Across the tech industry, on either Mondays or Fridays, sales executives are typically consumed with lengthy forecasting calls. Just about every sales leader spends too many hours doing top-down forecast calls, and along with his or her sales operations partner, plugs notes and numbers into their forecast spreadsheets.
After hours of phone conversations listening to the status of deals and updating their spreadsheets, sales leaders have to present to the executive team the updated forecast and discuss the most critical deals for the quarter.
While senior sales leaders have great deal instincts, can quickly sniff out b.s. from their AE, and demand that their team “net out” their deal status talk track, the current forecasting process is extremely inefficient, and inadvertently perpetuates “fake news” cycles.
Most reps are integrating their version of the truth with the facts of the deals. The problem is, their version of the truth is often not completely accurate. According to CSO Insights, 48% of sales people surveyed reported that they were accurate only half of the time when predicting deal amounts and close date time frames. This means that human judgment alone is no more accurate than simply tossing a coin.
Every sales cycle is a culmination of hundreds of events, data elements, activities, etc. While we summarize our selling processes into stages, it is hundreds of variables that come together to determine a deal’s fate. If you rely on spreadsheets, experience and human-based intuition, you’ll miss critical trends of similar deals, and not see a true picture of the forecast or the pipeline. This results in inaccurate forecasts and projections to the leadership team, misguided expectations, and a lot of invested time that could be better spent discussing deal strategy.
The solution lies in applying AI and machine learning to current and historical CRM data. In fact, in a recent study conducted by Harvard Business Review, more than 60% of sales executives surveyed reported that their future success depends on successfully adopting AI.
Of course, there are always objections. Sales leaders have often asked the value of applying machine learning models to “dirty” or inaccurate data. The reality is that AI helps sales teams clean up their data and guides them to improve their accuracy over time.
Case in point, we have a client that was nervous about the data their sales team was putting in to their CRM, fearing that sales people were unknowingly entering “fake news” about their deals. Here’s what happened: our models identified where there were likely inaccuracies, provided suggested adjustments and projected that they would close 33 more deals than expected. At the end of the quarter, they indeed closed 33 more deals.Trying to make sense of all the data you are managing in spreadsheets and the “fake news” being inadvertently reported by the sales team will kill the career of any sales executive. Leveraging AI-powered predictions to guide human judgment enables more accurate forecasts, improves commit accuracy and enables the delivery of reliable forecasts to the executive team.
There’s enough fake news out there. Let AI be the game changer that provides guidance and keeps your team honest and on track.