Doing More with Less: Overcoming Ineffective Long-term Targeting Using Short-Term Signals

58 Pages Posted: 24 Oct 2022 Last revised: 16 Feb 2024

See all articles by Ta-Wei Huang

Ta-Wei Huang

Harvard Business School

Eva Ascarza

Harvard Business School

Date Written: April 28, 2023

Abstract

Firms are increasingly interested in developing targeted interventions for customers with the best response. This requires identifying differences in customer sensitivity, typically through the conditional average treatment effect (CATE) estimation. In theory, to optimize long-term business performance, firms should design targeting policies based on CATE models constructed using long-term outcomes. However, we show theoretically and empirically that this method can fail to improve long-term results, particularly when the desired outcome is the cumulative result of recurring customer actions, like repeated purchases, due to the accumulation of unexplained individual differences over time. To address this challenge, we propose using a surrogate index that leverages short-term outcomes for long-term CATE estimation and policy learning. Moreover, for the creation of this index, we propose the separate imputation strategy, designed to reduce the additional variance caused by the inseparable nature of customer churn and purchase intensity in common marketing contexts. This involves constructing two distinct surrogate models, one for the observed last purchase time and the other for the observed purchase intensity. Our simulation and real-world application show that (i) using short-term signals instead of the actual long-term outcome significantly improves long-run targeting performance, and (ii) the separate imputation technique outperforms existing imputation approaches.

Keywords: Long-term Targeting, Heterogeneous Treatment effect, Statistical Surrogacy, Customer Churn, Field Experiments, Conditional Average Treatment Effect (CATE)

JEL Classification: C51, C52, C53, C54, M31

Suggested Citation

Huang, Ta-Wei and Ascarza, Eva, Doing More with Less: Overcoming Ineffective Long-term Targeting Using Short-Term Signals (April 28, 2023). Available at SSRN: https://ssrn.com/abstract=4254202 or http://dx.doi.org/10.2139/ssrn.4254202

Ta-Wei Huang (Contact Author)

Harvard Business School ( email )

Boston, MA 02163
United States

Eva Ascarza

Harvard Business School ( email )

Soldiers Field
Boston, MA 02163
United States

HOME PAGE: http://evaascarza.com

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
231
Abstract Views
1,112
Rank
237,011
PlumX Metrics