Prior to his academic career, Dr. Haenlein has worked five years as a strategy consultant for Bain & Company in Germany, France and the United Kingdom. Since then he has continued to support a large number of national and international firms in developing their marketing and customer relationship management (CRM) strategy as an independent consultant.
Michael Haenlein is specialized in questions of customer insights and segmentation, customer loyalty and marketing return-on-investment. His industry experience include telecommunications, financial services, technology and private equity.
Customer lifetime value (CLV) or the discounted future profit that companies can expect from a client relationship, is a key element in any customer relationship management (CRM) strategy. Only a profound understanding of CLV and its drivers (i.e., size of wallet, share of wallet, cost to serve, customer lifetime duration and customer acquisition cost), gives firms the necessary information to craft profitable customer relationships. Recent advances in statistical modeling have made it possible to predict future transaction volume, an essential input in any CLV calculation, with remarkable degrees of accuracy. This allows firms to spot customers who are particularly financially attractive, not only due to their past but also given their expected future purchase behavior.
In many industry sectors, the path to profitable growth goes through the development of long-term customer relationships. Yet, not all customers are worth to be retained and while in some cases loyalty is associated with substantial financial benefits, this is not necessarily true in other situations. By using advanced statistical techniques, customized to specific business models and industry sectors, it is possible to identify which customers should be retained, when and how often they should be contacted to generate optimal levels of customer loyalty and which client relationships should be terminated sooner rather than later. This allows companies to manage customer loyalty strategically and individually, without necessarily having to treat every customer as a king.
Choosing between the implementation of different marketing actions is part of the day-to-day business of every executive. Yet comparing the expected return-on-investment (ROI) of different strategic initiatives is far from trivial, especially in cases where they relate to different steps of the customer experience. Using a combination of data stemming from customer surveys and external sources and by applying sophisticated statistical models and advanced (agent-based) simulations, it is nowadays possible to estimate the expected ROI of a given marketing action prior to its implementation. This allows to make a rational and justifiable choice between competing initiatives which is particularly important in difficult economic times where cross-functional competition for budget tends to get more intense.