Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model

Published Online:https://doi.org/10.1287/isre.2021.1078

References

  • Aïvodji U, Arai H, Fortineau O, Gambs S, Hara S, Tapp A (2019) Fairwashing: The risk of rationalization. Proc. Internat. Conf. Machine Learn., vol. 97 (Long Beach, CA), 161–170.Google Scholar
  • Alvarez-Melis D, Jaakkola TS (2018) On the robustness of interpretability methods. ICML Workshop Human Interpretability Machine Learn (Stockholm).Google Scholar
  • Bass FM, Bruce N, Majumdar S, Murthi B (2007) Wearout effects of different advertising themes: A dynamic Bayesian model of the advertising-sales relationship. Marketing Sci. 26(2):179–195.LinkGoogle Scholar
  • Bel-Bachir I, Devillard S, Sawaya A, Valachovicova I (2019) Boosting mall revenues through advanced analytics. McKinsey & Company (January 18), https://www.mckinsey.com/industries/retail/our-insights/boosting-mall-revenues-through-advanced-analytics.Google Scholar
  • Bloch PH, Ridgway NM, Dawson SA (1994) The shopping mall as consumer habitat. J. Retailing 70(1):23–42.CrossrefGoogle Scholar
  • Brynjolfsson E, Hu YJ, Rahman MS (2013) Competing in the age of omnichannel retailing. MIT Sloan Management Rev. 54(4):23–29.Google Scholar
  • Bues M, Steiner M, Stafflage M, Krafft M (2017) How mobile in-store advertising influences purchase intention: Value drivers and mediating effects from a consumer perspective. Psych. Marketing 34(2):157–174.CrossrefGoogle Scholar
  • Carroll JD, Green PE, DeSarbo WS (1979) Optimizing the allocation of a fixed resource: A simple model and its experimental test. J. Marketing 43(1):51–57.CrossrefGoogle Scholar
  • Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Sydney, Australia), 1721–1730.Google Scholar
  • Chen M, Chen ZL (2015) Recent developments in dynamic pricing research: Multiple products, competition, and limited demand information. Production Oper. Management 24(5):704–731.CrossrefGoogle Scholar
  • Cooper LG, Baron P, Levy W, Swisher M, Gogos P (1999) PromocastTM: A new forecasting method for promotion planning. Marketing Sci. 18(3):301–316.LinkGoogle Scholar
  • Coull BA, Ruppert D, Wand M (2001) Simple incorporation of interactions into additive models. Biometrics 57(2):539–545.CrossrefGoogle Scholar
  • Danaher PJ, Smith MS, Ranasinghe K, Danaher TS (2015) Where, when, and how long: Factors that influence the redemption of mobile phone coupons. J. Marketing Res. 52(5):710–725.CrossrefGoogle Scholar
  • De Boor C (1978) A Practical Guide to Splines, vol. 27 (Springer-Verlag, New York).CrossrefGoogle Scholar
  • Doyle P, Saunders J (1990) Multiproduct advertising budgeting. Marketing Sci. 9(2):97–113.LinkGoogle Scholar
  • Fischer M, Albers S, Wagner N, Frie M (2011) Practice prize winner-dynamic marketing budget allocation across countries, products, and marketing activities. Marketing Sci. 30(4):568–585.LinkGoogle Scholar
  • Gao F, Su X (2017) Online and offline information for omnichannel retailing. Manufacturing Service Oper. Management 19(1):84–98.LinkGoogle Scholar
  • Ghose A, Li B, Liu S (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.LinkGoogle Scholar
  • Gijsenberg MJ (2017) Riding the waves: Revealing the impact of intrayear category demand cycles on advertising and pricing effectiveness. J. Marketing Res. 54(2):171–186.CrossrefGoogle Scholar
  • Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: An overview of interpretability of machine learning. 2018 IEEE 5th Internat. Conf. Data Sci. Advanced Analytics (DSAA) (IEEE, Turin, Italy), 80–89.Google Scholar
  • Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecological Model. 157(2–3):89–100.CrossrefGoogle Scholar
