A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach

Predicting sales opportunities outcome is a core to successful business management and revenue forecasting. Conventionally, this prediction has relied mostly on subjective human evaluations in the process of business to business (B2B) sales decision making. Here, we proposed a practical Machine Learning (ML) workflow to empower B2B sales outcome (win/lose) prediction within a cloud-based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: 1) an ML pipeline that trains probabilistic predictive models in parallel on the closed sales opportunities data enhanced with an extensive feature engineering procedure for automated selection and parameterization of an optimal ML model and 2) a Prediction pipeline that uses the optimal ML model to estimate the likelihood of winning new sales opportunities as well as predicting their outcome using optimized decision boundaries. The performance of the proposed workflow was evaluated on a real sales dataset of a B2B consulting firm.