Predicting Customer Lifetime Value in Multi-Service Industries

Abstract

Customer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.