  • Hall JM, Kopalle PK, Krishna A (2010) Retailer dynamic pricing and ordering decisions: Category management vs. brand-by-brand approaches. J. Retailing 86(2):172–183.CrossrefGoogle Scholar
  • Hastie T, Tibshirani R (1987) Generalized additive models: Some applications. J. Amer. Statist. Assoc. 82(398):371–386.CrossrefGoogle Scholar
  • Högberg J, Shams P, Wästlund E (2019) Gamified in-store mobile marketing: The mixed effect of gamified point-of-purchase advertising. J. Retailing Consumer Services 50:298–304.CrossrefGoogle Scholar
  • Hooker S, Erhan D, Kindermans PJ, Kim B (2019) A benchmark for interpretability methods in deep neural networks. Advances in Neural Inform. Processing Systems 32, 9737–9748.Google Scholar
  • Iyer G, Kuksov D (2012) Competition in consumer shopping experience. Marketing Sci. 31(6):913–933.LinkGoogle Scholar
  • Jindal P, Zhu T, Chintagunta P, Dhar S (2020) Marketing-mix response across retail formats: The role of shopping trip types. J. Marketing 84(2):114–132.CrossrefGoogle Scholar
  • Keller WI, Deleersnyder B, Gedenk K (2019) Price promotions and popular events. J. Marketing 83(1):73–88.CrossrefGoogle Scholar
  • Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980v3.Google Scholar
  • Lam SY, Vandenbosch M, Hulland J, Pearce M (2001) Evaluating promotions in shopping environments: Decomposing sales response into attraction, conversion, and spending effects. Marketing Sci. 20(2):194–215.LinkGoogle Scholar
  • Liu Y, Li KJ, Chen H, Balachander S (2017) The effects of products’ aesthetic design on demand and marketing-mix effectiveness: The role of segment prototypicality and brand consistency. J. Marketing 81(1):83–102.CrossrefGoogle Scholar
  • Lou Y, Caruana R, Gehrke J (2012) Intelligible models for classification and regression. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Beijing), 150–158.Google Scholar
  • Lou Y, Caruana R, Gehrke J, Hooker G (2013) Accurate intelligible models with pairwise interactions. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Chicago), 623–631.Google Scholar
  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv. Neural Inform. Processing Systems, 4768–4777.Google Scholar
  • Marketing Science Institute (2018) 2018–2020 research priorities. https://www.msi.org/research/2018-2020-research-priorities/.Google Scholar
  • Molnar C (2019) Interpretable Machine Learning. christophm.github.io/interpretable-ml-book/.Google Scholar
  • Narayanan S, Desiraju R, Chintagunta PK (2004) Return on investment implications for pharmaceutical promotional expenditures: The role of marketing-mix interactions. J. Marketing 68(4):90–105.CrossrefGoogle Scholar
  • Nordfält J, Lange F (2013) In-store demonstrations as a promotion tool. J. Retailing Consumer Services 20(1):20–25.CrossrefGoogle Scholar
  • Palaci F, Sedra R, Rao A (2019) Digital-native retailers are giving physical stores a radical makeover. Strategy + Business (January 18), https://www.strategy-business.com/article/Digital-Native-Retailers-Are-Giving-Physical-Stores-a-Radical-Makeover.Google Scholar
  • Peers Y, Van Heerde HJ, Dekimpe MG (2017) Marketing budget allocation across countries: The role of international business cycles. Marketing Sci. 36(5):792–809.LinkGoogle Scholar
  • Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you?: Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, San Francisco), 1135–1144.Google Scholar
  • Ross AS, Hughes MC, Doshi-Velez F (2017) Right for the right reasons: Training differentiable models by constraining their explanations. Proc. 26th Internat. Joint Conf. Artificial Intelligence, 2662–2670.Google Scholar
  • Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretablemodels instead. Nature Machine Intelligence 180:206–215.CrossrefGoogle Scholar
