List trigger.


There are some benefits to doing it this way. Static lists can be deployed quickly and in an ad-hoc way and disposed of just as easily. Lists also provide some additional insight that can be used in your operational reporting, since the list keeps a running count of people who have passed through the process.


The main drawback is you can only be added to a static list once. If you use the fast-track form example above, what happens the second or third time someone fills out the same fast track form?


To make sure your process doesn’t break, you would need to ensure you have additional automation to remove the lead from the static list when the process is complete. This is extra overhead and it creates the possibility for error (plus sets you up for more potential race conditions).



  • Allows one campaign to trigger another another.
  • Flexible for a wide variety of situations and very extensible (no functional limit to the number of static lists you can create).
  • Quick and easy to deploy for ad-hoc use.
  • Allows for centralization of repeatedly used functions, which is easier to maintain.



  • Managing 5 bazillion static lists.
  • For repeated processes, additional automation is required to remove leads from the static list after a process is complete. This creates the potential for failure if a lead isn’t removed correctly or not removed quickly enough.


When to Use

Static lists are a very sensible choice in most scenarios you might encounter, as long as there is no concern around being able to successfully remove the lead from the list in time.


I find static lists especially valuable when you need to identify a subset of leads that are waiting for a particular process to end, usually triggered by a data value change. The static list membership becomes a filter on a campaign triggered by that data value change, allowing you to control when those leads move through the flow.


Example: Send Alert After Owner Is Assigned

When someone fills out a fast-track form, you want to send an alert to the owner if the record has an owner in your CRM (we’ll use Salesforce for this example). However, at the moment the lead fills out your form, they may OR may not already have an owner assigned.


If they do have an owner assigned, your campaign should send the alert to that person. If the owner is not yet assigned, you need to wait until the owner is assigned (an outcome controlled by another process in another system) and then send the alert.


Campaign #1

The first campaign checks if the lead has an owner; if it does, the alert is sent, and if not the person is added to a static list to identify them as someone waiting for an owner.


Eg 4a - Static List - Smart List.jpg


Eg 4a - Static List - Flow.jpg


Campaign #2

The second campaign listens for lead owners changing BUT uses the static list membership as a filter. The list allows you to precisely identify the leads you want to take action on once their owner changes.


Eg 4b - Static List - Smart List.jpg


Eg 4b - Static List - Flow.jpg


5) Program Statuses

Program statuses are another very flexible method for controlling order of operations. The flow step is Change Program Status and the corresponding trigger is Program Status Is Changed. You can also use the “Member of Program” filter with specified program statuses.


Program statuses can be especially elegant when you are trying to coordinate a multi-stage operational process, since the various stages of your process can map against the statuses of a program channel. (My colleague Kristen did a Summit session all about this method, with some useful examples.)


For operational channels, make sure to exclude them from reporting and keep all your program statuses at the same number. That way, when a process is complete, you can easily move someone back to the first status if you want them to move through the process multiple times.



  • Allows one campaign to trigger another another.
  • Flexible for a wide variety of situations.
  • Allows for centralization of repeatedly used functions, which is easier to maintain.
  • Can be used repeatedly for the same lead if set up correctly.
  • Provides a quick “at-a-glance” view of how many leads sit where in your process
  • Program status changes are logged permanently in the activity log and do not get archived.



  • Less quick and easy to deploy - requires admin access to create new channels.
  • Potentially less extensible than other options.
  • Difficult to modify program statuses once existing programs are using them - plan your processes carefully.


When to Use

Program statuses would be my preferred option for operational programs where you have a clear multi-stage process that forms the foundation of your system. It is also an excellent choice for marketing activities that occur consistently in the same way.


For one-off or ad-hoc usage, a static list is a better choice as you don’t need to commit to creating a new channel in the admin.


Example: Lead Scoring Threshold

You have a single operational program set up to score your leads. Your scoring program has a defined score threshold. Leads reaching the threshold should be transitioned to MQL.


Additionally, your program needs to allow leads to be recycled, at which point there is a holding period where they are not eligible to reach MQL status again. After the holding period, their behavior score is reset to zero and they can reach MQL again.


Program Channel


Eg 5 - Program Statuses - Channel.jpg



The scoring flows would go in their appropriate folders, while the two campaigns below live in a “Management” folder.


Eg 5 - Program Statuses - Program.jpg


Campaign #1

When the lead reaches your scoring threshold, change their program status to “Reached Threshold”. This should serve to activate a trigger in a central lead lifecycle processing campaign (like the MQL flow above), which will handle everything associated with routing the lead to MQL.


Additionally the other program statuses serve as a filter to ensure that leads who have already reached a threshold or are in purgatory after being recycled do not get re-processed.


Eg 5a - Program Statuses - Smart List.jpg


Eg 5a - Program Statuses - Flow.jpg


Campaign #2

The second campaign triggers once the lifecycle stage changes to recycled. The lead is moved into a waiting program status for the appropriate period (30 days in this example). After the wait, the scores are reset and the program status changes again.


Eg 5b - Program Statuses - Smart List.jpg


Eg 5b - Program Statuses - Flow.jpg


In this case, the program statuses serve to trigger a process in another program and control the flow of leads within this program. Another benefit of this structure is that you can see at a glance from the program summary view how many leads you have in each stage.


Eg 5c - Program Statuses - Program Summary.jpg


6) Data Value Changes

If you are orchestrating processes that involve updates to lead data, data value changes are a very natural way to connect them.


There are many possible use cases here; the send-alert-after-owner-change example above is also a good example of a process triggered by a data value change, since the Salesforce process of assigning an owner triggers the alert in Marketo when the owner value updates.


There are a few restrictions here - most obviously, a field must exist in your schema in order to trigger off a change to it. For ad hoc use where a field isn’t already involved, it would be better to use another method, such as adding someone to a static list.



  • Allows one campaign to trigger another another.
  • Allows you to leverage existing data updates to control order of operations without any additional build or campaign overhead.



