Unbiased Sales Forecasting using Machine Learning

Sales forecasting is a common task performed by sales organizations. Accurate forecasts allow organizations to make informed business decisions. It gives insight into how a company should manage its resources – people, time and cash. Companies derive forecasts based on historical sales, market conditions, competitive analysis and gut instinct. However gut instinct (or judgement) can introduce human bias leading to inaccurate forecasts.

SpringML provides a purely data driven predictive forecasting model. These models consume both historical data to gauge trends and seasonality as well as current pipeline of opportunities to then forecast sales for the next 12 months. This model runs automatically and presents a monthly forecast while constantly learning on new data that becomes available. These data driven forecasts can also be used to supplement judgement. We bring together both predictive and judgement forecasts into a single screen so that sales management has access to both these numbers to help them gauge which regions or territories are better at forecasting.

Since forecasts are data driven the solution allows users to also perform “What-If” analysis. This is a tool that allows sales leaders to determine impact of certain factors on sales numbers. This type of analysis helps them determine what types of levers they have access to and what impact, either positive or negative, they can have on the sales. This advanced What-If analysis is based on machine learning where the model gets executed every time a user interacts with the tool. Some of the variables used in this analysis are number of sales reps, average deal duration, average deal amount, percent win rate. For example a sales manager can see what happens if they increase recruiting or if determine impact of a discounting program they have been considering. This list of features is configurable and can include other factors that may be more meaningful to a company.

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