Predictive Sales Analytics vs. Sales Data Analysis

A few days ago we had an interesting conversation with one of our prospects. This company had a strong growth in 2009 and for 2010 was looking to deploy sales analytics as a competitive advantage. This company had developed an evaluation criteria and one of their key requirements was the ability to support 'drill to detail and ad-hoc report creation'.

As we began to explore use cases, we learned that this requirement came from the need to identify and analyze the differences between deals won and lost. This is a common and important analysis but the key is how to achieve it most accurately and most effectively.

This company had an already stretched operations group and although they were looking forward to better tools they did not have a lot of extra time to do data analysis nor did they have room for additional headcount. The head of sales and regional managers were interested in the results but their focus was in the sales operations and they had no interest in performing the role of data analysts either.

This is an important difference between traditional reporting or business intelligence tools and predictive analytics. Predictive analytics leverages data mining techniques that can perform millions of sophisticated computations and automatically discover data patterns. These capabilities far outperform even the best analysts both in accuracy and cost.

For our prospect, this capability came as a surprise. Sales analytics have evolved significantly over the past couple of years and although predictive analytics have been widely used by marketeers for quite some time, it was not until recently that predictive analytics began to make inroads in sales applications.

We are excited to help our prospect increase revenue with predictive analytics and become another cloud analytics success story.