How can machine learning aid behavioral marketing research?

Abstract

Behavioral science and machine learning have rapidly progressed in recent years. As there is growing interest among behavioral scholars to leverage machine learning, we present strategies for how these methods that can be of value to behavioral scientists using examples centered on behavioral research.

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References

  1. Agarwal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines. Harvard Business Review Press.

  2. Anand, P., Thomas, M., Pillai, K. G., Meng-Lewis, Y. (2020). Linguistic analysis of psychological distancing: reading between the lines for unexpressed bad news. Working paper.

  3. Aral, S., & Nicolaides, C. (2017). Exercise contagion in a global social network. Nature Communications, 8, 1–8.

    Article  Google Scholar 

  4. Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55, 80–98.

    Article  Google Scholar 

  5. Bengio, Y. (2019). The consciousness prior. Working paper.

  6. Berger, J., & Milkman, K. L. (2012). What makes online content viral. Journal of Marketing Research, 49(2), 192–205.

    Article  Google Scholar 

  7. Berger, J., Humphreys, A., Ludwig, S., Moe, W., Netzer, O., & Schweidel, D. (2019). Uniting the tribes: Using text for marketing insight. Journal of Marketing Articles-in-Advance.

  8. Blanchard, S., Dyachenko, T., & Kettle, K. (2020). Locational choices: Studying consumer preference for proximity to others. Journal of Marketing Research forthcoming.

  9. Bollinger, B., Gillingham, K., Kirkpatrick, J., Sexton, S. (2019). Visibility and peer influence. Working Paper.

  10. Brewer, M., & Weber, J. (1994). Self-evaluation effects of interpersonal versus intergroup social comparison. Journal of Personality and Social Psychology, 66, 268–275.

    Article  Google Scholar 

  11. Chakraborty, I., Kim, M., Sudhir, K. (2019). Attribute sentiment scoring with online text reviews: Deep learning and accounting for attribute self-selection. Working Paper.

  12. Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E. (2018). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM Conference on Recommender Systems (pp. 224–232).

    Google Scholar 

  13. Chapelle, O., Schölkopf, B., & Zien, A. (Eds.). (2006). Semi-supervised learning. Cambridge: MIT Press.

    Google Scholar 

  14. Dzyabura, D., & Yoganarasimhan, H. (2018). Machine learning and marketing. In D. Hanssens & N. Mizik (Eds.), Handbook of marketing analytics: Methods and applications in marketing, Public policy, and litigation support. Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  15. Etkin, J. (2016). The hidden cost of personal quantification. Journal of Consumer Research, 42, 967–984.

    Article  Google Scholar 

  16. Fedus, W., Gelada, C., Bengio, Y., Bellmare, M., Larochelle, H. (2019). Hyperbolic discounting and learning over multiple horizons. Working paper.

  17. Fishbach, A., & Touré-Tillery, M. (2013). Goals and motivation. In R. Biswas-Diener & E. Diener (Eds.), Noba textbook series: Psychology. Champaign: DEF Publishers.

    Google Scholar 

  18. Gordon, M., Althoff, T., & Leskovec, J. (2019). Goal-setting and achievement in activity tracking apps: A case study of MyFitnessPal. WWW, 13-17(2019), 1–12.

    Google Scholar 

  19. Guo, T., Sriram, S., Manchanda, P. (2019). The effect of information disclosure on industry payments to physicians. Working paper.

  20. Hagen, L. (2020). Pretty healthy food: How and when aesthetics enhance perceived healthiness. Journal of Marketing (forthcoming).

  21. Hartford, J., Wright, J. R., & Leyton-Brown, K. (2016). Deep learning for predicting human strategic behavior. Thirtieth Annual Conference on Neural Information Processing Systems, 1–9.

  22. Hui, S., Bradlow, E., & Fader, P. (2009). Testing behavioral hypotheses using an integrated model of grocery store shopping path and purchase behavior. Journal of Consumer Research, 36, 478–493.

    Article  Google Scholar 

  23. Kettle, K., Trudel, R., Blanchard, S., & Häubl, G. (2016). Repayment concentration and consumer motivation to get out of debt. Journal of Consumer Research, 43, 460–477.