  • Requires a data field to exist in your schema.
  • For repeated processes, additional automation is required to reset a data value after a process is complete. This creates the potential for failure if the field isn’t updated correctly or quickly enough.
  • Data value changes are archived after 90 days, making this more difficult to audit over time.


When to Use

If a process is already updating a data value, and this update indicates that other processes can start, then using this as a trigger is a smart idea. Lifecycle stage updates, score changes, a lead source being set, and so on.


Example: Data Enrichment for Lead Routing

You and your SFDC admin want to team up to create some automated lead assignment rules. To route leads properly, you need some key data values to be populated - and you don’t want to kill your form conversion by adding a bunch of new fields.


Enter a new data enrichment vendor, who provides you with a webhook that you call in Marketo when the lead is created to enrich them with data like company size, headquarters location, and industry.


Your SFDC Admin sets up assignment rules so that when the leads sync to Salesforce they will get sent automatically to the right reps based on the enriched data. Huzzah!


However, at first you run into some problems. Both your data enrichment webhook and your lifecycle flow for new leads trigger off of “lead is created.” You need to find a way to make sure that the leads are not sent to Salesforce until the data enrichment webhook has finished pushing in data. This can happen almost instantly or take 1-2 minutes depending on how the enrichment vendor’s API is behaving.


The answer is to chain these actions together using a data value change.


Campaign #1

Your data enrichment process actually becomes the first step in your lifecycle processing, because it needs to be complete before you take any other action. So when the lead is created, you call your webhook.


Eg 6a - Data Value - Smart List.jpg


Eg 6a - Data Value - Flow.jpg


Campaign #2

In the webhook response containing the enriched data, your vendor will also pass back a status code. You can map this value into a field and use it to trigger the next step of your process.


Eg 6b - Data Value - Smart List.jpg


Eg 6b - Data Value - Flow.jpg


Of course this is an oversimplified example and in real life there are other things to consider. For example, if your enrichment webhook fails for any reason, you need a backup to ensure the lead is still processed. But you can see how this method is a lot more bulletproof than a wait step. You are essentially guaranteeing that your sync is not going to happen until the enrichment is complete.


Wrapping Up

Obviously there are about 5 different ways of executing any of the examples I mentioned above. Those ways aren’t all created equal, but there’s room for reasonable people to prefer different methods for various reasons. That level of flexibility and freedom is part of what makes Marketo great.


I’m really interested in what other people are doing in the trenches day to day, so please share your agreements, your disagreements, and your favorite methods I didn’t mention below.

Marketo announces a plethora of new features at Summit. The riches are so bountiful, its hard to keep track of them all. Fortunately my overly-detailed note-taking from high school still comes in handy on occasion.


Here is this year's deep dive into the product roadmap, along with some commentary to help you navigate the changes ahead.


There were some very significant announcements this year, including a complete rearchitecting of Marketo’s underlying technologies, a foray into the world of ABM, and the first step of a next-gen analytics experience. Let’s dive in.


Note: these features were announced as part of Marketo's roadmap for the coming year and many do not yet have a public release date. Unless they specifically say "Spring '16" next to them, the features are not expected as part of the Spring '16 release.


Project Orion: Re-Architecting Marketo for Enterprise Scale


Project Orion is the most exciting innovation this year. It’s not a new feature but rather a complete overhaul of the underlying architecture that makes Marketo work.


First, consider that Marketo is a huge workflow engine. It ingests behavioural data and lead information, logs that information as activities, and then executes actions in response to those activities based on rules. It’s beautiful and powerful.


Here’s the catch: at enterprise scale, Marketo can slow down. Anyone who’s gone to get lunch while waiting for a report to run or seen campaigns queued up for hours will know this pain.


And so we have tactics to help larger instances stay lean. We remove Munchkin from very high-volume pages and shift triggered campaigns to scheduled batches running at 3 AM. But do things have to be this way? Good design hygiene is a virtue (see some very useful tips in Josh Hill's recent post), but forced austerity due to platform limitations hurts us all. That’s like driving your Ferrari in the slow lane.


Project Orion tackles this issue head on, bringing greater capacity, increased scalability, and faster throughput to all of the platform’s key functions (data capture, activity logging, execution).


Without getting too deep in the weeds, Marketo is rebuilding its platform to leverage next-generation technologies like Kafka and Apache HBase that are used by some of the world’s most data-intensive applications. This is a multi-year initiative, but all clients will be migrated to the first iteration of the Orion platform sometime this year.



Credit: Ajay Awatramani


What does this mean in practical terms? Director of Product Management Ajay Awatramani flashed some statistics in his sessions. Things like 20 million activities per hour per instance and all triggers firing within 5 seconds. And that’s just for starters.


This is a much needed project that lays a foundation for years to come.


Marketo Account-Based Marketing


ABM: the first three letters of the alphabet if you don’t count C-L. It’s also the coolest marketing tactic EVER according to my LinkedIn feed.


However, Marketo Account Based Marketing is not merely a jump onto the bandwagon. To the credit of Mahesh Jeswani (MJ), the PM for ABM, it appears to be a thoughtful first step on what I suspect will be an ongoing roadmap of new account-oriented features. There’s good stuff here.



Credit: Mahesh Jeswani



Account Dashboard


The account dashboard offers an account-centric view of the lead database, bringing together information on fit, engagement, and revenue grouped by account into a single place.


One simple but powerful innovation is a new grid of named accounts, showing information like pipeline, account score, and other account-based data in a single table. Marketo makes it pretty easy to create tables of lead information via smart lists, but grouping this data by account is a challenge. Not with ABM.



Credit: Marketo Webinar



You can also create specific account lists (e.g., Top 50 in High Tech) to segment your view and see a summary of information (e.g, average account score) by list.


