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Michael Haenlein is Professor of Marketing at the Paris Campus of ESCP Europe. His research interest and expertise deal with the subjects of customer relationship management (CRM), marketing research and social media. In particular, he analyzes the relationship between social networks and customer profitability and more broadly the concepts of social CRM, Word-of-Mouth (WoM) and customer social value. He also works on the best methods to manage unprofitable customers and divest undesirable client relationships. Furthermore, Michael is doing research in quantitative marketing research, particularly structural equation modelling and agent-based modelling, and has co-authored several articles in the area of social media.
Professor Haenlein has published in journals such as the Journal of Marketing, International Journal of Research in Marketing (IJRM), European Management Journal and Business Horizons. He is an Area Editor at the Journal of Marketing and a Senior Editor at the International Journal of Research in Marketing. From 2012 to 2015 Michael was the Editor-in-Chief of the European Management Journal. He is also a member of the editorial boards of the Journal of Marketing Research, Recherche et Applications en Marketing and Business Horizons.
As a consultant, he has worked with a large number of international companies in a variety of industry sectors such as telecommunications, financial services, technology and private equity.
He holds a PhD from the WHU, Otto Beisheim School of Management (2004) and a Habilitation from the Pantheon-Sorbonne University (2013).
Personal Webpage: www.michaelhaenlein.eu
The Sampling Supercharger: Using Agents to Improve Survey Samples (with Roland T. Rust, University of Maryland)
In marketing research, samples are often biased, with many variables (e.g., income, age) collected using ordinal categories. It is often the case that aggregate incidence of these categories is known, by virtue of Census data. Current best practice is to then re-weight the sample’s data points such that the marginal distributions of the variables match the known values. Analyses are then conducted on the re-weighted data. We propose an alternative approach to building a representative sample. We use copulas to estimate the joint distribution. Then, in the spirit of bootstrapping and agent-based modeling, we generate a new “supersample” consisting of agents drawn from the estimated joint distribution. Statistical analyses then use the supersample rather than the original data. The approach might be used to improve any biased sample, even those not arising from surveys. We show using simulations that this “sampling supercharger” can improve the accuracy of statistical estimation, both for estimating the values of individual parameters or estimating multivariate statistical relationships. This improvement is accuracy can be substantial enough to result in qualitatively different results and conclusions.
The Value of Word of Mouth Programs: Seeing the Forest from the Trees (with Barak Libai, Arison School of Business, IDC, Herzliya, Israel)
The increasing connectivity of customers, better tracking mechanisms and a growing realization of the pivotal role of social influence in customer decision-making have led to one of the most notable developments in marketing practice of recent years: proactive efforts to manage customers’ social interactions via word-of-mouth marketing programs. Yet, the value of such programs remains a “riddle” for many in industry, and academic research knowledge on the subject is relatively new and scattered. Within this manuscript, we provide a conceptual approach that helps to synthesize current knowledge towards a systematic valuation of word of mouth programs, and highlight the challenges and open questions. We outline the different program types, the key program decisions to be considered, and how network, product and market characteristics can affect the value created. The value measurement approach we advocate is based on the need to use customer equity as the key measure of word-of-mouth program value, while considering the incremental value of programs and not only their absolute profitability.