Pipeline Management Series (Part 1): Four Ways to Excel in Data-Driven Pipeline Management

Pipeline Management Series (Part 1): Four Ways to Excel in Data-Driven Pipeline Management

pipeline management

Sales, in essence, isn’t much different from a production machine: Input – Throughput – Output. If you want to grow output (and you wouldn’t be reading this blog post if you don’t) you have two levers: add more input or increase the throughput.

The input to your sales machine is the pipeline and pipeline management is the optimization of the conversion of the opportunities in the pipeline.

So with every action you should ask yourself three questions:

  1. Should I focus on increasing the pipeline (input)?
  2. Should I focus on improving the conversion rate (throughput)?
  3. How does one affect the other (interdependence, feedback loop)?

In this article, I will share four ways beyond the standard, run-of-the-mill analysis that will help you take your pipeline management to the next level.

1. Track Stage-By-Stage Conversion Rates Along Your Sales Process

In our last post, we already established the vital importance of having a standardized sales process in place that consists of a known set of sales stages. Without it you might as well be throwing darts at a board when it comes to projecting sales and managing your pipeline. This may sound like common sense – but believe me, it’s not. Your sales stages should mirror the buyers’ journey, for example:

Equally important is to define a hard set of criteria that states when an opportunity can be moved to another stage. Did the prospect express a clear interest in the solution and is there available budget? If not, this is still a lead and not yet a qualified sales opportunity.

Once you have established a sales process with stages, it’s critical to measure the conversion rate from each stage to the next. Many people only look at their overall conversion rate, i.e. the percentage of total opportunities that are eventually converted to wins. While this is an incredibly important metric, it lacks granularity and does not reveal where along the sales process you may have a problem. Therefore, track the conversion rates stage-by-stage:

The following visualization shows this in action.

This is what differentiates great from merely good pipeline management. It’s an important topic and I will discuss this in-depth in the next blog post – stay tuned!

2. Project Additional Pipeline and Number of Opportunities Needed

All organizations have one way or another to forecast sales. Some just multiply the open pipeline in the CRM by some probability (Hint: not a very accurate method, learn more in our previous article) others use a more sophisticated approach such as the forecast workflow in Vortini. But the bottom line is that we will end up with a difference between what we project at the end of the period and the target.

That is already great information to have, yet you can do better!

Instead of just knowing the projected output and its variance with regard to the target, it would be much more useful to know how much additional input (pipeline) we need in order to close the gap.

This can be achieved by reverse-engineering our sales machine. Given that we know our historical conversion rate we can calculate the additional pipeline needed:

But we can take this one more step further and estimate the number of new opportunities that would be needed:

So, instead of just knowing how large the output gap is, we now have an actual number of opportunities that we need to eliminate that gap. This is much easier to communicate to sales reps and management and provides a clear path to action. A caveat being that projections are always an imperfect science, Vortini has found that conversion rate and median deal sizes are usually stable over time for most businesses and thus provides an excellent proxy for estimates.

*The Median, which is the middle number of a data set, is often more representative of a typical deal than the average, since this can be skewed by few very large/small deals.

Ingo is a Solution Architect at Vortini. He has been designing and implementing sales analytics solutions for more than ten years.


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