Hello, Marketing Nation! Welcome to the fourth post of "Tuesday Tech Bytes 2024 Edition.". Today, we're diving into the world of Sales and Marketing Alignment by our very own Balkar Singh Rao and reviewed by the rest of the Tech Bytes Team! Use these to supercharge your marketing efforts with Adobe Marketo Engage.
Sales & Marketing alignment challenge is not an issue of systems. It is an issue of cultures. However, some systems show promise. Many of us face the same problems: "Why didn’t this person sync to SFDC?" or "Why didn’t this lead qualify?" These questions pop up in most of our projects and roles, showing a common issue—unclear definitions and confusing processes. What exactly is a lead? Is it someone who shows interest or just a prospect? Without clear definitions, things get messy.
To fix this, we need clear definitions and simple steps. So, let’s cut the story short - you need a definition which can be distilled into the system. Even with GenAI, I can’t talk to a smart list yet. I need filters and triggers. By finding out which system is causing the problem, we can fix it effectively.
This problem isn’t anyone's fault; it's everyone's issue. With a focused approach, we can make improvements.
Use Case : Lack of lead-flow knowledge
We take the use case of a lead flow system in place, which marks people as MEL, MQL and so on. It also updates stages like SAL, SQL etc. However neither this is documented, nor everyone has a good understanding of its nature. This situation has resulted from inherited systems.
You can apply these steps to more programs, however we will focus on one. Lead Lifecycle. We shall take an example of the following program. This is to drive understanding in this blog post. Note that the real lead lifecycle models are not limited to this flow - there are usually more complexities and business specific challenges.
Step-by-Step Solution to Enhance Alignment
A simple flow chart isn’t enough. Alignment needs a system which you can use, contemplate, and iterate on to improve. Sales-support needs some systems which you can refer to and respond with speed, including recommendations to consider.
Hence, the first step is to document your lead flow. One way to do this has always been flowcharts and visuals. I have been a fan of this method until recently, as we now have more abilities. The problem with flow charts is that not everyone likes it - despite their elegance. The more strategic role, the less technical they want to get - flow charts are not inherently technical. They usually simplify complexities. However, alignment also means being able to talk in the language of your stakeholders. So this is what I have found recently useful. Instead of creating flow charts, create more accessible knowledge to drive effective education.
Step 1 - Export the rules of your workflows.
You can do this by going to smart campaigns of your program.
Your export has great detail about the logic of the smart campaigns. Export this for all smart campaigns which impact lead flow in the context of the lifecycle model. In this case, the export shall be for all the smart campaigns built to stamp stages (Known, MEL, MQL, SAL, SRL, SQL, Customer). The export looks like the following, and this is very valuable to understand the logic.
You can see it lists details about its type - whether it’s triggered, or batch. It clarifies qualification rules. It also mentions the member count. In addition, it specifies the criteria which qualifies people to run through it. In this case, it specifies if “Person Status” changes to “Accepted” via salesforce.com, and if “Lifecycle Status” is “MQL”, the smart campaign will update the “Lifecycle Status” to “SAL”.
This is the expression of the logic in natural language, which is the next step of transformation of the exported file. Transform your exports into natural language.
Step 2 - Transform rules into natural language.
Natural Language Description of the Smart Campaign which MQLs people -
This workflow runs when someone’s “Person Status” is updated to “Accepted” and the source is “salesforce.com”. It applies only to those people who have their Lifecycle Status as “MQL”. People who qualify go through this workflow which marks their “Lifecycle Status” as “SAL”. The workflow is a triggered workflow, and each person can run through this flow only once.
Write this description in a Text file, name it “MQL” and save it.
Apply this step to all your smart campaigns in the Lead Lifecycle model.
This exercise will give a natural written expression of details of the smart campaigns and what they do. You will have a text file which contains the rules of the smart campaigns of the program. By the time you reach this step, you will start to develop some clarity and may be able to help sales for some of their queries.
Step - 3 - Configure a GPT Agent
In ChatGPT, navigate to “Explore GPTs” > “Create”
Configure a GPT. You don’t need any technical knowledge to do this. It’s plain English. A GPT is an AI agent which you can design by writing instructions in natural language, and providing it with any special knowledge. In our case, the special knowledge is in the files we created in the last step.
Write the instructions about how to use the knowledge in your files, and upload the files. Ensure that you add instructions to refer exclusively to your knowledge which you have uploaded.
Switch off the following capabilities
Create this GPT and publish it. You probably have already created some for users, however in this case, publish it for yourselves.
Your AI Agent is ready to test and use.
Step 4 - Test and use
Scenario -
For some reason, a person who filled out a contact us form did not make it to sales because the person did not MQL. The sales team got to know about this during a reporting discussion, and is now asking about why a person who filled out the contact us form did not MQL? The usual way is to go through the flow charts, smart campaigns, brainstorm and troubleshoot. However, let’s try asking our AI Agent, aka GPT.
While this response is not bad, it’s also not everything.
It does give you a quick idea of the scope which might need investigation. If you have an intelligent agent like this created with more diligence than mine in this blogpost, you can spot issues rather pretty quickly. And the next step is about your own recommendations and considerations.
Step 5 - Develop a feedback loop
Consider the insights by agent, by including stakeholder feedback to improve the process. Let’s say you talk to sales in this case. The issue was that this person did not have some basic information like Country, Company Names etc which was required for the person to be marked MEL. In addition, being an MEL further is a prerequisite for the person to be marked MQL. Seek feedback from stakeholders and adjust. If there is a change recommended, make the change and maintain a change log.
The feedback loop will work continuously in harmony with educating stakeholders about the lead flow, and incorporating feedback into the system. This results in increasing the alignment you have with sales, in the context of Lifecycle Program. You can do this for other programs as well.
In many of my projects this was a common issue, however I started applying these iterations about a year ago. This has helped me to increase my time to respond to queries, and pinpoint areas which need introspection. This ultimately results in better conversations, and it has better results too.
Stay tuned for more Marketo insights next Tuesday as we continue to explore the intricacies of successful marketing operations.
Until next time, keep marketing smart, Marketing Nation!
Thank you very much for your time!
Your Tuesday Tech Bytes 2024 Team,
Balkar Singh Rao, Ajay Sarpal, Amit Jain, and Darshil Shah
Edit (August 6th, 2024): Here's next week's post for your ready reference:
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