Each named account also has its own dashboard. The dashboard is tabbed similar to a lead detail record (although much prettier) and gives some useful high-level metrics such as the number of people from this account in the database, open pipeline, trending account engagement over time, account-wide interesting moments, and top ten best bets for people in the account.



Credit: Marketo Webinar


This is a super useful page. It is not yet entirely clear to me how to expose this data to sales. PM Mahesh Jeswani mentions there will be a new browser plugin to assist the entire account team in collaborating. Look forward to seeing that. Will it also someday be available in Sales Insight?


In any case, think of how powerful this view would be for building sales/marketing alignment around account targeting.


Lead-to-Account Mapping


CRMs are messy things, with people from the same company scattered across different accounts and across your lead object. It isn’t always clear that the new inbound lead you got last week is actually a decision-maker from a target account.


Lead-to-account mapping bridges that gap by automatically linking leads to accounts and consolidating that information into a single view.


This feature will use some sophisticated fuzzy logic and match across multiple objects (leads, contacts) and data points (domain, company name, IP, etc., etc.).


Also if you use an existing lead to account mapping solution, you will apparently be able to plug that into Marketo ABM so that the third-party solution will handle the logic for the mapping. Not sure which providers are supported but this is a nice touch to allow you to maintain an existing part of your stack.


Account Scoring


abm-marketo.jpgMarketo’s standard scoring functionality is lead-centric. But looking at people individually doesn’t help you compare and prioritize your targets at the account level.


Marketo’s account scoring feature will offer several ways of calculating a score for an account.


One method will be to calculate a score based on the firmographic attributes of the company (what we do now as demographic scoring, but centralized at the account-level).


To gauge engagement, you will be able to aggregate the behavior score of all people associated with that account or create an average.


This is a straightforward port of traditional lead scoring into an account-based world, but it formerly required custom coding in your CRM to achieve. Now we’ll be able to do it easily in Marketo.


New Email Editor and Template Picker (Spring ‘16 Release)

In January 2012, I graduated from MailChimp to Marketo. It was both awesome and painful. I got lots of new features that went way beyond simple drip emails (awesome). But I also lost a UI that let me create new email templates without feeling like I needed to call a front-end developer (painful).


The new email editor and template picker will erase that pain for a lot of new customers and also just generally make the email experience way more delightful. I wish they had done this 4 years ago.


Pick your Template


Home - Starter templates.png

Credit: Marketo Release Notes



It’s beautiful! So simple. Nicely done, Justin Cooperman.


This will come pre-loaded with a bunch of templates that will be actively maintained by Marketo (presumably to deal with the latest obscure limitations in Microsoft Outlook). Under “My Templates” you can also create and maintain your own templates, obviously, which will have their own thumbnail and be equally pretty.


Email templates will also get a new templating syntax akin to landing pages to support the new features described below.


Release Notes - Template Picker


Email Editor for the 21st Century


The new email editor incorporates the best of the new innovations in the landing page editor and then leapfrogs it. Variables come to email as well as “modules,” containing elements like images, videos, text, etc. that can be reordered and moved about with ease.


You can easily preview your emails on desktop and mobile. You can easily add and edit your pre-header. You can easily edit source code within a module without breaking the email from its template.


It’s like they just took a list of popular feature requests and implemented them all!


Modules next.png

Credit: Marketo Release Notes


Release Notes - Email Editor


Next-Generation Analytics: Email Insights (Spring ‘16 Release)


Marketo’s reporting module has been in need of an overhaul for some time now. That overhaul is coming (courtesy of Brian Theodore and team), and Email Insights is a glimpse of what the future holds.


This is a well-executed reinvention of email reporting in Marketo. The sheer flexibility it offers compared to current Email Performance Reports should leave most seasoned users feeling gratified. But there’s a lot more going on beyond additional dimensions.


The key innovation here is the “insights” part. The report will proactively surface new insights for the user, such as “you have the most opens in the state of Florida.” What you do with that is up to you - but it's nice the report is taking an active role to push these factoids in front of the marketer.


Insights are also contained in the various trending indicators that accompany the metrics. So the report doesn’t just show you that open rate is x% and CTR is y%, but also helps you put that information in context through a layer of visual cues, indicating whether something is up, down, or the same.


Also worth noting: the reporting engine is based on the Orion architecture, meaning it should be fast.


email_insights.pngCredit: Marketo Release Notes


Based on the demos I’ve seen so far, I like this report. It will be fun to stress test it in real life.


And consider that this report is merely a beachhead in the larger redesign of Marketo’s reporting and analytics. I look forward to seeing this level of thought and care applied to program and opportunity analysis.


Public Service Announcement: Make sure to create your custom dimensions during the 4 week roll-out window. More information here.


Release Notes - Email Insights


Mobile Engagement: In-App Messaging (Spring '16 Release)


The mobile app has a nifty new in-app messaging designer and some advanced logic to target specific mobile audiences. This is really neat, and I wish I had a client with a mobile app to be able to take this for a spin. Would love to hear some feedback from those who have been doing mobile outreach and what their results have been.



Credit: Marketo Release Notes


Release Notes - Mobile In-App Messages


Admin and Workflow Improvements


Audit Trail


Now you can finally find out who broke your smart campaign and deactivate their Marketo account once and for all. Audit trail provides granular logging of all changes (create, edit, and delete) made to assets in design studio and marketing activities and in admin settings.


It comes with a cool new interface that allows you to filter and search through changes for specific information -- targeting, for example, all actions of a specific type, by a specific user, affecting a particular type of asset, or in a particular workspace.


You can access a 6 month history and also export to CSV for compliance and archival purposes. Access to Audit Trail is a new permission in the admin.


No-Draft Snippets (Spring ‘16 Release)


Another feature that is long overdue. If you’ve ever updated a footer snippet and then had to re-approve a bajillion landing pages, you probably wept bitter tears and promised yourself you’d use tokens next time.


