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Summary Articles Marketing Analytics EBC4081

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Summary articles Marketing Analytics EBC4081 1. Ascarza, Eva, Raghuram Iyengar, and Martin Schleicher (2016). The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment. Journal of Marketing Research, 53, 46-60. 2. Bijmolt, Tammo H.A., Harald J. van Heerde, and Rik G.M. Pieters (2005). New Empirical Generalizations on the Determinants of Price Elasticity. Journal of Marketing Research, 42 (May), 141-156. 3. Cleeren, Kathleen, Harald J. van Heerde and Marnik G. Dekimpe (2013). Rising From the Ashes: How Brands and Categories Can Overcome Product-Harm Crises. Journal of Marketing, 77 (March), 58-73. 4. Datta, Hannes, Bram Foubert, and Harald J. van Heerde (2015). The Challenge of Retaining Customers Acquired with Free Trials. Journal of Marketing Research, 52, 217-234 5. De Vries, Lisette, Sonja Gensler, and Peter S.H. Leeflang (2017). Effects of Traditional Advertising and Social Messages on Brand-Building Metrics and Customer Acquisition. Journal of Marketing, 81, 1-15. 6. Dinner, Isaac M., Harald J. van Heerde, and Scott A. Neslin (2014). Driving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising. Journal of Marketing Research, 51, 527-545. 7. Fader, Peter S., Bruce G.S. Hardie, and Ka Lok Lee (2005). RFM and CLV: Using IsoValue Curves for Customer Base Analysis. Journal of Marketing Research, 42, 415- 430. 8. Gedenk, Karen, Scott A. Neslin, and Kusum L. Ailawadi (2006). Sales promotion. In: Kraft and Mantrala Eds. Retailing in the 21st Century: Current and Future Trends, Springer, 345-359. 9. Germann, Frank, Gary L. Lilien, and Arvind Rangaswamy (2013). Performance Implications of Deploying Marketing Analytics. International Journal of Research in Marketing, 30, 114-128. 10. Joshi, Amit and Dominique M. Hanssens (2010). The Direct and Indirect Effects of Advertising Spending on Firm Value. Journal of Marketing, 74 (January), 20-33. 11. Kumar, V., Yashoda Bhagwat, and Xi (Alan) Zhang (2015). Regaining “Lost” Customers: The Predictive Power of First-Lifetime Behavior, the Reason for Defection, and the Nature of the Win-Back Offer. Journal of Marketing, 79, 34-55. 12. Leenheer, Jorna, Harald J. van Heerde, Tammo H.A. Bijmolt , and Ale Smidts (2007). Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical Marketing Analytics - EBC Page 9 of 33 Analysis Accounting for Self-selecting Members. International Journal of Research in Marketing, 24, 31-47. 13. Lobschat, Lara, Ernst C. Osinga, and Werner J. Reinartz (2017). What Happens Online Stays Online? Segment-specific Online and Offline Effects of Banner Advertisements. Journal of Marketing Research, forthcoming. 14. Reinartz, Werner J., and V. Kumar (2003). The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration. Journal of Marketing, 67, 77-99. 15. Risselada, Hans, Peter C. Verhoef, and Tammo H.A. Bijmolt (2010). Staying Power of Churn Prediction Models. Journal of Interactive Marketing, 24, 198-208. 16. Schmitt, Philipp, Bernd Skiera, and Christophe Van den Bulte (2011). Referral Programs and Customer Value. Journal of Marketing, 75, 46-59. 17. Sethuraman, Raj, Gerard J. Tellis, and Richard A. Briesch (2011). How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities. Journal of Marketing Research, 48 (June), 457-471. 18. Tellis, Gerard J. ((2007). Advertising Effectiveness in Contemporary Markets. The SAGE Handbook of Advertising. Eds: Gerard J. Tellis and Tim Ambler. Sage Publications: Los Angeles (CA), Chapter 4.4, p264-279. 19. Van Heerde, Harald J., Peter S.H. Leefland, and Dick R. Wittink (2000). The Estimation of Pre- and Postpromotion Dips with Store-Level Scanner Data. Journal of Marketing Research, 37, 383-395. 20. You, Ya, Gautham G. Vadakkepatt, and Amit M. Joshi (2015). A Meta-Analysis of Electronic Word-of-Mouth Elasticity. Journal of Marketing, 79, 19-39.

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Summary Marketing Analytics
Meeting 1: Opening lecture
Rising from the ashes: How brand and categories can overcome product-harm crises. Cleeren
et al. (2013)
Aim: Develop a contingency framework that bridges research on crisis characteristics and the post
crisis effectiveness of advertising and price. Moreover, it studies their main and interaction effect on
brand share and category purchases.
Whether the effectiveness of post crises
advertising and price adjustments is moderated
by the crisis characteristics (the amount of
negative publicity surrounding the crisis and
whether the affected brand had to acknowledge
blame.)
● Advertising X negative publicity -> H1 & H2:
The effectiveness of brand advertising and
category advertising are greater when there is
high level of negative publicity surrounding the
crisis
● Price X negative publicity -> H3 & H4:
Brand price sensitivity and category price
sensitivity are greater when there is a higher
level of negative publicity surrounding the crisis.
● Advertising X blame -> H5 & H6:
The effectiveness of brand advertising and
category advertising are lower when the affected
brand acknowledges blame than when they don’t
● Price X blame -> H7& H8:
Brand price sensitivity and category price
sensitivity are greater when the affected brand
Negative publicity: The extent
acknowledges blame than when they don’t
to which the media report on
the product-harm crisis (could
be harmful or beneficial)
Blame acknowledgement:
Whether the company
acknowledges responsibility for
the product-harm crisis




Results:
Crisis:
• By acknowledging blame, the other brands in the
category move out of harm’s way. (B=1.173,
p<0.01)
Interaction:
• H3 not supported -> Negative publicity has no
significant effect on the price sensitivity of brand
share.
• H8 not supported -> category price sensitive is not
influenced by blame acknowledgement.

Control:
• When product-harm crisis strikes there is a strong
decrease in brand share and category purchases for
brand-loyal consumer and high users. (B=-1.138 &
B=-0.728, p<0.01)
• With private labels the spill over to the category is
reduce (B=1.125, p<0.01)
• Brands suffer more when the category is more
concentrated. (B=-0.257, p<0.05)
• Category purchases decrease more in the
Netherlands. (B=-0.506, p<0.05)



1

, Key takeaway: The effectiveness of advertising and price depends on publicity and blame!




2

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