Cien.ai Growth Essentials Series:  Pipeline Quality vs. Pipeline Management.

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

What’s the difference between bad pipeline quality and bad pipeline management?

These terms can be confusing, but our definition is that the quality of the pipeline is measured when an opportunity is created. If it is very unlikely to be won, then it is of low quality. Pipeline management is something you measure while working on the deals. A pipeline with a lot of “zombie deals” is poorly managed. I.e., deals that clearly will not be won but remain in the pipeline creating the appearance of progress while wasting rep resources. 

How do you know if you have any of these problems?

First the gut check: Does it feel like you have either of these problems? Usually, the answer is a “tentative” yes…

If so, apply an AI propensity model on the deals as they are created (not after the reps have already worked them for a while), and get a win probability. Make sure the model is properly calibrated (this can be measured and should be transparent to the users) so that on historical data the predicted and actual win rates are similar. For existing customer deals, the probabilities are often fairly high, especially for renewal opportunities, but if you find that for new logo deals (the first purchase by a new account) your average win rate is below 20% or even as low as 10%, then you have a problem. If your hunter reps have to work 10 new logo deals to win one, that is a lot of selling without much to show for it…

Pipeline management problems can sometimes be easy to spot. If you compare sales cycles for won vs. lost deals and the lost cycles are longer, that is a telltale sign of poor pipeline hygiene. Other red flags are things like a lot of skipped stages on won deals. If you have access to time spent per deal data, looking at the effort reps put in on deals that stall and have low prospect engagement can give you additional signals that lost deals are closed too late.

One of our Management Consultant partners recently said the following after working on a publicly traded SaaS giant:

“Sales had been king for so long, that nothing was even questioned. But upon closer inspection, they had serious problems with both Pipeline Quality & Pipeline Management. That was part of the reason for their recent $10B loss in Market Cap.”

What does success look like? 

Good pipeline quality is when new logo deals have an average expected win rate of 20% or better so that reps are not chasing “unwinnable” deals. You accomplish that through a combination of looking at the “static factors” that make certain deals better than others; like industry, size of the business, location, and size of deal, etc.  You should also look at the buyer and rep behavior before becoming a pipeline ( SQL) deal. E.g., how much time did an SDR spend with the prospect? Did they have meetings, or just emails, etc? With a clear list of positive and negative factors and a way to measure pipeline quality continuously it is much easier to transform the GTM team. 

The traditional way to keep the pipeline accurate has been to do weekly pipeline review meetings. Those take time and often it’s hard to cover every deal due to reps not attending or running out of time. Getting a continuous pipeline health report to measure and flagging “dying deals” automatically is a way to minimize that manual effort and get a healthier pipeline. 

About the Cien.ai Growth Essentials Series 

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

Contact
Rob Kall, Cien.ai, media@cien.ai, www.cien.ai

SOURCE Cien.ai