Now your suffering is at an end. Marketo has sensibly created the ability (controlled by a new permission in the admin) to either force all assets into a draft when you update a snippet or allow them to maintain their existing state.


Release Notes - No-Draft Snippets


Granular Permissions


With granular permissions, we will be able to set permissions at the asset and folder level. This sounds dry, but there are a lot of potential use cases we will appreciate.


If you are working with an agency and want to give them access only to a development folder without exposing the rest of your marketing activities tree, you can do that.


If you have different teams working in a shared workspace but don’t want them getting their paws on each other’s stuff, you can limit them to certain folders.


If you are an iron-fisted admin and don’t want your marketing users even considering touching your operational programs, you can lock them out.


Friendly Instance Name (Spring ‘16 Release) and Universal User ID


These are two related features that you frankly may not fully appreciate unless you have spent a day working as a consultant and logging in and out of 12 different Marketo instances over and over and over again.


The “Friendly Instance Name” feature allows admins to provide a name for the instance - “Acme Sandbox,” “Acme Production,” “Fluffy,” and so on. It’s up to you. This feature is coming out in Spring ‘16.


This feature paves the way for the incredible Universal User ID. This is single-sign-on for Marketo, allowing you to log in with one user name and then switch seamlessly between different instances that you have access to. The friendly instance name is the key to knowing which instance you are actually logged into.


Release Notes - Friendly Instance Name


Limited Time Access (Spring ‘16 Release)


Similar to the “Grant Login Access” in Salesforce, Limited Time Access allows you to provide access to a user for...a limited time. Fairly self-explanatory.


The uses/benefits of this feature are many. For example, providing a Launchpoint vendor with temporary access to help set up an integration. Combine with granular permissions for bonus points!


Release Notes - Limited Time Access


Outbound Advertising


Ad Bridge will get a bit more automated with automated audience sync, meaning that you no longer will need to push a button to pass leads from a list/smart list into an ad platform.


This is good but still falls short of fully integrating ad bridge into the automated workflow with flow steps. Maybe next year…


In the meantime you can vote up this idea to do just that.


Predictive Content for Email


Give your emails a dash of machine learning and watch your click-through rates soar. At least, that’s the idea behind predictive content for email.


Similar to predictive content for the web (formerly part of RTP), you’ll be able to add a dynamic section to your emails to automatically recommend a “next-best” content asset.


Example: if someone signs up for a webinar, figure out the best white paper they can read in the meantime. Or, if someone downloads a white paper, recommend the best video to watch next, and so on.


Being able to do this in email is potentially a real game changer. You can go outbound on your entire lead database and leverage all of the known data you have to present them with the most relevant content.


Data Model and API


Custom Activities (Spring ‘16 Release)


Custom activities are really cool. You can represent the activities that matter most to your business in Marketo in the way you want to see them. Then you can use this data for targeting, triggering, and personalization.


We all know the standard activities that Marketo has - Visits Webpage, Fills Out Form, Opens Email, Clicks Link in Email, etc.


Custom activities are just like that but they are specific to your business. For example, “Registers for a Class” (education), “Makes a Purchase” (ecommerce), “Creates New Doo-Hickey” (software app that allows people to create doo-hickeys). Say goodbye to virtual pageviews.


The only downside right now is that you need to create custom activities via the REST API. API calls are a finite resource, so you need a well-considered strategy for what you create. Also, not everyone has the technical know-how to implement a REST solution, which is a bit more involved than using the Munchkin API. However, it’s only a matter of time before third-party solutions emerge to make this process simpler even for small, non-technical marketing teams.


Side Note: Custom Activities vs. Custom Objects


Custom Activities are not to be confused with custom objects, which were announced at Summit last year and became generally available in the Fall ‘15 release.


Custom objects represent additional entities that live in your Marketo instance - they are things that have properties that can change, like courses, cars, tractors, shoes, or high-end kitchen appliances.


Whereas, like their name suggests, custom activities are actions or events that happen at a moment in time, with details that can’t be changed.


Release Notes - Custom Activities

Documentation - Custom Activities


REST API Updates


The REST API continues to expand, with new APIs for landing pages and forms. This completes the asset management component of the REST API, meaning you now have programmatic control over all Marketo assets.




Microsoft Dynamics users in our midst will be pleased to hear that the performance of that integration will see vast improvements thanks to Orion.


Ajay Awatramani states it will be “zippy fast” - 20x faster for the initial sync and 5x faster for the incremental sync. Maybe we’ll see the Dynamics integration continue to be brought up to par with Salesforce.


Wrapping Up


I really enjoy the Customer Love and other product-focused sessions at Summit. The PM team works hard and is responsive to user feedback. At the same time, us Marketo users are a critical and demanding bunch, which no doubt keeps them on their toes. That’s a good thing. But every now and then a thanks is in order. Thanks!

The term “data-driven marketer” is rapidly becoming a redundancy: to be a marketer these days is to be data-driven, whether you work in demand gen, marketing ops, content, or social.


And yet, being an evidence-based marketer is not without challenges.


  • Data - We are simultaneously drowning in data while also missing some critical pieces of it.
  • Time - Marketers still have programs to run and don’t have all day to spend looking at reports.
  • Skills - Data analysis is a skill. Not all marketers have developed it. And that can lead to very bad decisions (a little analysis is a dangerous thing).


Predictive analytics software is so appealing precisely because it solves all these problems. Predictive tools tools tap into our existing datasets, combine them with thousands of external signals, and use advanced statistical methods and machine learning to produce statistically valid insights in very little time.


If we are serious about being data-driven, it’s only logical that more and more of our marketing decision-making will become powered by this sort of technology. What will this future look like?


With this question in mind, I spoke to six predictive analytics vendors about how predictive can be applied to empower marketing. I distilled their feedback into five top use cases for integrating predictive into marketing automation that are all possible today.


How Predictive Analytics Tools Work


Before we explore these use cases, let’s consider how exactly predictive tools work and how they should fit into your existing technology stack.


