Sales pipeline win propensity prediction: a regression approach

TL;DR

This paper introduces a machine learning framework for predicting sales win propensity, addressing data noise and market volatility, demonstrated on real B2B enterprise data.

Contribution

It presents a novel unified regression-based approach for sales win propensity prediction tailored for B2B markets with noisy and limited data.

Findings

Effective prediction accuracy demonstrated on real enterprise data

Addresses challenges of noisy and small datasets in B2B sales

Provides a scalable framework for sales pipeline management

Abstract

Sales pipeline analysis is fundamental to proactive management of an enterprize's sales pipeline and critical for business success. In particular, win propensity prediction, which involves quantitatively estimating the likelihood that on-going sales opportunities will be won within a specified time window, is a fundamental building block for sales management and lays the foundation for many applications such as resource optimization and sales gap analysis. With the proliferation of big data, the use of data-driven predictive models as a means to drive better sales performance is increasingly widespread, both in business-to-client (B2C) and business-to-business (B2B) markets. However, the relatively small number of B2B transactions (compared with the volume of B2C transactions), noisy data, and the fast-changing market environment pose challenges to effective predictive modeling. This paper proposes a machine learning-based unified framework for sales opportunity win propensity prediction, aimed at addressing these challenges. We demonstrate the efficacy of our proposed system using data from a top-500 enterprize in the business-to-business market.