Lately, we’ve been getting a lot of questions on sales calls about pipeline and pipeline coverage and how we deal with that.
Each concept has some flaws which is why we kept avoiding the topic as of late. But at the same time, it also was clear to us that they can be super useful. So here are our thoughts.
But first, what do we mean by Pipeline and Pipeline Coverage (PLC)?
Pipeline is measured by #deals * expected deal value with closing date this year or this Q (depending on what timeframe you look at)
PLC puts that number in relation to the target you have. E.g. 5M in Pipeline with 1M in target gives you a “coverage” of 5 to 1. So this is a 5x PLC.
So, what are the issues?
More and more top-funnel teams are being measured by “pipeline created”. That’s, generally speaking, a good thing. But in a data-driven world, this comes with a few issues.
- Large chunks of the pipeline target can be hit by 1 big (outlier) inbound. Think about Coca-Cola inbounding. Marketing then is done with the Q.
- Larger inbounds tend to take longer to close and might have worse conversion rates than your “bread & butter” deals. While larger deals account for more pipeline.
And then, once pipeline is created, AEs are being asked to maintain a “5x PLC”. Meaning their quarterly target needs to be “covered” by a 5x of Pipeline. Issues with this:
- Many people are just blindly copying the 5x here. If you have a pipeline conversion rate of 20% then 5x might be the right number. If you have a 10% pipeline CVR then you need 10x.
- Pipeline per Q tends to fluctuate in funny ways. (a) it suddenly increases as the previous Q ends (push-overs), (b) it increases with each deal closed, and (c) it sharply drops with the new Q approaching (pushing into new Q).
- This can easily be gamed by the reps, basically keeping the expected closing date in this quarter to pretend to have a sufficient PLC. Once that fails, all pipeline moves over to next Q – again leading to a great PLC.
So how do we look at this in a data-driven way?
Above we plotted how pipeline for each Q usually looks like. Each curve is measured by [#deals * expected deal value with expected closing date in that Q].
You can see how pipeline for a specific quarter builds up and then drops off. You can luckily see that in this example the curves are increasing, corresponding with higher targets.
Looking at this, when exactly are you supposed to measure that “5x PLC”? At the peak? At Q-start? 45 days into the Q? Again, a hard-to-use metric.
Instead of just summing up the pipeline and creating some PLC number, we think about solving it a bit differently.
- We project your revenue based on your revenue engine and your future additions (hires, projects, campaigns etc.)
- We take your last few quarters and create an average pipeline curve plot – corresponding to the revenue target you then actually hit
- Then we plot that curve going forward scaling it up & down with the projected revenue
- You can now track your actual pipeline to the actual daily/weekly pipeline goal according to your revenue target. If you are below: alarm bells – you can take action asap.
This is currently in early testing with SQL and spreadsheets on our side. Let me know if you think this is valuable. Early feedback would be great and will accelerate this onto the roadmap.