There are many predictive marketing vendors, each with their own unique features, but at a high level they all do the following:


  • Read Internal Data - Evaluate the demographic/firmographic data in your CRM, behavioural data in your MAP, or potentially even information from a product database or internal data warehouse.
  • Add External Signals - Enrich those records with external data points on your contacts and accounts (e.g., technologies used, job posting activity, intent data, and so on); most vendors have their own web scrapers to collect this information and also purchase it from third-party data vendors.
  • Build a Model - Evaluate your historical success metrics and use analytical techniques to build a predictive model. This model identifies variables in your data that correlate with successful outcomes, and it can now be applied in various ways to make data-driven marketing decisions.




How Predictive Marketing Fits into your MarTech Stack


So how does a predictive tool fit in with your existing marketing automation platform? The key thing is to understand the roles of each system in your stack.


The System of Execution


Automation tools like Marketo excel at enabling marketing execution. Marketo is a workflow machine, allowing you to define rules-based actions with enormous flexibility and complexity.


Marketing automation also tends to be the central integration point for all marketing activities - the hub of the wheel that drives outreach across multiple channels through more specialized integrated apps. You can apply that incredible workflow power to achieve outcomes directly or through a variety of integrated systems.


For all these reasons, it makes sense to think about marketing automation as your primary “System of Execution”.


The System of Insight


While marketing automation gives you the tools to act, they don’t necessarily tell you what you should do. That’s where predictive comes in. A predictive tool can ingest all your data, combine with external signals, and provide decision support and guidance. As Jim Walker (VP of Marketing at EverString) put it to me, this is your “System of Insight”.


From this perspective, predictive analytics can be the “brain” that powers the execution of your sales and marketing programs via your MAP, CRM, and through a stack of specialized execution tools.



(Side note: there are clearly some areas where Marketo is both system of execution and insight -- e.g., RTP provides predictive content recommendations already. And Marketo is investing heavily in building up its own predictive capabilities, so we may see more convergence between execution and insight going forward.)


Now that we’ve got some context, let’s look at how this interaction between insights and execution can work in practice with some concrete examples.


How to Combine Predictive Insights with Marketing Automation


Lead Scoring


This is the most well-known marketing application of predictive analytics, as most vendors started out as predictive lead scoring tools before expanding to other applications.


Predictive lead scoring is a lot like the traditional lead scoring that we build in Marketo, only smarter. Instead of using anecdotal insights or limited data analysis to decide that a job title containing “CMO” should be +30 points and someone attending your webinar should be +15 points, you feed all your data into your predictive model and let the algorithms determine how leads should be scored.


Don’t get me wrong: traditional lead scoring can still be tremendously valuable in prioritizing your leads. But there are limits to how far you can take it, even if you are a data geek and spend a lot of time working in a spreadsheet. A computer can consider thousands of variables and the correlations between them to deliver a lot more predictive power.


Calculating a predictive score for a lead is just one part of the puzzle. Many tools can also help enable salespeople to act on the data. For example, Nipul Chokshi, Head of Product Marketing at Lattice, stresses the importance of making it easy for sales to take action on the highest-scoring leads. He suggests to accompany the predictive score with insightful context, such as the external signals that drove the score. For example, you could tell a rep that the prospect account uses complementary technologies or that contacts at the company have been demonstrating intent, and so on.


He notes it’s also important to make the predictive data actionable - for example by providing call plans with talking points and discovery questions all in one easy-to-access place.


These steps ensure marketing is building real alignment by providing full context behind the score and actionable insights for next steps.


Demand Generation/Account Targeting


Lead scoring is fundamentally an inbound application of predictive analytics. Demand generation turns this on its head, allowing you to go outbound and support an account-based marketing strategy.


Instead of telling you which of the leads already in your funnel are the good ones, predictive demand generation tells you which accounts should be in your funnel and then gives you the information for contacts on those accounts.


Account selection is sometimes called “look-alike” modelling, because the tool will take the profile of what a good customer looks like for your business (How many employees? Annual revenues? What technologies used? etc.) and then match that profile against a proprietary database of companies. Every vendor I spoke to has one, and they typically have millions of accounts.


You can then import these new contacts directly into your CRM and MAP and from there engage them in outbound sales and marketing programs.




Nurturing is all about cultivating relationships with the right people until they are ready to buy. But how do we know how ready they are? Using predictive analytics there is a variety of ways to more intelligently route leads into the correct nurture.


Architecting Your Nurture Around Fit


Sean Zinsmeister, Senior Director of Product Marketing at Infer, shared some of his approaches for architecting a lead nurture using predictive analytics with me. In Sean’s diagram below, leads are routed based on likelihood to convert (fit data) so the messaging sequence can be calibrated accordingly. “A” leads are routed directly to sales while other leads are given increasingly softer messaging the less likely they are to be sales-ready.




Architecting Your Nurture Around Buying Stage


When it comes to nurturing, Amanda Kahlow, CEO of 6Sense notes that “a key factor missing in this is timing: whether the prospect is even in an active buying cycle to care about your products and services.”


According to Kahlow, the key to resolving this is better activity data that allows marketers to connect the behavioural signals collected on their own website with “buyer intent signals from third-party activity data across the B2B web.”


For example, she notes that if multiple employees from Acme Inc. are clicking on ads for server virtualization technologies, browsing content related to virtualization on major publisher sites and buying guide portals, or downloading related resources across the B2B web, Acme Inc. might be considering a purchase in this area.


“This intent data from Acme’s buying committee is aggregated at the account level and fed into our predictive engine, where it can be used to determine not just whether Acme is in-market for server virtualization, but even the specific buying stage they are in.”


I think this intent data has powerful implications for your nurturing. For example, if you were equipped with detailed buying stage insights about your prospects, you would no longer need to start everyone on an early-stage or “awareness” nurturing track just because they are new to your database. If you have intent data to suggest they are actually well-along into research or even consideration of your specific product, you can route them accordingly and give them the most relevant content.




