An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline

摘要

Services organization manage a pipeline of sales opportunities with variable enterprise sales engagement lifespan, maturity levels (belonging to progressive sales stages), and contract values at any given point in time. Accurate forecasting of contract signings by the end of a time period (e.g., a quarter) is a desire for many services organizations in order to get an accurate projection of incoming revenues, and to provide support for delivery planning, resource allocation, budgeting, and effective sales opportunity management. While the problem of sales forecasting has been investigated in its generic context, sales forecasting for services organizations entails the consideration of additional complexities, which has not been thoroughly investigated: (i) considering opportunities in multi-staged sales pipeline, which means providing stage-specific treatment of sales opportunities in each group, and (ii) using the information of the current pipeline build-up, as well as the projection of the pipeline growth over the remaining time period before the end of the target time period in order to make predictions. In this paper, we formulate this problem, considering the service-specific context, as a machine learning problem over the set of historical services sales data. We introduce a novel optimization approach for finding the optimized weights of a sales forecasting function. The objective value of our optimization model minimizes the average error rates for predicting sales based on two factors of conversion rates and growth factors for any given point in time in a sales period over historical data. Our model also optimally determines the number of historical periods that should be used in the machine learning framework to predict the future revenue. We have evaluated the presented method, and the results demonstrate superior performance (in terms of absolute and relative errors) compared to a baseline state of the art method.