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Moneyball for B2B sales, part 2

Updated: Aug 31, 2023

What else can your forecasts teach you? How to stack the game in your favor.

In part one of this series, we pointed out there are several uses for a forecast—beyond telling you what to expect. If you are only focused on forecasting results, then you are missing out on the power of mining your CRM data. In this post, we'll look at some of the other uses.

Forecast scores on individual opportunities can help you separate deals you need to work on from lost causes; and highlight areas where your CRM has conflicting information. For example, an opportunity in the qualification stage, with a closing date in the quarter, in the omitted forecast category, and with a salesperson assigned probability of 90%. These are mixed messages to anyone. A good forecasting application will flag these conflicts for you, prompting you to investigate. These conflicting signals should be reconciled before deciding to allocate resources or to ignore the opportunities.

That said, deal scores are one of the least interesting ways to use CRM analytics. All deal scores are wrong. The outcome is binary. No one wins a fractional deal. The real practical use of deal scores is to produce an aggregate forecast for a portfolio of opportunities.

CRM analytics can help you optimize your sales process sales by highlighting weaknesses. For example, large gaps in your win rates from one stage to the next indicate the need for intermediate sales stages that tell your team how to advance your cause. Redundant stages may give the illusion of progress.

Your data can also help you sell more and optimize your resources by highlighting exploitable inefficiencies in your processes. For instance, you can identify certain business segments (like industry, customer size, product, and geography) that are considerably more profitable than others; and you can use this information to focus opportunity development in those areas.

Summarizing. When assessing how good a forecast is, first you need to define your objective. Do you want to:

  • Forecast what to expect?

  • Optimize your sales processes?

  • Optimize business resources allocation?

Assuming you just want to focus on knowing what to expect, then assessing the quality of a forecast requires you to define:

  • What you are forecasting

  • What period you are forecasting

  • What your next best alternative is

Let’s say you are trying to forecast sales for new business closing within the current quarter. Your next best alternative is to use the probabilities that your sales stages or your salespeople assign to opportunities. And your objective is simply to get know what to expect—how much you will sell in the quarter. Most CRMs conveniently produce an “expected revenue” (or the “weighted amount”) for each opportunity that is simply the product of the assigned probability and the applicable amount field. There are plenty of great articles on the web about how you should sum these expected revenue figures to arrive at a forecast.

That’s a start. If your results are like the hundreds of others we have seen, you’ll probably find that those figures are biased to somewhere between highly optimistic and insanely optimistic; and inconsistently so (meaning you can’t just discount the figures by a factor to correct for the bias). But all is not lost, you can improve on probability-weighted forecasts with some smart filtering of your opportunities to eliminate the obvious opportunities that are no longer in play.

We find that Funnelcast forecasts consistently beat these weighted (and properly filtered) forecasts and offers insights into sales optimization that are not available from opportunity probabilities. Compared to sales management, we find that Funnelcast consistently beats their forecasts as well; except when the sales team may have not yet updated the CRM with the latest information, or when there are a small number of very large deals that dominate the outcome.

If you are interested to learn about how Funnelcast compares to probability-weighted forecasts—and to learn how to make your probability-weighted forecasts work better, check out our next post on how to make the most of your CRM probabilities and Funnelcast probabilities.


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