The 8 Ton Elephant in the Room: Poor Pipeline/Forecast Visibility

Over the past year, I have often sounded like a broken record, pointing out repeatedly that while tools like CPQ sales analytics, and AI are readily available, they are underutilized. This is a mistake, as these systems do not just drive revenue, they make business more predictable as well. A recent event has compelled me to once again beat that drum of awareness. I was recently poring over CSO Insights historical sales performance for the past decade, looking for trends on what has changed over time. I came across a key metric that showed essentially “no” change, shown in the following chart. This should be disturbing not only for sales management, but also executive management.

In 2007, we failed to close the deals we forecasted more than half of the time. Fast-forward to today, and the problem persists. So, what are the implications of poor forecast management on the rest of the enterprise? If you are a CFO, how do you maximize the use of credit lines if you have minimal confidence in the revenue forecast? If you are a top HR executive, how do you optimize staffing? If you are in charge of determining what quantities of which products to produce and when, how do you make the right choices with such bad data? This goes on and on and touches every functional area in a company.

The pain point is clear to all executives, and so is the root cause: salespeople and sales management have poor visibility into the overall pipeline and shorter-term revenue forecast, and are therefore making imprecise decisions on the real status of opportunities. We all too often lament that we live in an imprecise world, and act as if that was just the way life is in sales, when in fact it does not have to be.

We live in a much more precise world compared to a decade ago. Over the past ten years there have been major advances in technology designed to bring more science to the art of sales. CPQ technology has been optimizing the way we create solutions to meet customer needs. Quote-to-cash systems allow you to track the status of deals as they move through (or stall out) in the customer approval process. Sales management analytics, and more recent AI solution advances, have created ways to get real time insights into which opportunities are likely to actually close, and which ones are not. The solutions exist to take on the problem of poor pipeline/forecast management, yet far too many companies are not using them.

Why? Lack of time, resources, budget; we all know the excuses. Well, take five minutes and do an exercise: Take your win rate of forecast deals today and add ten percentage points to it, and at the same time reduce your competitive loss and no-decision rates by five percentage points. Now estimate the impact this would have on revenues, and also think about the implications for the rest of the enterprise as well if they could gain more confidence in the forecast.

Take this up a level and do a little pen to paper analysis and think about the impact this improvement would have on shareholder value. Does that exercise start to create a business case for finding time, resources, budget, etc.? If the answer is “yes” then do so, otherwise we will be talking about this again in another ten years when we absolutely do not have to.

Questions to Ask:

  • How would you assess the ability of your sales organization to accurately forecast business?
  • What might the ROI be to the entire company if all functional areas of the firm had a higher confidence in the sales forecast?
  • Have you taken the time recently to truly understand what technology choices are available to your firm to help your sales teams close the deals they forecast?


Additional Resources:


1 Comment
  • Victor Antonio
    Posted at 07:30h, 19 June

    Good summary and suggestions! As you know, one key factor is to get the frontline salespeople to be as realistic as possible (i.e., keep their happy ears in check). 🙂 VA

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