Improving Sales Forecasting: Leveraging Unstructured CRM Activity Logs, Large Language Models, and Generative AI

49 Pages Posted: 3 Jun 2024

See all articles by James C. Reeder, III

James C. Reeder, III

University of Kansas - School of Business

Nawar Chaker

Louisiana State University, Baton Rouge - E.J. Ourso College of Business Administration

Johannes Habel

University of Houston

Date Written: May 30, 2024

Abstract

Our study examines whether unstructured data in customer relationship management (CRM) software can enhance sales forecasting. While unstructured CRM data provides useful information for managers to review salesperson performance, it is unclear whether and when such data can predict changes in sales revenue with customers. By leveraging advances in machine learning, we seek an answer to this question by combining Generative AI (GenAI) with large language model fine-tuning. Specifically, we construct a measure of positive sales change by scoring over 180,000 sales activity logs associated with 11,201 customers served by a medical device manufacturer. We find that our constructed measure predicts a statistically significant growth in sales revenue; the effect remains stable through a battery of different specifications and robustness tests. We test a series of moderators of this effect by using variables grounded in information processing theory. For example, our measure is only predictive of changes in sales revenue for outside (vs. inside) salespeople or for salespeople operating in smaller territories. Our study contributes to the broader literature on leveraging unstructured text and helping managers exploit internal information to better understand changes in customer outcomes.

Keywords: Customer Relationship Management Data, Machine Learning, Large Language Models, ChatGPT, Sales Forecasting, Salesperson Performance

Suggested Citation

Reeder, III, James C. and Chaker, Nawar and Habel, Johannes, Improving Sales Forecasting: Leveraging Unstructured CRM Activity Logs, Large Language Models, and Generative AI (May 30, 2024). Available at SSRN: https://ssrn.com/abstract=4848956 or http://dx.doi.org/10.2139/ssrn.4848956

James C. Reeder, III (Contact Author)

University of Kansas - School of Business ( email )

1300 Sunnyside Avenue
Lawrence, KS 66045
United States

Nawar Chaker

Louisiana State University, Baton Rouge - E.J. Ourso College of Business Administration ( email )

Johannes Habel

University of Houston ( email )

4800 Calhoun Road
Houston, TX 77204
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
122
Abstract Views
295
Rank
432,118
PlumX Metrics