A marketing rule of thumb is that the more relevant your content is to a prospect, the greater impact it will have.


One big challenge to relevant marketing is a lack of knowledge about your prospect and their interests. Are they after product A or product B? Which of 5 different pain points/value propositions should you emphasize? Should you talk about integrations with complimentary products they are already using? Many predictive tools can help you solve these problems.


Nipul Chokshi suggests creating multiple scoring models for different products. This way you can evaluate which product has the highest score and provide the lead with content they’re most likely to be interested in.


Other vendors expose rich demographic/firmographic/technographic data about your prospects and allow you to build marketing campaigns around those data points. I’ve seen Leadspace at work during a client engagement and was impressed with how it provides full-scale data enrichment in addition to building predictive models on top of that data.


As an example of what this enables, you can detect if a prospect is using a complementary technology and then personalize emails and landing pages to highlight your compatibility with that technology or have a completely different series of touchpoints based on this knowledge. It also allows you to potentially cover both data enrichment and predictive needs with a single app.


Lastly, it is also possible for a predictive tool to do the heavy lifting in building rich profiles for segmentation - profiles that go far beyond just a handful of data points. Infer has a really interesting tool for this purpose, which they call their Profile Management solution. It aids users in combining many data signals together to form what Zinsmeister calls a “hyper-segment”, a profile of one of your ideal customers. Infer will use its predictive model to assess how desirable the segment is in real time and gauge the business impact all the way through opportunity win-rate.


Now you can work to develop highly relevant content that is completely designed around the needs of that profile. And as you identify or acquire new contacts that fit this desirable profile, you can feed them the extremely targeted messaging that resonates with pain points you know they have.


Marketing Program Evaluation


Jessica Cross, Director, Demand Acceleration and Customer Marketing at EverString, had some great tips on using predictive analytics to evaluate your marketing programs. By evaluating the quality of leads each program produces in real time, you can get a leading indicator about where to invest your marketing dollars. Without predictive, two programs producing the same volume of leads might seem equally appealing, but with the lens of a predictive model you could see immediately that one program is producing leads of much higher quality and should be prioritized.


Predictive Campaigns


During his keynote at Marketo’s Summit in 2015, CEO Phil Fernandez presented the metaphor of the self-driving car as a vision for the future of marketing. In this vision, the marketer will input the goal, and the technology will figure out how to get leads to that destination.


This vision would seem to be a logical conclusion to the development of predictive analytics. But does this mean that the marketer’s creative inputs will soon be obsolete? Far from it, for the marketer still needs to create the destinations on the journey (by designing relevant content and touchpoints), while predictive tools provide the capacity for determining the best route for each customer to take.


Mintigo’s new Predictive Campaigns functionality is designed to do exactly that. Predictive Campaigns can evaluate the relationships between marketing programs, contact and account profiles (defined by technographic and intent data), and successful outcomes to develop predictive models for campaign orchestration.


These models would provide the ability to determine, for any particular person, that they should receive the following touches in the following order through the following marketing tactics to have the best chance of reaching a successful outcome. In other words, this helps marketers offer the right message through the right channel to the right person at the right time – essentially 1-to-1 personalized marketing that’s dynamic to each individual buyer’s journey.


It should be said that it is still early days for this capability and the feature is only available for Eloqua right now , but nonetheless it is a powerful example of where these technologies are headed. And hopefully they will roll out a Marketo version soon.


Lessons Learned


What struck me most when chatting with all these smart and passionate vendors was the extent to which the market is converging in terms of core functionality. All the tools work in more or less the same way and have a shared set of basic applications. The ability to build a predictive model in itself is rapidly becoming commoditized.


Where vendors are differentiating is around the use of that data for enabling sales and marketing to make better decisions. We are beginning to see some new and novel applications emerging, and this is where I would expect to see the most innovation in the months and years ahead. The practice of leveraging predictive data in the system of execution (marketing automation) is still in its early days - I believe the applications above only scratch the surface. This is exciting, because there’s lots of room to grow.


Share Your Use Cases


If you have figured out some advanced ways of using predictive analytics in your marketing automation or sales enablement efforts, I’d love to hear about it! Please share in the comments.




I’m grateful to the following people for spending some time to help inform this post.


Amanda Kahlow, CEO at 6Sense

Jessica Cross, Director, Demand Acceleration and Customer Marketing, and Jim Walker, VP of Marketing at EverString

Sean Zinsmeister, Senior Director of Product Marketing at Infer

Nipul Chokshi, Head of Product Marketing at Lattice

Kylee Hall, Senior Director of Marketing at Leadspace

Tony Yang, VP of Marketing at Mintigo


Disclosure: I work for Perkuto and EverString is a partner of ours.

Swag: it can be a great way to introduce yourself to leads and make loyal customers feel appreciated.


But let's face it, finding really cool swag is a challenge. And most of the time you also have to spend a ton of time ordering, transporting, and distributing it. printfection_demo


Enter Printfection. Their stuff looks awesome, and they let your swag-consumers self-serve their orders with a very smooth online process. Just send someone a one-time URL, and that person can choose an item, enter their address, and place an order in about 90 seconds.


There's just one missing link...out of the box, you still need to do a fair bit of manual work and spreadsheet monkeying to bridge the gap between Printfection and your Marketing Automation platform.


How to Automate your Swag Distribution Right Now


If you use Marketo, you're in luck, because you can have a "lights out" swag distribution machine in about 5 minutes — meaning you can start offering leads t-shirts and hoodies as easily as you do white papers and e-books today.


How It's Done


The process is really easy even if you can't write a line of code. The secret ingredient is a Marketo Webhook, an extremely powerful feature that essentially lets you make an API call from within a Marketo Smart Campaign.


