Practical RevOps Analytics Series: Can AI Make Your Sales Team’s Lead Distribution More Fair?

By Rob Kall, CEO & Co-Founder, Cien.ai

“When analyzing a sales team, I have found it helpful to use the analogy of a workshop that creates revenue. “

– Rob Kall, CEO & Co-Founder, Cien.ai

ESG and AI?

ESG stands for Environmental, Social & Governance, and is an increasingly important topic in corporate board rooms. Companies are now evaluated on their ESG conscientiousness.  We are reminded of Martin Luther King Jr.’s fundamental call for equal opportunity for all regardless of race, creed, etc. Today virtually every corporation in the US subscribes to his vision. Unfortunately, there still may be an unfair distribution of opportunities in many organizations.

At the same time, companies are now investing heavily in Artificial Intelligence (AI) to help predict and optimize business outcomes. AI is often cited as a source for biased decisions. It is true that if data used to train an AI is problematic, the result can be biased, but there is nothing inherent in AI that favors a specific group over another. In many cases the opposite can be true – AI can help promote greater fairness in your organization since it removes favoritism and “just doing things the way they have always been done…” Here is such an example:

Leads & Accounts – The “Raw Materials” in the Sales Process

When analyzing a sales team, I have found it helpful to use the analogy of a workshop that creates revenue. Leads are turned into useful things (opportunities and won deals) or are exhausted, ignored, and discarded. The artisans (the reps) in the workshop all have their unique skills and behaviors. Some create big, beautiful deals, and some are still at the apprentice level. But you cannot expect anything of value if they do not have access to the right raw materials. 

Good leads are the raw materials for new logo business, and the quality of existing accounts drives upselling revenue. If for example, you have an SDR team of 10 reps, you may track how many leads you give each of them, but do you also track the Value of those leads? Similarly, if you have 10 AMs, each with their assigned accounts, is each basket of accounts equivalent, or are some much more valuable?

How to Measure Lead & Account Fairness using AI?

If we agree that all leads are not created equal, let us start by determining, what the aggregate value is of the leads each rep has received. To do this, look at a specific period (I recommend looking at a minimum of a quarter, to smooth out the data), and count the number of new leads each rep in a particular role received. You may find a problem right there if there are incorrectly configured lead distribution rules. If the numbers are roughly the same, how do you know if the value distribution is fair?

Lead Value – A Practical Example

If you have some kind of AI lead scoring you may already have either a probability of conversion or a lead score such as A, B, C, etc.  If you have a score or a percentage, simply check the average conversion ratio of each category (here is an article) that helps you avoid common pitfalls when doing that).  You want to create a table that looks something like this:

Then tag each lead record with the appropriate expected probability (this also allows you to check the quality of your lead scoring). Then sum the average. Expected conversion probabilities and summarize by rep using a pivot. Now you see the real difference in lead value received by each rep.

To measure the value of existing accounts, you can do a similar analysis, using a linear regression of expected sales based on a set of factors such as industry, size, prior purchases, etc,  (An article on how to do this will be published shortly) or you can manually put in an “expected 2022 revenue” value for each account.

Why Does This Matter?

What we have found is that in almost all cases, the top producers receive significantly more value than their peers. This may be justified by their superior skills, but what about the reps that have the potential to also exceed their quota but are currently being short-changed?

In 2022, most companies struggle with The Great Resignation, i.e. retaining key employees who perform critical tasks. Study after study shows that employees care deeply about working for a company that they consider fair and “doing the right thing” for their employees, customers, and society in general (e.g. in this Mercer study). Today the situation has somewhat reversed. 

At the same time, every sales team needs to build a culture of personal responsibility where reps take ownership of their results. Nothing supports that better, than letting existing and prospective employees know that you are constantly monitoring the value and fairness of leads, accounts, and territory assignments and that as a committed member of the sales team, each rep can expect a fair deal from their corporate leadership.

About the Cien.ai Practical RevOps Analytics Series

This article is part of our Practical RevOps Analytics Series, inspired by our work with B2B business leaders, growth consultants, and PE operating partners. These articles focus on the technical aspects of improving GTM performance. If you want to dig in on the business details of how to improve the concepts we use here, please refer to our “Growth Essentials Analytics Series”.