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Michael Haenlein est professeur au département marketing du campus Paris d'ESCP Europe. Il donne des cours de Customer Relationship Management et de Marketing Research, ainsi que de e-Commerce, Pricing and Principles of Marketing. Il enseigne auprès des étudiants du cycle Master, mais aussi pour les cadres en formation continue. Suite à l’obtention de la licence de WHU, l’école de Management d’Otto Beisheim, il travaille cinq ans comme consultant stratégique pour Bain & Company dans les bureaux de Munich, Londres et Paris. Il propose alors des solutions en termes d’organisation et de marketing à des entreprises issues d’une large gamme d’industries, comme les télécommunications, le divertissement et le développement de logiciels. Il obtient un Ph.D. en Sciences de Gestion à WHU et rejoint ESCP Europe en septembre 2005 où il développe les cours de Customer Relationship Management et e-Commerce. La recherche de Michael repose sur les domaines du management de la relation client (CRM) et les analyses de bases de données, ainsi que les modèles du marketing stochastiques et les modélisations des équations structurelles. Parmi ces domaines, Michael est particulièrement intéressé par la gestion des relations client non-profitables ainsi que les effets du réseau social et du comportement de bouche-à-oreille dans les estimations de valeur de vie du client (CLV). Le professeur Haenlein est l’auteur de différentes publications de recherche pour d’importants journaux en langue anglaise, dont Journal of Marketing, Journal of Product Innovation Management et European Management Journal. Il fait partie du comité de lecture du Journal of Marketing et Recherche et Applications en Marketing, les premières revues de marketing en France et dans le monde. En tant que consultant, il a collaboré avec des grandes entreprises de télécommunications et industrie de services financiers et redéfini leur stratégie de CRM.

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Recherche actuelle

A model to determine customer lifetime value in a retail banking context (with Andreas M. Kaplan and Anemone J. Beeser) European Management Journal, (2007)
During the past decade, the retail banking industry started to face a set of radically new challenges that had an overall negative impact on industry margin and profitability. In response to these challenges, more and more retail banks have focused on increasing the scale of their operations, which has led to a rising importance of mergers and acquisitions (M&A). From a Marketing perspective, M&A transactions are nothing other than the acquisition of the customer base of one company by another one, usually based on the assumption that the acquiring bank can manage this customer base more profitably than the selling bank was able to. It is therefore not surprising that questions about the valuation of customers have become more important than ever in the retail banking industry. Our article provides a contribution in this area by presenting a customer valuation model that we developed in cooperation with a leading German retail bank, which takes account of the specific requirements of this industry. Our model is based on a combination of first-order Markov chain modeling and CART (classification and regression tree) and can deal equally well with discrete one-time transactions as with continuous revenue streams. Furthermore, it is based on the analysis of homogeneous groups instead of individual customers and is easy to understand and parsimonious in nature. In our article we provide proof of the practical value of our approach by validating our model using 6.2 million datasets. This validation shows how our model can be applied in day-to-day business life.

Factors influencing the adoption of mass customization: The impact of base category consumption frequency and need satisfaction (with Kaplan, Andreas M., Detlef Schoder), Journal of Product Innovation Management, 2007, 24 (2), 101 – 16.
Mass customization has received considerable interest among researchers. However, although many authors have analyzed this concept from different angles, the question of which factors can be used to spot customers most likely to adopt a mass-customized product has not been answered to a satisfactory extent until now. This article explicitly deals with this question by focusing on factors related to the base category, which is defined as the group of all standardized products within the same product category as the mass-customized product under investigation. Specifically, this article investigates the influence of a customer’s base category consumption frequency and need satisfaction on the decision to adopt a mass-customized product within this base category. A set of competing hypotheses regarding these influences is developed and subsequently evaluated by a combination of partial least squares and latent class analysis. This is done by using a sample of 2,114 customers surveyed regarding their adoption of an individualized printed newspaper. The results generated are threefold: First, it is shown that there is a significant direct influence of base category consumption frequency and need satisfaction on the behavioral intention to adopt. The more frequently a subject consumes products out of the base category or the more satisfied his or her needs are due to this consumption, the higher the behavioral intention to adopt a mass-customized product within this base category. Second, the article provides an indication that base category consumption frequency has a significant moderating effect when investigating the behavioral intention to adopt in the context of the theory of reasoned action and the technology acceptance model. The more frequently a subject consumes products out of the base category, the more important will be the impact of perceived ease of use mediated by perceived usefulness. Finally, this article shows that different latent classes with respect to unobserved heterogeneity regarding the latent variables base category need satisfaction or dissatisfaction have significantly different adoption behaviors. Individuals who show a high level of need dissatisfaction are less interested in the ease of use of a mass-customized product than its usefulness (i.e., increase in need satisfaction). On the other hand, subjects who have a high degree of base category need satisfaction base their adoption decision mainly on the ease of use of the mass-customized product. These results are of managerial relevance regarding the prediction of market reactions and the understanding of the strategic use of product-line extensions based on mass-customized products. This work provides an indication that base category consumption frequency and need satisfaction positively influence the behavioral intention to adopt a mass-customized product. Hence, mass customization can be seen as one way to deepen the relationship with existing clients.

Valuing the real option of abandoning unprofitable customers when calculating customer lifetime value (with Andreas M. Kaplan, and Detlef Schoder), 2006, Journal of marketing, 70 (3), 5 - 20.
In recent years, several authors have developed models that focus on the allocation of scarce marketing resources based on customer lifetime value (CLV). These approaches use CLV to develop a rank order of customers and recommend devoting more resources to customers with higher ranks. However, it has been discussed in the literature that a simple net present value analysis may not reflect the value of the flexibility to make such decisions. Therefore, some authors recommend the use of a real-options analysis in certain situations. Building on this stream of research and using the case of the option to abandon unprofitable customers, this article proposes an approach that combines real-options analysis and CLV; this approach explicitly values the seller’s flexibility to abandon unprofitable customers. Using a combination of examples, empirical analysis, and Monte Carlo simulations, the authors provide evidence that the divergence between CLV that includes and CLV that excludes option value can be substantial and may not be the same for all customers. Therefore, the authors conclude that a distribution of customers based on CLV can change when option value is included. Thus, using CLV as a basis for marketing decisions but not including the value of the option to make such decisions a priori when calculating CLV can lead to an overall biased result.

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