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EMAC 2019 Annual Conference


New Insights into Customer Management II
(A2019-9728)

Published: May 28, 2019

AUTHORS

Peter Verhoef, University of Groningen

ABSTRACT

In the last decade customer management has gone through a considerable change to the extent it can be described as "turmoil" (Haenlein 2017). New technologies enable firms to collect customer-related information like never before, and novel methods are developed to profitably reach the right customer at the right time. This allows to characterize customers on a deeper level and to analyze the impact of marketing actions in a fundamentally more detailed manner.

In the first presentation (Polarized America: Going Beyond Political Partisanship to Preference Partisanship), Verena Schoenmueller, Oded Netzer and Florian Stahl use data from the Twitter users of 592 brands and 4 major politicians and political parties in the US to better understand the differences between conservatives and liberals. Twitter allows to analyze users’ preferences and values with respect to the stores they like to shop in, their sports team they rout for, the newspapers they read, their alcoholic beverage of choice or the charity organizations they support.

The second talk by Lisan Lesscher, Lara Lobschat and Peter C. Verhoef (Do Offline and Online Go Hand in Hand? Cross-Channel and Synergy Effects of Direct Mailing and Display Advertising Along the Consumer Journey to Purchase) study the cross-channel and synergy effects of direct mailings and display advertising. Through a field experiment conducted together with a large insurance firm and Google the authors analyze how direct mailings affect the consumer journey to purchase in online environments as well as how they interact with other frequently applied (online) touchpoints.

The third paper (From RFM to LSTM: Predicting customer future with autoregressive neural nets), co-authored by Thomas Reutterer, Jan Valendin, Klaudius Kalcher and Michael Platzer, investigates the problem of predicting customer behavior. Using recurrent neural nets the authors develop an approach to customer purchase modeling. They benchmark their approach for customer base analysis against state-of-the-art probability models, including the Pareto/GGG model, and demonstrate improved holdout predictions both on an aggregate and on an individual level.

Finally, Huan Liu, Lara Lobschat, Peter C. Verhoef and Hong Zhao (The effectiveness of a long-term and deep promotion strategy in digital channels) use data from a Chinese B2C digital retailer to look into the effects of long-term deep promotions, i.e., promotions offered continuously with an average discount of more than 50% of an order's value. The authors analyze the effects of such promotions on customer behavior and how customers’ promotion expectations impact such behavior.

REFERENCES

We thank Michael Haenlein for taking the initiative.