Sales Forecasting: Analytics to the Rescue

Sales teams have long deployed CRM and SFA tools to answer questions about how much has been sold, what deals are pending, and when they're expected to close. While an increase in sales volume can impact the future in terms of top-line performance, how can the forecast itself be utilized to drive better profit margins?

Sales teams have long deployed CRM and SFA (sales force automation) solutions to support a variety of chronologically ordered questions within the organization: the past (what did the customer purchase?), present (what is in our pipeline?) and future (when is the deal likely to close, for how many dollars, and at what probability to "seal the deal?"). While an increase in sales volume can impact the future in terms of top-line performance, how can the forecast itself be utilized to drive better profit margins?

According to July 2008 Aberdeen research, "Sales Analytics: Hitting the Forecast Bulls-Eye," 87 percent of Best-in-Class companies held regular sales forecast reviews for sales reps and managers, and 81 percent of them created the opportunity for cross-functional views of the forecast within their enterprise. Laggards deployed only 68 percent and 64 percent of these capabilities, respectively, and furthermore were 49 percent less likely to include formal definitions of progressive sales stages, used to weight their forecasts.

More recent research for "Inside Sales Enablement: 'Let Them Drink Coffee!' (December 2009) shows that use of commercial sales analytics solutions remains dramatically different between the Best-in-Class, at 76 percent, and Laggards, 40 percent of whom include the technology in their sales support efforts.

Limitations on internal visibility into predictive business results are compounded by the changing dynamics of many business environments, and thus affect both forecasting accuracy and, ultimately, an organization's actual revenue flow. As a result, flawed source data affects decisions on how sales execution will occur and then, in turn, the level and type of resources that will be applied to sales situations based on past successes / failures, sales cycle timeframes, and close ratios.

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