I don’t know anyone who was ever great at sales forecasting. This is not to say that it can’t be done, but it’s a hard problem — like forecasting the weather. Today, we can get weather forecasts for many days in advance with temperatures accurate within a few degrees, because we’ve invested in many data gathering and analysis tools like satellites, computer modeling and analytics.
It’s not that weather forecasting has been perfected, though. Instead, forecasters have been able to bring their predictions down to a range of probabilities through the use of all that data, as well as models for how the oceans and atmosphere, the jet stream, and other major parts of the Earth work.
So, we typically see something like a 30 percent chance of rain and a temperature range of about 4 degrees, and those ranges are enough to enable us to make plans — like going to a picnic or not, or remembering to carry a hat or umbrella.
How’s the Weather?
Sales forecasting has none of this going for it at the moment. That’s partly because even though a sales forecast is a much smaller problem than the weather five days hence, we still do it much as we did a hundred years ago.
Too often that means a senior person reviews all the deals and follows a gut instinct, based on experience. By the way, that person is known affectionately as a “HiPPO” because the decision was the result of the Highest Paid Person’s Opinion. How’s that for science?
What would the forecasting process look like if we employed as much science as a local TV weatherperson? Like the weatherperson, the sales manager would have a model based on many iterations of patterns that account for all kinds of variables, including deal stages and their inputs, the sales rep’s track record, the product’s adoption patterns and much more.
If the interest is in total revenue intelligence, the model also would include inputs from the current customer base, including propensity to churn, cross-sell and upsell opportunities, and customer lifetime value.
There would be powerful analytics to process all the data, and the result would not be a single answer, such as a dollar number or the determination of whether or not you’ll make your target.
The output would be a set of ranges. Just like the weather forecast, you don’t know until the forecast is in the past — so there’s no such thing as 100 percent. Also like a weather forecast, you don’t need absolute certainty — you simply need to know the probability of rain on Saturday so you can figure out if you want to golf.
Analytics vs. Intuition
Right now, human behavior plays a big role in sales forecasting. We have a number to shoot for, and often we try to figure out ways to make it — regardless of the soundness of our reasoning. So, we assume deals are going to close even if it means we have to ignore telltale signs of trouble until it’s too late.
Imagine using modeling and analytics to evaluate your company’s position in each deal. The model could tell you the warning signs, because analytics would reveal how closely any deal fit with the model’s known history of success. As with weather forecasting, there would be no value judgment — just a probability of rain.
Managers would still need to apply their reasoning, but armed with this kind of knowledge, sales people up and down the organization could evaluate scenarios constructed to make their deals match up with the ideal.
Here’s a trivial example: A company I know has a sales process, which is a model itself. At a certain point, it has the rep meeting with a key decision maker. If the key decision maker is not on board, it’s clear the chances of getting the deal done are greatly reduced, so the meeting is an important gating factor.
Yet, time after time, reps skip this step. Perhaps the coach in the deal reassures the rep that the decision maker already has approved — or maybe the decision maker doesn’t have time to see the rep, or any of a hundred other reasons.
It might be hard for management to discover this if, for instance, the rep simply indicates on the forecast that the deal is at forecasting stage. A manager with five reps each working 50 deals in various stages might not have the time to go through every piece of deal data, so the mistake would get through but it wouldn’t improve the forecast one bit.
Cloudy With 30 Percent Chance of Sale
Now, take a more scientific approach. The sales process stage is one of the variables in the model that the company uses to evaluate deals. More importantly, the business applies analytics to the deal data rather than expecting managers to review all of it.
Analytics would have no trouble spotting the incomplete deal stage and would downgrade the forecast appropriately. A report then would show the variance between the forecast and the model.
If you apply this logic to every deal in the forecast, then you have a range of probabilities similar to those weather people rely on to tell you about Saturday’s conditions.
More importantly — and unlike a weather report — the forecast is also prescriptive, because it shows you how you can improve it. Get that meeting with the decision maker if you want to close the deal!
Mark Twain once said, “Everyone complains about the weather but no one does anything about it.” If you’re tired of complaints about sales forecast accuracy, consider building an accurate model and applying analytics. It works for the weather.