Publications
Publications
- July–August 2024
- Marketing Science
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Abstract
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which 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 that such
an approach may fail to improve long-term performance, and can even harm it, when the outcome of interest
(e.g. repeated purchases or CLV) accumulates unobserved individual differences over time. Our theoretical
analysis demonstrates that unexplained variations in the outcome variable can lead to inaccurate CATE
estimates and incorrect targeting policies. To address this issue, we propose using a surrogate index that
leverages less noisy short-term purchases for long-term CATE estimation and policy learning. Furthermore,
we introduce the separate imputation strategy to handle the non-separable nature of churn and purchase
in marketing contexts. This involves constructing two distinct surrogate models, one for the observed last
purchase time and the other for purchase frequency. 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-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Citation
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.