Magazine article CRM Magazine

Manage Forecasts with Metrics, Not Hunches: Look to Big Data, Sales Analytics for Accurate Insights

Magazine article CRM Magazine

Manage Forecasts with Metrics, Not Hunches: Look to Big Data, Sales Analytics for Accurate Insights

Article excerpt

AS PART OF CSO Insights' 2013 Sales Management Optimization (SMO) study, we asked more than 1,700 firms to identify the ultimate outcome of their forecast deals. As you can see from the chart on this page, the average win rate came in at a very lackluster 44.7 percent.

The fact that more than half of the deals sales teams confidently add to the forecast end up as losses or no decisions is causing Maalox moments not just for chief sales officers, but for chief financial officers and chief executive officers as well. How can you manage your business when you have such poor visibility into what customers will be buying?

A clue to why many companies have major issues regarding forecast accuracy surfaced in the 2013 SMO data. The most common tools sales managers are using to manage their forecasts are spreadsheets and their core CRM systems, neither of which offer the analytics needed to generate metrics that sales managers need to effectively manage their teams.

To deal with this challenge, more firms are turning to two types of CRM 2.0 applications. The first, which is helping sales management make better choices at the beginning of the sales process, is big data platforms. Solution providers such as Birst, Lattice Engines, PROS, and Vendavo can pull data from a variety of internal, and in some cases external, systems.

They analyze the information, looking for key metrics that help predict the profile of a high-quality opportunity. Big data analysis may find that there are certain industries you are more effective at selling to than others, that you relate better to certain types of stakeholders, that you solve certain problems more effectively than others, etc.

Based on these insights, marketing can do a more effective job of interest generation by developing campaigns that target prospects that fit the profile. They can also more precisely score leads as they come in to determine if they are "sales ready." In addition, once the leads are passed on to sales and qualified, sales managers can more effectively coach their salespeople on if and how to pursue the opportunity based on the big data analysis.

What is often seen in benchmarking these types of sales effectiveness initiatives is that there is an initial drop in the size of the pipeline. …

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