A Causal Approach for Business Optimization: Application on an Online Marketplace

TL;DR

This paper applies causal inference to optimize outreach strategies in an online marketplace, resulting in a 22% increase in item delivery rate through personalized contact policies validated by A/B testing.

Contribution

It introduces a causal inference framework for decision-making in business processes and demonstrates its effectiveness in a real-world online marketplace setting.

Findings

22% increase in item delivery rate

Validated approach through A/B testing

Personalized contact policy improves outcomes

Abstract

A common sales strategy involves having account executives (AEs) actively reach out and contact potential customers. However, not all contact attempts have a positive effect: some attempts do not change customer decisions, while others might even interfere with the desired outcome. In this work we propose using causal inference to estimate the effect of contacting each potential customer and setting the contact policy accordingly. We demonstrate this approach on data from Worthy, an online jewelry marketplace. We examined the Worthy business process to identify relevant decisions and outcomes, and formalized assumptions on how they were made. Using causal tools, we selected a decision point where improving AE contact activity appeared to be promising. We then generated a personalized policy and recommended reaching out only to customers for whom it would be beneficial. Finally, we validated the results in an A\B test over a 3-month period, resulting in an increase in item delivery rate of the targeted population by 22% (p-value=0.026). This policy is now being used on an ongoing basis.