Don't be scared off by terms like "Webhook" and "API". Even if you consider yourself non-technical, you don't need your developer to do it. This one's all you. Let's go!


Step 1: Create a Printfection Account


You'll need to have your Printfection account ready to go before you can integrate it with Marketo.


You can see all the steps in more detail over at the Printfection Website, but basically you need to open an account, create a campaign that offers individual give-away URLs, and stock it with some promotional inventory (like this cool beanie with tassles).


Once you've got all that set up, there are two pieces of information you'll need from your account:


  1. Campaign ID: You can find this by clicking on the campaign inside the Printfection platform and copying the numbers at the end of the URL, after "storeid=".


  2. API Key: Click on Account Options > API Access and copy down your API key.


Caution! Your API key gives great power. Do not post it on the internet. The one above is not a real key.


That's all you need from the Printfection platform.


Step 2: Create a Custom Field for Your Give-Away URL


Let's say you're running a campaign to give away your branded tassled beanies to anyone who signs up for a free demo of your product.


You have a "Request a Demo" form all set up. Once people fill out the form, you want to send them a link to get their free beanie, and have the order processed and shipped with absolutely no manual work required by you.


To do this, we first need to create a custom field in Marketo to contain this URL. (You'll use a token to merge this field into your emails later.)


Inside Marketo, go to Admin > Field Management > New Custom Field. Choose a "String" field and call it whatever you'd like.




Step 3: Create Your Marketo Webhook


In Marketo, go to Admin > Webhooks > New Webhook. The screenshot below shows what yours needs to look like -- I'll explain each field in detail after.


Printfection Marketo Webhook


Webhook Name: Something descriptive and memorable, so you can easily call it in Flow Steps and recognize it in a lead's Activity Log.


Description: Help your future self or co-workers know what this Webhook is for and what it should do with a concise description.


URL: This is the URL of the Printfection API endpoint used to create a new order. (An "order" is what allows a lead to claim some free swag.)


However, the URL needs to be merged with your secret API key to authenticate you and give access to your Printfection account. The format is:



Substitute your actual API key in place of [your-api-key].


(Side note: it is also possible to authenticate by adding a custom header and using Basic Access Authentication. When viewing your Webhook, click on Webhook Actions > Set Custom Header.)


Request Type: Choose "POST".


Template: This is the body of your API request. In it, all you need to include is the Campaign ID of the campaign you want to call. Copy the syntax below and substitute in your campaign ID from your own Printfection campaign.



Request Token Encoding: Choose "JSON".


Response Type: Choose "JSON". Your Webhook is ready to go!


Step 4: Map the API Response


When you run a lead through this Webhook, it will now make a call to the Printfection API and create an order. In response, Printfection will send you some details about that order. You can see all these details in the lead's Activity Log.


Here you can see that the Webhook was called and that there was a response.





And here you can see that the response actually contains an attribute called "url".


Marketo Webhook Response URL


This is the little gem you want to extract and send to your leads. Fortunately, Webhooks makes that fairly easy.


Go back to Admin > Webhooks and select your new Webhook in the left-side panel. Underneath, you'll now see a section where you can add and edit Response Mappings.


Marketo Webhook Response Mappings


Click on "Edit" next to Response Mappings, and add a new mapping in the dialogue that appears. The "Response Attribute" is going to be "url" (this identifies the part of the response we want to extract) and the field is going to be the custom field you created in Step 2.




Basically, you're telling Marketo, "look through the response until you see an attribute called 'url', then take what comes after it and put it into this field." It's that easy!


Step 5: Deploy Your New Marketo Webhook


You can deploy your Webhook using a Flow Step in any Marketo Smart Campaign. For now, let's create a very simple test campaign so we can verify that it's working.


Create a Test Lead


Go to Lead Database > New > New Lead and create a test lead with an email address that can receive email. (Hint, if you use Gmail or Google Apps, you can use [your-email]+[any-string]@[your-domain].com and it will still go to your [your-email]@[your-domain].com address!)


Build a Test Email Asset


To make sure that the give-away URL is working properly, we need to create an email that will send that URL to the lead. Create an email and insert a text call-to-action that links to your give-away URL field using the token for that field. When the email is sent, the token will by replaced dynamically by the lead's unique give-away URL so they can redeem their swag.




Build a Test Smart Campaign


Smart List


Use a "Campaign is Requested" trigger (so we can easily request this campaign for testing purposes).




Flow Steps


In the first flow step, we're going to request the Webhook we created. In the second flow step, we're going to send the email asset containing the give-away URL to the lead.




Note: for a production (rather than testing) campaign, I'd certainly recommend triggering off of the  data value change of the URL field being populated by the webhook, to be sure that the API call has finished its round trip before sending the email. In my testing this worked just fine as displayed above, but you want to make sure the email does not send without the URL field being populated.


Send Your Test


Go to Lead Database > All Leads and look up your test lead by email. From the menu, select Lead Actions > Special > Request Campaign, and request the test campaign you just build.


Step 6: Rejoice!


If all has gone well, you should receive your test email with a link to your Printfection give-away. Click on it.




You should now be at the Printfection page where the lead can redeem their merchandise. They're happy getting a cool gift, and you're happy because software did all the hard stuff.




You've just added some swagger to your Marketing Automation. Good work!


Bonus Step: Think Big


Now that you've seen how easy it is to automate the offering and distribution of swag through standard Marketo functionality, it's time to think about how swag items could increase velocity and conversion at different stages of your funnel. How could you use swag to attract more net new leads? Re-activate recycled prospects? Increase opportunity velocity? Time to run some experiments and see what impact it has on conversion, cost, and ROI metrics. But you basically have a whole new channel open to explore. Have fun!

Do you want to send a different message to someone if they email you during business hours or after hours? What about on the weekend? Should leads be routed to a different sales team based on the time of an inquiry? What about segmenting leads based on dayparting or day-of-week? You can do all this with Marketo and a connected CRM like Salesforce.


