Sales forecasting is a common task performed by sales organizations providing accurate forecasts allowing organizations to make informed business decisions
Companies derive forecasts based on historical sales, current market conditions, and gut instinct – however gut instinct can introduce human bias leading to inaccurate forecasts
A data science powered sales forecasting solution provides the following improvements over the traditional approach:
- Discover all the underlying factors that influence an opportunity’s likelihood of success. Only a data science approach can uncover and accurately evaluate the complete set of factors driving deal flow.
- Sense and report actionable intelligence to keep deals in the best possible condition as they travel through the sales pipeline
- Discover and report any pattern changes to deal attributes such as velocity, amount, region, product and industry that reflect a change in business conditions or environment.
- Provide context and full rationale for each factor’s impact on pipeline flow.
- Deliver the results to all levels of the organization (regions, business units, channels)
- Do all of the above in a scalable, automated, reliable and real-time manner.
With access to this data-science driven report, a salesperson or manager can:
- Identify deals that are in need of effort and gauge the response to that effort.
- Assign resources more objectively and efficiently.
- Size the sales force to the size of the opportunity set
- Rationalize compensation in all forms
- Compare sales strategy effectiveness in more concrete terms, generating more actionable analysis.
Using predictive analytics in sales forecasting
Predictive analytics is the application of advanced statistical and machine learning techniques to extract patterns from salesforce activity and third party data sources (industry specific and cross-industry) and make them consumable by the sales organization to improve sales effectiveness. The three steps to creating the right solution are:
- Create an integrated data store that merges data from different data sources:
a. CRM data, including historical sales activity
b. Financial data, including product margins
c. External data including industry-specific data sources and cross-industry data sources
- Run factor analysis discovery methods on the data sets to objectively and comprehensively uncover factors driving deal flow through the sales pipeline.
- Using machine learning techniques to model how these factors contribute to sales outcomes, in a time-sensitive context, results in our ability to make time-forward predictions.
- Clearly present the predictive results emphasizing actionable recommendations, provide supporting context and full rationale to support merging of understanding between machine and salesperson.
- Override ability to take sales force input to improve projections/forecasts.
Sales teams have detailed and practical knowledge of their customers and prospects that is often not captured in the enterprise information systems.Giving the sales team the ability to audit and override the results of a data-driven model and share the improved projections with others is critical to drive the best possible decisions