Predicting Loss Risks for B2B Tendering Processes

TL;DR

This paper introduces a multi-class classification model for B2B tendering that predicts win probability and specific loss reasons, providing more detailed insights than traditional binary models, with high accuracy and AUC scores.

Contribution

It presents a novel multi-class approach to predict not only win likelihood but also specific loss reasons, enhancing decision-making in B2B sales processes.

Findings

Achieved 85% accuracy in loss prediction

Attained an average AUC score of 0.94

Improved model performance after class imbalance handling

Abstract

Sellers and executives who maintain a bidding pipeline of sales engagements with multiple clients for many opportunities significantly benefit from data-driven insight into the health of each of their bids. There are many predictive models that offer likelihood insights and win prediction modeling for these opportunities. Currently, these win prediction models are in the form of binary classification and only make a prediction for the likelihood of a win or loss. The binary formulation is unable to offer any insight as to why a particular deal might be predicted as a loss. This paper offers a multi-class classification model to predict win probability, with the three loss classes offering specific reasons as to why a loss is predicted, including no bid, customer did not pursue, and lost to competition. These classes offer an indicator of how that opportunity might be handled given the nature of the prediction. Besides offering baseline results on the multi-class classification, this paper also offers results on the model after class imbalance handling, with the results achieving a high accuracy of 85% and an average AUC score of 0.94.