Advertisement

Do Store Flyers Work? Implications for NBs and PLs from a Subgroup Analysis with Experimental Data

  • Marco IevaEmail author
  • Ida D’Attoma
  • Cristina Ziliani
  • Juan Carlos Gázquez-Abad
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Store flyers are one of the key media featuring retail and brand promotions. However, the importance attributed to store flyers is not matched by an understanding of how customers respond to them. To shed light on flyer effectiveness, we employ a field experiment to estimate the response of 5000 retail customers to store flyers. We perform an Intention-To-Treat analysis and a Subgroup Analysis as post-hoc analyses with the aim of identifying unusual or unexpected treatment effects. Empirical evidence questions the effectiveness of untargeted flyer distribution. Subgroup Analysis provides further insights at customer segment level.

Keywords

Store flyers Intention-to-treat Subgroup analysis Cluster analysis 

References

  1. Ailawadi, K., Beauchamp, J. P., Donthu, N., Gauri, D., & Shankar, V. (2009). Communication and promotion decisions in retailing: A review and directions for future research. Journal of Retailing, 85(1), 42–55.CrossRefGoogle Scholar
  2. Ailawadi, K., Harlam, B., César, J., & Trounce, D. (2006). Promotion profitability for a retailer: The role of promotion, brand, category, and store characteristics. Journal of Marketing Research, 43(84), 518–535.CrossRefGoogle Scholar
  3. Beecroft, E., & Lee, W. S. (2000, August). Looking beyond mean impacts to see who gains and who loses with time-limited welfare: Evidence from the Indiana Welfare Reform Evaluation. Paper presented at the annual meeting of the National Association for Welfare Research and Statistics, Scottsdale, AZ.Google Scholar
  4. Bodapati, A. (1999). The impact of out-of-store advertising on store sales. Unpublished doctoral dissertation, Stanford University.Google Scholar
  5. Bonetti, M., & Gelber, R. D. (2004). Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics, 5(3), 465–481.CrossRefGoogle Scholar
  6. Chow, S. C., & Liu, J. P. (2004). Design and analysis of clinical trials: Concepts and methodologies. Hoboken, NJ: Wiley-Interscience.Google Scholar
  7. D’Attoma, I., & Liberati, C. (2011). An optimal cluster-based approach for subgroup analysis. International Journal of Business Intelligence and Data Mining, 6(4), 402–425.CrossRefGoogle Scholar
  8. Gázquez-Abad, J. C., & Martínez-López, F. J. (2016). Understanding the impact of store flyers on purchase behaviour: An empirical analysis in the context of Spanish households. Journal of Retailing and Consumer Services, 28, 263–273.CrossRefGoogle Scholar
  9. Gibson, C. M. (2003). Privileging the participant: The importance of sub-group analysis in social welfare evaluations. American Journal of Evaluation, 24(4), 443–469.CrossRefGoogle Scholar
  10. Gijsbrechts, E., Campo, K., & Goossens, T. (2003). The impact of store flyers on store traffic and store sales: A geo-marketing approach. Journal of Retailing, 79, 1–16.CrossRefGoogle Scholar
  11. Grewal, D., & Levy, M. (2009). Emerging issues in retailing research. Journal of Retailing, 85(4), 522–526.CrossRefGoogle Scholar
  12. Kravitz, R. L., Duan, N. H., & Braslow, J. (2004). Evidence-based medicine heterogeneity of treatment effects, and the trouble with averages. The Milbank Quarterly, 82, 661–688.CrossRefGoogle Scholar
  13. Leeflang, P., Parreño-Selva, J., Van Dijk, A., & Wittink, D. (2008). Decomposing the sales promotion bump accounting for cross-category effects. International Journal of Research in Marketing, 25, 201–214.CrossRefGoogle Scholar
  14. Longford, N. T. (1999). Selection bias and treatment heterogeneity in clinical trials. Statistics in Medicine, 18(12), 1467–1474.CrossRefGoogle Scholar
  15. Peck, L. R. (2003). Subgroup analysis in social experiments: Measuring program impacts based on post-treatment choice. American Journal of Evaluation, 24(2), 157–187.CrossRefGoogle Scholar
  16. Peck, L. R. (2005). Using cluster analysis in program evaluation. Evaluation Review, 29(2), 178–196.CrossRefGoogle Scholar
  17. Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815–1828.CrossRefGoogle Scholar
  18. Shadish, W. R., Cook, D. T., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Belmont: Wadsworth Cengage Learning.Google Scholar
  19. Shen, J., & He, X. (2015). Inference for subgroup analysis with a structured logistic-normal mixture model. Journal of the American Statistical Association, 110(509), 303–312.CrossRefGoogle Scholar
  20. Su, X., Tsai, C. L., Wang, H., Nickerson, D. M., & Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 10, 141–158.Google Scholar
  21. van Lin, A., & Gijsbrechts, E. (2015). The battle for health and beauty: What drives supermarket and drugstore category-promotion lifts? International Journal of Research in Marketing. doi: 10.1016/j.ijresmar.2015.09.002.
  22. Volle, P. (1997). Quelles perspectives de développement pour les prospectus promotionnels des distributeurs. Décisions Marketing, 12(September), 39–46.Google Scholar
  23. Zhang, J., Wedel, M., & Pieters, R. (2009). Sales effects of attention to feature advertisements: A Bayesian mediation analysis. Journal of Marketing Research, 46(October), 669–681.CrossRefGoogle Scholar
  24. Ziliani, C., & Ieva, M. (2015). Retail shopper marketing: The future of promotional flyers. International Journal of Retail & Distribution Management, 43(6), 488–502.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marco Ieva
    • 1
    Email author
  • Ida D’Attoma
    • 2
  • Cristina Ziliani
    • 1
  • Juan Carlos Gázquez-Abad
    • 3
  1. 1.Department of EconomicsUniversity of ParmaParmaItaly
  2. 2.Department of StatisticsUniversity of BolognaBolognaItaly
  3. 3.Faculty of Economics and Business, Agrifood Campus of International Excellence ceiA3University of AlmeríaAlmeríaSpain

Personalised recommendations