Will Harmon

Marketo Master Class: Lead Scoring with Chris Wilcox

Blog Post created by Will Harmon Employee on Aug 21, 2019

 

In this edition of Marketo Master Class, we're teaming up with Marketo Champion Chris Wilcox to get into the weeds of Lead Scoring. Our aim was to break down the complexities around Lead Scoring and provide the Marketing Nation Community with actionable insights into best practices. Are you leveraging Lead Scoring in other innovative ways? Let us know in the comments!

 

1. What are the attributes of a successful lead scoring model? What are some factors that have resonated well with your Sales org?

 

I find the most important attributes of a successful scoring model are that they are both practical and scalable. From a practical perspective, your model needs to be designed for your business, your leads, and your pipeline. There is no one-size-fits-all lead scoring model. Of course, many concepts translate from business to business, but your organization needs to understand who are the best prospects for your product or service, and what actions those leads are taking that indicate that they may be primed for a sales conversation. Designing a model around your business will ultimately drive better success as you’ll be funneling the right leads to the right people.

 

Secondly, you can’t design a scoring model that requires every touch or action be monitored and scored by your marketing team. It needs to be designed and implemented in a way that works at any scale, and this can mean getting creative with the way your teams organize around certain engagements like conferences or live events. Making sure there is visibility into those actions in Marketo in as real-time as possible can be a (fun) challenge.

 

I have found that by including your sales organization’s leaders in the discussions around what factors feed the leads being passed over to your sales team can help build organic support from within. Many times lead scoring can feel like a black box from a sales perspective, but by bringing them into the conversation and revealing the “man behind the curtain” so to speak, it can really help them better understand that a lead scoring model’s purpose to put the right people in front of your sales organization at the right time to drive better success within their sales pipeline. Getting their perspective and input on what helps them do their job better is a great way to drive adoption and alignment between your marketing and sales organizations. 

 

2. What are some of the more sophisticated/non-conventional lead scoring strategies you have implemented in the past? 

 

Most lead scoring models are for identifying the best prospects, but an interesting use case is to build a scoring model for servicing existing customers.  To do this, build a scoring model to classify your existing customers using the relevant attributes just like you do for prospects. Contract size, subscription level, all of the quality indicators your firm uses to classify your customers. Another way to do this could be to work with your sales team to have them select target current customers that they want to keep a pulse on as well using a boolean field on the contact record that they can update in the CRM.

 

Bucket them into categories like low, medium, high value customers (get as granular as you’d like and is practical, I’ve seen upwards of 20+ levels of customer value J ). You can execute the classifications a few ways, by using a tiered score value (e.g. 10 for Low, 20 for Medium, 30 for High), creating a custom field to define with these smart lists, or even use a segmentation to maintain the contact’s category. Whichever process makes sense to you and for your instance.

 

In this example, I have a new score field of “Servicing Score” that will change based on the customer’s attributes. These smart campaigns would run periodically (weekly/monthly) to keep the score current.

 

Servicing Category Scoring Program Structure:

 

“High” Value Category Smart List:

 

“High” Value Score Change:

 

Servicing Score Token Values:

 

From there, I like to combine this with a custom field that date stamps a contact when Marketo sees logged sales email or phone calls with an existing customer. This can be tricky depending on how your sales team logs activities and how Marketo can interpret them. You may want to partner with your CRM admins if needed to get this field created and populated accurately.

 

Trigger to Populate “Last Contact Date”

 

Using these two things you can classify your existing customers into groups and overlay which customers have not has a sales contact in the last XX number days. Immediately that group of people (or at least the high-value subset) should be of interest to your sales team which you could communicate via alerts and/or Smart List subscriptions (or SFDC reports if your score values make it into your CRM!)

 

For the scoring piece, I like to combine the Servicing Score with a modified version of Engagement Score (webinar attendance, web visits, email clicks, etc.) to help the sales team identify a good time to reach out to that pool of customers. The reason I use a separate score value is that you will want to apply additional choice options on your change score flow steps for the servicing behavior based on the Last Contact Date which you wouldn’t want to do with your overall behavior score. You can build these right into the flow steps of your existing behavior score rules, and even use the same token values. You just add a choice based on your last contact date cutoff.

 

 

You might also have a window of time (90-180 days) where you watch for an activity to pass the lead over the sales, but then a cutoff where if the last contact date crosses you simply hand the lead off at that time.