    Article  Google Scholar 

  24. Kim, S. Y., Lewis, M., and Wang, Y. (2019). Physical store openings and product purchase and return behaviors: A quasi-experimental approach using the causal Forest method. Working Paper.

  25. Knäuper, B., Carriare, K., Frayn, M., Ivanova, E., Xu, Z., Ames-Bull, A., Islam, F., Lowensteyn, I., Sadikaj, G., Luszczynska, A., Grover, S., & McGill CHIP Healthy Weight Program Investigators. (2018). The effects of if-then plans on weight loss: Results of the McGill CHIP healthy weight program randomized controlled trial. Obesity, 26, 1285–1295.

    Article  Google Scholar 

  26. Liu, L., Dzyabura, D., & Mizik, N. (2019). Visual listening in: Extracting brand image portrayed on social media. Marketing Science Articles-in-Advance.

  27. Lockwood, P., Wong, C., McShane, K., & Dolderman, D. (2005). The impact of positive and negative fitness exemplars on motivation. Basic and Applied Social Psychology, 27, 1–13.

    Article  Google Scholar 

  28. Lu, S., Xiao, L., & Ding, M. (2016). A video-based automated recommender (VAR) system for garments. Marketing Science, 35, 484–510.

    Article  Google Scholar 

  29. Lu, T., Bradlow, E., Hutchinson, W. (2017). Binge consumption of online content. Working paper.

  30. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31, 521–543.

    Article  Google Scholar 

  31. Nevskaya, Y., & Albuquerque, P. (2019). How should firms manage excessive product use? A continuous-time demand model to test reward schedules, notifications, and time limits. Journal of Marketing Research, 56, 379–400.

    Article  Google Scholar 

  32. Pelham, B., & Wachsmuth, J. (1995). The waxing and waning of the social self: Assimilation and contrast in social comparison. Journal of Personality and Social Psychology, 69, 825–838.

    Article  Google Scholar 

  33. Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. Neuroimage, 45, 199–209.

    Article  Google Scholar 

  34. Puranam, D., Narayan, V., & Kadiyali, V. (2017). The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Science, 36, 726–746.

    Article  Google Scholar 

  35. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141.

    Article  Google Scholar 

  36. Sanakoyeu, A., Bautista, M., & Ommer, B. (2018). Deep unsupervised learning of visual similarities. Pattern Recognition, 78, 331–343.

    Article  Google Scholar 

  37. Suher, J., Huang, S.-c., & Lee, L. (2019). Planning for multiple shopping goals in the marketplace. Journal of Consumer Psychology, 29, 642–651.

    Article  Google Scholar 

  38. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.

    Google Scholar 

  39. Tanaka, K., & Titterington, D. M. (2004). Probabilistic image processing based on the Q-Ising Model by means of the mean-field method and loopy belief propagation. In Proceedings of the 17thInternational Conference on Pattern Recognition (ICPR’04) (pp. 1–4).

    Google Scholar 

  40. Uetake, K., & Yang, N. (2019). Inspiration from the “biggest loser”: Social interactions in a weight loss program. Marketing Science Articles-in-Advance.

  41. Wager, S., & Athey, S. (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 113(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839

  42. Wang, Y., Lewis, M., Cryder, C., & Sprigg, J. (2016). Enduring effects of goal achievement and failure within customer loyalty programs: A large-scale field experiment. Marketing Science, 35, 565–575.

    Article  Google Scholar 

  43. Zhu, Y., Yu, Z., & Cheng, G. (2019). High dimensional inference in partially linear models. Proceedings of Machine Learning Research, 89, 2760–2769.

    Google Scholar 

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Correspondence to Linda Hagen or Kosuke Uetake or Nathan Yang.

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Hagen, L., Uetake, K., Yang, N. et al. How can machine learning aid behavioral marketing research?. Mark Lett (2020). https://doi.org/10.1007/s11002-020-09535-7

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Keywords

  • Behavioral science
  • Big data
  • Semi-supervised learning
  • Supervised learning
  • Unsupervised learning