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Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition

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Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition. / Esteban-Bravo, Mercedes ; Vidal-Sanz, Jose M. ; Yildirim, Gokhan.

In: Marketing Science, Vol. 33, No. 5, 2014, p. 621-640.

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Esteban-Bravo, Mercedes ; Vidal-Sanz, Jose M. ; Yildirim, Gokhan. / Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition. In: Marketing Science. 2014 ; Vol. 33, No. 5. pp. 621-640.

Bibtex

@article{b692340c14f34105bd74e42c1436d887,
title = "Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition",
abstract = "The CRM allocation of marketing budget is potentially misleading when it uses individual CLV estimations from historical data. Planned marketing interventions would change the purchasing behavior of different customers and history- based decisions would thus be sub-optimal. To cope with this inherent endogeneity, we model the optimal allocation of the marketing mix by accounting simultaneously for mass interventions and direct marketing interventions on each customer. This is a large stochastic dynamic problem that, in general, is computationally rather intractable due to the “curse of dimensionality”. We present an algorithm to derive the optimal marketing policies (how the firm should allocate its marketing resources), and the expected present value of those decisions which maximize the long-term profitability of firms. This allows the firm to value customers/segments and helps the firm to target the customers/segments that maximize long-term profitability given the optimal marketing resources allocation. We apply the proposed approach in the context of a manufacturer of kitchen appliances. The results identify the most effective marketing policies and the endogenous customer values. It is in this context that we also dynamically identify the most-profitable customer and the short- and long-term effects of marketing activities on each customer.",
keywords = "CRM, marketing resource allocation , long-term effect of marketing activities , stochastic dynamic programming , dynamic panel-data models",
author = "Mercedes Esteban-Bravo and Vidal-Sanz, {Jose M.} and Gokhan Yildirim",
year = "2014",
doi = "10.1287/mksc.2014.0848",
language = "English",
volume = "33",
pages = "621--640",
journal = "Marketing Science",
issn = "0732-2399",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "5",

}

RIS

TY - JOUR

T1 - Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition

AU - Esteban-Bravo, Mercedes

AU - Vidal-Sanz, Jose M.

AU - Yildirim, Gokhan

PY - 2014

Y1 - 2014

N2 - The CRM allocation of marketing budget is potentially misleading when it uses individual CLV estimations from historical data. Planned marketing interventions would change the purchasing behavior of different customers and history- based decisions would thus be sub-optimal. To cope with this inherent endogeneity, we model the optimal allocation of the marketing mix by accounting simultaneously for mass interventions and direct marketing interventions on each customer. This is a large stochastic dynamic problem that, in general, is computationally rather intractable due to the “curse of dimensionality”. We present an algorithm to derive the optimal marketing policies (how the firm should allocate its marketing resources), and the expected present value of those decisions which maximize the long-term profitability of firms. This allows the firm to value customers/segments and helps the firm to target the customers/segments that maximize long-term profitability given the optimal marketing resources allocation. We apply the proposed approach in the context of a manufacturer of kitchen appliances. The results identify the most effective marketing policies and the endogenous customer values. It is in this context that we also dynamically identify the most-profitable customer and the short- and long-term effects of marketing activities on each customer.

AB - The CRM allocation of marketing budget is potentially misleading when it uses individual CLV estimations from historical data. Planned marketing interventions would change the purchasing behavior of different customers and history- based decisions would thus be sub-optimal. To cope with this inherent endogeneity, we model the optimal allocation of the marketing mix by accounting simultaneously for mass interventions and direct marketing interventions on each customer. This is a large stochastic dynamic problem that, in general, is computationally rather intractable due to the “curse of dimensionality”. We present an algorithm to derive the optimal marketing policies (how the firm should allocate its marketing resources), and the expected present value of those decisions which maximize the long-term profitability of firms. This allows the firm to value customers/segments and helps the firm to target the customers/segments that maximize long-term profitability given the optimal marketing resources allocation. We apply the proposed approach in the context of a manufacturer of kitchen appliances. The results identify the most effective marketing policies and the endogenous customer values. It is in this context that we also dynamically identify the most-profitable customer and the short- and long-term effects of marketing activities on each customer.

KW - CRM

KW - marketing resource allocation

KW - long-term effect of marketing activities

KW - stochastic dynamic programming

KW - dynamic panel-data models

U2 - 10.1287/mksc.2014.0848

DO - 10.1287/mksc.2014.0848

M3 - Journal article

VL - 33

SP - 621

EP - 640

JO - Marketing Science

JF - Marketing Science

SN - 0732-2399

IS - 5

ER -