 

From here, you have trigger programs watching for Service Behavior Score changes to contacts with a Service Score for whichever groups you want to include and either assign a task or push an alert to their sales rep for follow up.

 

 

 

This seems complicated, but it’s not!  Identify your best customers however you can, try to understand how long it’s been since a good sales contact has taken place, and watch for the activity of those customers to alert your sales reps. Also, whenever last contact date updates, make sure you’re resetting your Servicing Behavior Score value to =0!

 

To get started with something like this, you could do something as simple as watching for activity on things like the pricing page of your website, contract terms and conditions pages (if you have them) with your high-value clients to give your sales team some insight into that activity. You don’t have to start with the most complicated servicing model

 

3. What results did the above lead scoring models achieve that a standard model could not deliver?

 

Servicing lead scoring models can help your customer churn and retention rates, and give your sales team a leg up on taking care of clients that matter to your organization by systematically surfacing important customers that need a sales touch.

 

4. How do you strategically update your lead scoring model without having to reinvent the wheel every time? 

 

This all comes down to what attributes are delivering the best outcomes for the MQLs that are being handed off to sales. Make sure you’re properly populating acquisition programs to understand first touch attribution to identify the best lead sources.  I typically try to take a deeper dive into the best recent Close>Won opportunities to understand what about those opportunities made them such great wins (vertical? company size? industry?) to see if there are potential levers to pull to overweight those types of opportunities in the future, and to underweight those attributes that lead to more Close>Lose opportunities.

 

These changes should be small and incremental unless the current outcomes of the scoring model are extremely poor. We want to continue to push the right people down the funnel, and understanding what works and optimizing our scoring is the easiest way to do it, but we don’t want to constantly change who we’re feeding to sales without proper discussions and analysis. That can quickly lead to misalignment and confusion between marketing and sales.

 

5. How long does a lead scoring model need to be active to determine its success and what metrics do you consider?

 

I think this is entirely dependent upon the length of your organization’s sales cycle, but there are ways around completely succumbing to the (sometimes) lengthy cycles many organizations operate within.

 

For example, if your sales cycle takes 4-6 months, you might want to optimize your scoring to simply get more SALs instead of the best-case scenario of optimizing towards Close>Won opportunities. In most cases, it should take at least a few months to really prove to disprove the scoring model, but there are definitely cases where it should be shorter.

 

When evaluating the validity of your scoring model, a significant amount of analysis should be put into what activities are feeding the positive sales outcomes, which is where having a plan for attribution and properly ensuring acquisition programs are getting populated play a critical role in your ability to properly evaluate a scoring model.

 

6. When should you use a global vs. local program in your lead scoring strategy? 

 

In my experience, I have found using global lead scoring rules saves a ton of time and effort in the long-term from a maintenance perspective. Even if you have multiple scoring models in place in your instance, having those score tokens and trigger programs operating globally saves a ton of time when you want to make changes or adjustments to your scoring. Obviously, this can all be done at the local level, but there is a level of scalability and ease of maintenance of a global program structure that you can’t achieve with local program builds.

 

Typically, I see global scoring programs built off of program status change triggers, or using some interaction as a trigger point (fills out a form, visits key web page, etc.)

 

7. How do you leverage tokens to scale your lead scoring model? 

 

The biggest place where we leverage tokens is in the scoring values themselves. Simply to streamline the management of scoring change values for any given activity or engagement, creating all of those values as tokens puts all of your scoring values in a single place to manage and maintain which can save a ton of time in the long run, especially if you’re running multiple models or tweaking your scoring frequently.  I find it best to keep a single Scoring folder wherever you keep your Operational or Data Management campaigns and keep all of your scoring values in that parent folder.  This way, if you want to use the same score token across multiple scoring model programs, you can do so.

 

In the Servicing Score example above, you can reference the exact same token values for that score with no additional build, you’re just applying a choice to the change score to account for the servicing need.

 

 

 

8. Do you have any innovative plans for future lead scoring models?

 

The biggest innovations with lead scoring are all around predictive analytics and/or next-best-product type models. Maybe organizations are working with data teams to be able to better predict the next best product or offer for any given individual based on a variety of factors. You might have multiple demographic or quality scores running, one for each product or category your firm offers and having a model that would identify a contact’s likely best fit could drastically improve the quality of contacts that get handed off to sales as MQLs.

 

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