How can machine learning aid behavioral marketing research?

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.

[1]  M. Brewer,et al.  Self-evaluation effects of interpersonal versus intergroup social comparison. , 1994, Journal of personality and social psychology.

[2]  Joseph G. Weber,et al.  Self-evaluation effects of interpersonal versus intergroup social comparison. , 1994, Journal of personality and social psychology.

[3]  B. Pelham,et al.  The waxing and waning of the social self: Assimilation and contrast in social comparison. , 1995 .

[4]  D. Titterington,et al.  Probabilistic image processing based on the Q-Ising model by means of the mean-field method and loopy belief propagation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[5]  P. Lockwood,et al.  The Impact of Positive and Negative Fitness Exemplars on Motivation , 2005 .

[6]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[7]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[8]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[9]  Eric T. Bradlow,et al.  Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior , 2009 .

[10]  Jacob Goldenberg,et al.  Mine Your Own Business: Market-Structure Surveillance Through Text Mining , 2012, Mark. Sci..

[11]  Katherine L. Milkman,et al.  What Makes Online Content Viral? , 2012 .

[12]  Kevin Leyton-Brown,et al.  Deep Learning for Predicting Human Strategic Behavior , 2016, NIPS.

[13]  Jordan Etkin.  The Hidden Cost of Personal Quantification , 2016 .

[14]  Michael Lewis,et al.  Enduring Effects of Goal Achievement and Failure Within Customer Loyalty Programs: A Large-Scale Field Experiment , 2016, Mark. Sci..

[15]  Li Xiao,et al.  A Video-Based Automated Recommender (VAR) System for Garments , 2016, Mark. Sci..

[16]  G. Häubl,et al.  Repayment Concentration and Consumer Motivation to Get Out of Debt , 2016 .

[17]  Sinan Aral,et al.  Exercise contagion in a global social network , 2017, Nature Communications.

[18]  Vrinda Kadiyali,et al.  The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors , 2017, Mark. Sci..

[19]  Puneet Manchanda,et al.  The Effect of Information Disclosure on Industry Payments to Physicians , 2017, Journal of Marketing Research.

[20]  Margaret A. Johns,et al.  Corrigendum: The OncoPPi network of cancer-focused protein–protein interactions to inform biological insights and therapeutic strategies , 2017, Nature Communications.

[21]  G. Sadikaj,et al.  The Effects of If‐Then Plans on Weight Loss: Results of the McGill CHIP Healthy Weight Program Randomized Controlled Trial , 2018, Obesity.

[22]  Barbara E. Engelhardt,et al.  How algorithmic confounding in recommendation systems increases homogeneity and decreases utility , 2017, RecSys.

[23]  Björn Ommer,et al.  Deep unsupervised learning of visual similarities , 2018, Pattern Recognit..

[24]  Daria Dzyabura,et al.  Machine learning and marketing , 2018 .

[25]  Eva Ascarza.  Retention Futility: Targeting High-Risk Customers Might be Ineffective , 2018 .

[26]  Stefan Wager,et al.  Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.

[27]  Ishita Chakraborty,et al.  Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes , 2019, SSRN Electronic Journal.

[28]  Arun Rai,et al.  Explainable AI: from black box to glass box , 2019, Journal of the Academy of Marketing Science.

[29]  Jure Leskovec,et al.  Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal , 2019, WWW.

[30]  Paulo Albuquerque,et al.  How Should Firms Manage Excessive Product Use? A Continuous-Time Demand Model to Test Reward Schedules, Notifications, and Time Limits , 2019, Journal of Marketing Research.

[31]  Ying Zhu,et al.  High Dimensional Inference in Partially Linear Models , 2019, AISTATS.

[32]  A. Sumner,et al.  De-industrialization, re-industrialization, and the resurgence of state capitalism: The case of Indonesia , 2019, WIDER Working Paper.

[33]  Leonard Lee,et al.  Planning for Multiple Shopping Goals in the Marketplace , 2019, Journal of Consumer Psychology.

[34]  David A. Schweidel,et al.  Uniting the Tribes: Using Text for Marketing Insight , 2019, Journal of Marketing.

[35]  Kenneth Gillingham,et al.  Visibility and Peer Influence in Durable Good Adoption , 2020, Mark. Sci..

[36]  Yoshua Bengio,et al.  Hyperbolic Discounting and Learning over Multiple Horizons , 2019, ArXiv.

[37]  Linda Hagen.  Pretty Healthy Food: How and When Aesthetics Enhance Perceived Healthiness , 2020 .

[38]  S. Blanchard,et al.  Locational Choices: Modeling Consumer Preferences for Proximity to Others in Reserved Seating Venues , 2020 .

[39]  Nathan Yang,et al.  Inspiration from the "Biggest Loser": Social Interactions in a Weight Loss Program , 2020, Mark. Sci..

[40]  Daria Dzyabura,et al.  Visual Listening In: Extracting Brand Image Portrayed on Social Media , 2020, Mark. Sci..