A Discrete Constraint-Based Method for Pipeline Build-Up Aware Services Sales Forecasting.

Services organizations maintain a pipeline of sales opportunities with different maturity level (belonging to progressive sales stages), lifespan (time to close) and contract values at any time point. As time goes, some opportunities close (contract signed, or lost) and new opportunities are added to the pipeline. Accurate forecasting of contract signing by the end of a time period (e.g., quarterly) is highly desirable to make appropriate sales activity management with respect to the projected revenue. While the problem of sales forecasting has been investigated in general, two specific aspects of sales engagement for services organizations, which entail additional complexity, have not been thoroughly investigated: (i) capturing the growth trend of current pipeline, and (ii) incorporating current pipeline build-up in updating the prediction model. We formulate these two issues as a dynamic curve-fitting problem in which we build a sales forecasting model by balancing the effect of current pipeline data and the model trained based on historical data. There are two challenges in doing so, (i) how to mathematically define such a balance and (ii) how to dynamically update the balance as more new data become available. To address these two issues, we propose a novel discrete-constraint method (DCM). It achieves the balance via fixing the value of certain model parameters and applying a leave-one-out algorithm to determine an optimal free parameter number. By conducting experiments on real business data, we demonstrate the superiority of DCM in sales pipeline forecasting.