Sales has been called the world's second oldest profession. It's an inherently human endeavor that not only draws on the seller's skills but taps into a range of emotions as well -- from confidence, optimism and ambition to fear and greed.
Sales may be a game of hard numbers, but sales forecasting has long been prone to wide fluctuations because of these emotional factors. Whether it's an overzealous rep providing an unrealistic deal amount or another telling a white lie about an expected close date to please his boss, human foibles can skew the data. This can lead to end-of-quarter negative surprises that may clobber a company's stock, reputation and well-being.
Data science -- artificial intelligence, machine learning and predictive analytics -- has the power to strip out such mistakes from sales forecasting, in effect, a situation where computers can save people from themselves. More on that in a bit.
Conceptually, quarterly sales forecasting should be a straightforward exercise -- a company predicting, based on what's happening in the field, what the revenue will be so the firm can make informed business decisions, and allocate resources appropriately to improve the ability to meet targets.
Sales forecasts themselves are important only as a means to an end; unlike the way that weather forecasts tell you whether it will be a nice weekend for a trip to the beach, but no one has the ability to change the weather. In sales forecast, one has the ability to influence the result by taking various actions. The forecast provides the best possible path to meeting or exceeding the target, and hence needs to be highly reliable and actionable.
But sales forecasting can be clouded by a host of conditions that are more about the salespeople than the sales.
All of us are bound by Maslow's hierarchy of needs -- physiological (air, food, drink, shelter, warmth, sex, sleep); safety, which includes financial security; love and belonging, including within a work group; esteem (achievement, status, dominance, prestige, self-respect, respect from others) and self-actualization (self-fulfillment and personal growth).
With one of the highest-pressure jobs on the planet, salespeople are under uniquely intense stress to succeed in order to meet these needs. So they sometimes resort to behaviors that can throw off accurate sales estimates. The six most common ones are:
"Happy ears": It's easy to do in life -- you hear what you want to hear, not necessarily what the speaker was really saying. When a salesperson misreads a prospect's true intent, it can distort his or her assessment on the amount and close date of a deal.
Sandbagging: This is so prevalent in the sales world that the term is in the Urban Dictionary: "In corporate sales: when reps report a much lower deal opportunity than actually exists." Why would someone do this? It could be an under-promising/over-delivering technique to make the rep look like a hero. Another reason could be that if the deal doesn't go through, the rep doesn't look as bad. Still another could be to overachieve on a quota and receive a higher bonus. Whatever the reason, sandbagging is sales forecasting poison.
Fear of the boss: Everybody wants to make the boss happy, and is scared of the career consequences when the opposite occurs. So salespeople may exaggerate the status and amount of deals out of a sense of career self-preservation.
Peer pressure: No one likes to look bad in front of his or her peers. A salesperson may inflate estimates simply out of bravado or showmanship out of a sense of competition with other salespeople.
Guessing: Rather than admit he or she simply doesn't know how a deal will turn out, a salesperson takes a random stab. Unfortunately, the guesses then become part of the forecasting "official record."
Over-reaching: Sales reps are highly optimistic and bullish by nature. That's a good thing. But this quality can lead to over-optimism, where a salesperson unintentionally overstates a deal's prospects in the forecast.
Remember what I said earlier about data science's ability to save people from themselves? It's now possible to introduce a new level of reality into the imperfect sales forecasting process.
By using pattern-matching, machine learning and predictive algorithms, these technologies are remarkable in their ability to drill down into each deal's dynamics, providing a more reliable view and removing the errors that emotions can bring in.
Sales forecasting just can't afford to continue to fall into the old familiar traps when there are so many insights that can be surfaced from data.
K. V. Rao is founder and CEO of Aviso, producers of sales analytics software that helps sales teams optimize their performance and exceed revenue goals.
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