Many corporate sales and marketing organizations rely extensively upon Customer Relationship Management (CRM) applications to manage customer and sales data. These applications are excellent sales operations tools, but have significant limitations when used for sales forecasting and analysis.
First of all, while these applications can provide detailed insight into opportunities over the next few quarters, they often deliver limited visibility into prospective sales further out. For the longer view, companies need to plan top-down, using high-level assumptions or drivers, including ways to incorporate top-level management judgments. What’s required is a combination of bottom-up and top-down modeling.
Furthermore, CRM systems do a strong job of providing up-to-the-minute views of the pipeline, but they offer limited ways to create snapshots of data at different points in time, for analysis of changes over time. Yet insight into what has changed is critical to understanding the underlying dynamics of the pipeline, and to creating accurate forecasts.
As a consequence of these shortcomings, sales and finance teams typically export CRM pipeline data to Excel, where they can make adjustments before committing to final forecast numbers. But this introduces a new set of challenges. Spreadsheet-based systems are slow, inaccurate, and non-collaborative, and don’t lend themselves well to version comparison. The result is poor forecast accuracy, and missed plans.