What You Need


To use time-based logic, you need the following:


  1. Fields to contain date/time stamps and (optional) day of week
  2. Marketo Smart Campaign to populate date/time fields
  3. Salesforce logic to parse date stamp into the day of the week (optional)
  4. Marketo Smart Campaign to route leads based on time and (optional) day of week


Step 1: Create Your Fields


To get the benefit of routing by both the time of day AND day of week, you’ll need to create your fields in your CRM. I’m using Salesforce for this example. You can call these fields whatever you like and can track whatever behavior you like. Let’s assume for this example you are interested in tracking the time of a “Contact Us” form submission.


Create a Field to Hold Your Time Stamp Only


In Salesforce, go to Setup > Customize > Leads > Fields > New and use the type “Text”. Make sure to repeat this on the Contact object and map these fields to each other so that this field can be used for both Salesforce Leads and Contacts in Marketo. Why use “text”? This will make sense later on!




Create a Field to Hold a Date/Time Stamp


In Salesforce, go to Setup > Customize > Leads > Fields > New and use the type “Date/Time”. Make sure to repeat this on the Contact object and map these fields so that this field can be used for both Salesforce Leads and Contacts in Marketo.




Create a Field to Calculate Day of Week


In Salesforce, go to Setup > Customize > Leads > Fields > New and use the type “Formula” with a return type of “Text”. Make sure to repeat this on the Contact object and map these fields so that this field can be used for both Salesforce Leads and Contacts in Marketo. Here’s a screenshot of the formula you need (substitute your actual field name instead of “Last_Contact_Us_Date__c”):




And here’s text you can copy:


MOD(DATEVALUE(Last_Contact_Us_Date__c) - DATE(1900, 1, 7), 7),
0, "Sunday",
1, "Monday",
2, "Tuesday",
3, "Wednesday",
4, "Thursday",
5, "Friday",
6, "Saturday", "Error")


Here’s how this formula works: First you get the date-only value of your date/time field. Then you subtract another date from it with a known day of the week. Then you calculate the modulus (remainder) when dividing that number by seven. The remainder indicates the day of the week of the date value you are trying to parse. You can then use a CASE formula to populate a string with the name of the day of week based on that remainder. It’s a little complicated, but basically you just need to know it works. :)


Step 2: Stamp Your Date/Time Fields in Marketo


The next step is to use a triggered Smart Campaign to populate your time and date/time fields based on the lead behavior you want to track. Our goal is to stamp the time and date/time fields, pass the data into Salesforce so our formula field can calculate day of week, and give that data time to sync back to Marketo. To ensure the right order of operations, we’ll be using a simple two-campaign structure here where the first campaign will request the second.


Marketo Date_Routing_Program_Overview


Marketo Campaign #1 - Stamp Your Fields


Smart List


Use a “Fills Out Form” trigger with the name of your form.


Marketo Date_Stamp_Trigger


Flow Steps


  • Use a “Change Data Value” flow step referencing your time field and populating it using the Marketo system token, {{system.time}}.
  • Use a “Change Data Value” flow step referencing your date/time field and populating it using the Marketo system token, {{system.dateTime}}.Note, these tokens will use the timezone set in your account settings.
  • Sync the lead to SFDC
  • Wait a few minutes, to ensure that the formula field is populated
  • Sync the lead to SFDC again, to bring the formula field value back into Marketo (this is usually necessary because formula field changes will not trigger a sync by themselves)
  • Wait a few minutes again to ensure the formula field value is safely in Marketo
  • Finally, request the second campaign. You’ll need to build it first in the next step before you can add this Flow Step.


Marketo Date_Stamp_Flow


You’ll note this method is a bit “expensive” in terms of Salesforce API calls, so use it judiciously.


Step 3: Do Some Time-Based Routing!


Now you’ve got everything you need at your fingertips to route based on time of day and day of week. Our final steps are to build a Smart List to crunch the logic, and then a Smart Campaign to take the action we want.


Build an “After-Hours” Smart List in Marketo


To determine whether a lead has made an inquiry outside of business hours, we’re going to use a Smart List as a local program asset. The Smart List will check whether the time of last contact was before 8 AM or after 5 PM on Monday-Friday, or anytime on Saturday or Sunday. Here’s what that Smart List looks like:


Marketo After_Hours_Smart_List


How does this work? Well, consider that the actual time-stamp looks something like, "11:35 AM (-800 GMT)". So basically you are parsing the time-stamp string to see whether it contains a combination of certain hours (numbers that end with ":") and AM or PM. And that’s why this field must be a text field, because otherwise we couldn’t use the “contains” operator. Using advanced filter logic you can combine these filters together with the day of the week to make your targeting as granular as you like! Make sure to check your filter logic and test, test, test, to be sure you’re getting the intended result.


Build Your Marketo Outbound Smart Campaign


In this example, the campaign is sending an email.


Smart List


Use a “Campaign is Requested” trigger. Remember this request is coming from the Flow Steps of the previous campaign.


Marketo Auto_Responder_Trigger


Flow Steps


Use a Send Email flow step that checks for membership in our “After Hours” smart list using a Choice. If not a member, send the standard auto-response.


Marketo Auto_Responder_Flow


Think of the Possibilities…


Sending different emails is just one option. You can do lots of different things with this technique, including assigning a lead to different sales team or a third-party answering service based on time of day, using different marketing channels (an SMS, etc.) depending on time of day, using the data to see which days of the week bring in the best leads...use your imagination! See also a similar use of this technique from Josh Hill for routing leads to different locations based on time of inquiry.


Credits: Credit for the technique to parse the time-stamp using filters should go to Adam New-Waterson, since I’m pretty sure I first saw this in a Community post he wrote. Thanks Adam! Credit for the formula to parse the day of the week name from a date value belongs to someone on the Salesforce Success Community. Unfortunately I can't remember who, but thanks, whoever you are.

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