Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer

Abstract Retailers are experiencing a systematic shift in the buying habits of their customers as more customers buy across different channels. Marketing managers face the daunting task of embracing online and offline channels to engage consumers, influence choice, and create habits to sustain a competitive advantage. We develop a dynamic segmentation model of channel choice and purchase frequency to assess the responsiveness of segments to catalogues and email communications. In addition, we perform profitability analysis to offer insights on the profitability of using catalogues and emails to reach customers. For certain firms, especially those with a history of using catalogue mailings, the findings suggest that catalogues remain relevant and are an effective tool at influencing purchases across both online and offline channels despite the increasing trend toward digital marketing. In addition, we found a segment of digital consumers respond favorably to both emails and catalogues. We argue catalogues have retained their competitive advantage over email marketing communication because the catalogue may not compete for attention with consumers' other digital distractions.

[1]  J. Mullahy Specification and testing of some modified count data models , 1986 .

[2]  S. Neslin,et al.  Multichannel Shopper Segments and Their Covariates , 2008 .

[3]  Thorsten Wiesel,et al.  Marketing's profit impact , 2011 .

[4]  Vibhanshu Abhishek,et al.  Media Exposure through the Funnel: A Model of Multi-Stage Attribution , 2012 .

[5]  Bruce G. S. Hardie,et al.  A Joint Model of Usage and Churn in Contractual Settings , 2013, Mark. Sci..

[6]  Jacquelyn S. Thomas,et al.  Challenges and Opportunities in Multichannel Customer Management , 2006 .

[7]  Scott A. Neslin,et al.  Can Marketing Campaigns Induce Multichannel Buying and More Profitable Customers? A Field Experiment , 2016, Mark. Sci..

[8]  David H. Reiley,et al.  Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo! , 2014 .

[9]  Ricardo Montoya,et al.  Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability , 2010, Mark. Sci..

[10]  S. Neslin,et al.  The Effect of Search Channel Elimination on Purchase Incidence, Order Size and Channel Choice , 2013 .

[11]  Avi Goldfarb,et al.  Online Display Advertising: Targeting and Obtrusiveness , 2011, Mark. Sci..

[12]  W. Zucchini,et al.  Hidden Markov Models for Time Series: An Introduction Using R , 2009 .

[13]  Wayne S. DeSarbo,et al.  A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models , 2018 .

[14]  Koen Pauwels,et al.  Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity , 2016 .

[15]  Peter J. Danaher,et al.  Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign , 2013 .

[16]  S. Neslin,et al.  Decision Process Evolution in Customer Channel Choice , 2011 .

[17]  P. K. Kannan,et al.  Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment , 2014 .

[18]  Scott A. Neslin,et al.  Driving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising , 2013 .

[19]  Carl F. Mela,et al.  Customer Channel Migration , 2008 .

[20]  Prasad A. Naik,et al.  A Hierarchical Marketing Communications Model of Online and Offline Media Synergies , 2009 .

[21]  Katherine N. Lemon,et al.  Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time , 2013 .

[22]  Emma K. Macdonald,et al.  The impact of different touchpoints on brand consideration , 2015 .

[23]  P C Molenaar,et al.  Confidence intervals for hidden Markov model parameters. , 2000, The British journal of mathematical and statistical psychology.

[24]  Oded Netzer,et al.  A Hidden Markov Model of Customer Relationship Dynamics , 2008, Mark. Sci..

[25]  John N. Tsitsiklis,et al.  Dynamic Catalog Mailing Policies , 2006, Manag. Sci..

[26]  Mary Caravella,et al.  Adding Bricks to Clicks: Predicting the Patterns of Cross-Channel Elasticities over Time , 2011 .

[27]  Stephanie T. Lanza,et al.  Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences , 2009 .

[28]  P. K. Kannan,et al.  Digital Marketing: A Framework, Review and Research Agenda , 2017 .

[29]  Jan Bulla,et al.  Computational issues in parameter estimation for stationary hidden Markov models , 2008, Comput. Stat..

[30]  Katherine N. Lemon,et al.  Understanding Customer Experience Throughout the Customer Journey , 2016 .

[31]  S. Neslin,et al.  Multichannel customer management: Understanding the research-shopper phenomenon , 2007 .

[32]  Glenn B. Voss,et al.  Enough Is Enough! The Fine Line in Executing Multichannel Relational Communication , 2011 .

[33]  Jonathan Z. Zhang,et al.  The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings , 2016 .