The 3 Types of Analytics To Use In Your Sales Forecast

Sophie Grais

Sophie Grais

The 3 Types of Analytics To Use In Your Sales Forecast

What would it take for you and your team to make better decisions — and call a more accurate forecast?

At EXCEED, Sales Ops leaders considered this question together in breakout sessions on forecasting and the use of data to inform a sales forecast. EXCEED focused not only on the Sales Ops vocation, but also on new technology and innovation within the field. In “The Future of Forecasting” breakout session, Sales Ops leaders discussed the use of analytics in their team forecasts — and how analytics can improve forecast accuracy and team efficiency.

Sales teams are gold mines of deal data, and analytics enable organizations to leverage that data toward better, smarter decisions. Analytics can help your sales team answer the tough questions on deals past, present, and future: what happened, why did it happen, and what might happen next. Sales analytics can fall into any of the three categories below:  

Descriptive Analytics

  • What happened?
  • Where did it happen?
  • When did it happen?

Diagnostic Analytics

  • Why did it happen?

Predictive Analytics

  • What is likely to happen — and exactly how likely?

All three of these categories foster a better understanding of your business to enable better outcomes in the future. But good data is a prerequisite for good insight: in the breakout session, our customers discussed the challenges of data quality and how they began to get to predictive. The VP of Sales Ops at a cloud storage company explained that his team began by scoring their customers, starting with the profile of successful past customers. From there, they could begin to recognize patterns and spot key indicators of various deal outcomes far in advance. Documenting reasons for wins and losses added an extra layer of helpful detail.

The VP of Sales Ops at a clean energy company agreed. “Which factors might have an impact on predictability, especially when it comes to deal scoring?” he explained. He suggested isolating areas in the sales cycle that could stretch longer than their typically allotted time;  for example, larger contract negotiations and redlines may take months to complete.

Starting with descriptive and diagnostic analytics to understand the wins and losses of yesterday will inform your team’s understanding of the present, and ultimately bring you to the end goal: a predictive understanding of tomorrow. Analytics can convert data into insight, but insight alone isn’t enough. The biggest challenge lies in converting insight into automated, consistent action. To continue the conversation on Sales Ops technology and the use of analytics in sales, join the EXCEED LinkedIn community.

Rev Up Your Forecast with AI

The ugly truth is that most forecasts fail. But it doesn't have to be that way. Learn how leading B2B companies are leveraging AI to predict business outcomes more accurately.

Subscribe to our blog

By submitting this form, you agree to the Clari Privacy Policies and Clari GDPR Agreement