  • Simester D, Hu Y, Brynjolfsson E, Anderson ET (2009) Dynamics of retail advertising: Evidence from a field experiment. Econom. Inquiry 47(3):482–499.CrossrefGoogle Scholar
  • Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H (2020) Fooling lime and shap: Adversarial attacks on post hoc explanation methods. Proc. AAAI/ACM Conf. AI Ethics Soc. (New York), 180–186.Google Scholar
  • Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. J. Machine Learn. Res. 15(1):1929–1958.Google Scholar
  • Stein S (2019) Mall of America’s makers market. Forbes (December 26), https://www.forbes.com/sites/sanfordstein/2018/12/26/mall-of-americas-makers-market/#3228ba28162b.Google Scholar
  • Swinyard WR (1993) The effects of mood, involvement, and quality of store experience on shopping intentions. J. Consumer Res. 20(2):271–280.CrossrefGoogle Scholar
  • Tan S, Caruana R, Hooker G, Koch P, Gordo A (2018) Learning global additive explanations for neural nets using model distillation. Preprint, submitted January 26, https://arxiv.org/abs/1801.08640.Google Scholar
  • Thibault L, Marie-Jeanne L, Christophe M, Xavier R, Detyniecki M (2019) The dangers of post-hoc interpretability: Unjustified counterfactual explanations. Proc. 28th Internat. Joint Conf. Artificial Intelligence (Macao, China).Google Scholar
  • Thomas L (2019) Offering shoppers new experiences isn’t helping as malls see tsunami of store closures, falling traffic. CNBC (April 15), https://www.cnbc.com/2019/04/15/malls-see-tsunami-of-store-closures-as-foot-traffic-declines-further.html.Google Scholar
  • Verhoef PC, Lemon KN, Parasuraman A, Roggeveen A, Tsiros M, Schlesinger LA (2009) Customer experience creation: Determinants, dynamics and management strategies. J. Retailing 85(1):31–41.CrossrefGoogle Scholar
  • Vidale M, Wolfe H (1957) An operations-research study of sales response to advertising. Oper. Res. 5(3):370–381.LinkGoogle Scholar
  • Vitorino MA (2012) Empirical entry games with complementarities: An application to the shopping center industry. J. Marketing Res. 49(2):175–191.CrossrefGoogle Scholar
  • Wakefield KL, Baker J (1998) Excitement at the mall: Determinants and effects on shopping response. J. Retailing 74(4):515–539.CrossrefGoogle Scholar
  • Wang T (2018) Multi-value rule sets for interpretable classification with feature-efficient representations. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems, 10858–10868.Google Scholar
  • Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P (2017) A Bayesian framework for learning rule sets for interpretable classification. J. Machine Learn. Res. 18(1):2357–2393.Google Scholar
  • Warner EJ, Barsky RB (1995) The timing and magnitude of retail store markdowns: Evidence from weekends and holidays. Quart. J. Econom. 110(2):321–352.CrossrefGoogle Scholar
  • Wood SN (2017) Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, Boca Raton, FL).CrossrefGoogle Scholar
  • Wu J, Zhao H, Chen H (2021) Coupons or free shipping? Effects of price promotion strategies on online review ratings. Inform. Systems Res. 32(2):633–652.LinkGoogle Scholar
  • Yang Y, Zeng D, Yang Y, Zhang J (2015) Optimal budget allocation across search advertising markets. INFORMS J. Comput. 27(2):285–300.LinkGoogle Scholar
  • Ye S, Aydin G, Hu S (2015) Sponsored search marketing: Dynamic pricing and advertising for an online retailer. Management Sci. 61(6):1255–1274.LinkGoogle Scholar
  • Yiu CY, Xu SY (2012) A tenant-mix model for shopping malls. Eur. J. Marketing. 46(3-4):524–541.Google Scholar
  • Yohn DL (2017) Why retailers should retire holiday shopping season. Harvard Bus. Rev. (October 9), https://hbr.org/2017/10/why-retailers-should-retire-holiday-shopping-season.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.