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The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners

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  • Robert P. Rooderkerk
  • Nicole DeHoratius
  • Andrés Musalem

Abstract

We document the evolution of academic research through a bibliometric analysis of 123 retail analytics articles published in top operations management journals from 2000 to 2020. We isolate nine decision areas via manual coding that we verify using automated text analysis (topic modeling). We track variation across decision areas and method‐usage evolution per analytics type, featuring the degree to which big data (e.g., clickstream, social media, product reviews) and analytics suited for these new data sources (e.g., machine learning) are used. Our analysis reveals a rapidly growing field that is evolving in terms of content (decisions, retail sector), data, and methodology. To determine the state of practice, we interviewed global practitioners on the current use of retail analytics. These interviews shed light on the barriers and enablers of adopting advanced analytics in retail. They also highlight what sets companies on the frontier (e.g., Amazon, Alibaba, Walmart) apart from the rest. Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data.

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  • Robert P. Rooderkerk & Nicole DeHoratius & Andrés Musalem, 2022. "The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3727-3748, October.
    • Handle: RePEc:bla:popmgt:v:31:y:2022:i:10:p:3727-3748
    DOI: 10.1111/poms.13811
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    1. Marshall Fisher & Ananth Raman, 2018. "Using Data and Big Data in Retailing," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1665-1669, September.
    2. Aditya Jain & Sanjog Misra & Nils Rudi, 2020. "The Effect of Sales Assistance on Purchase Decisions: An analysis using retail video data," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 273-303, September.
    3. Carl F. Mela & Jason Roos & Yiting Deng, 2013. "Invited Paper --A Keyword History of Marketing Science," Marketing Science, INFORMS, vol. 32(1), pages 8-18, September.
    4. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    5. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    6. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    7. Necati Ertekin & Michael E. Ketzenberg & Gregory R. Heim, 2020. "Assessing Impacts of Store and Salesperson Dimensions of Retail Service Quality on Consumer Returns," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1232-1255, May.
    8. Christian Terwiesch & Marcelo Olivares & Bradley R. Staats & Vishal Gaur, 2020. "OM Forum—A Review of Empirical Operations Management over the Last Two Decades," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 656-668, July.
    9. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    10. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    11. Kaiquan Xu & Jason Chan & Anindya Ghose & Sang Pil Han, 2017. "Battle of the Channels: The Impact of Tablets on Digital Commerce," Management Science, INFORMS, vol. 63(5), pages 1469-1492, May.
    12. Santiago Gallino & Antonio Moreno, 2018. "The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment," Manufacturing & Service Operations Management, INFORMS, vol. 20(4), pages 767-787, October.
    13. Marshall Fisher & Santiago Gallino & Jun Li, 2018. "Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments," Management Science, INFORMS, vol. 64(6), pages 2496-2514, June.
    14. Nicole DeHoratius & Ananth Raman, 2007. "Store Manager Incentive Design and Retail Performance: An Exploratory Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 518-534, April.
    15. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    16. David R. Bell & Santiago Gallino & Antonio Moreno, 2018. "Offline Showrooms in Omnichannel Retail: Demand and Operational Benefits," Management Science, INFORMS, vol. 64(4), pages 1629-1651, April.
    17. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    18. Scott C. Ellis & Shashank Rao & Dheeraj Raju & Thomas J. Goldsby, 2018. "RFID Tag Performance: Linking the Laboratory to the Field through Unsupervised Learning," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1834-1848, October.
    19. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    20. Andrés Musalem & Marcelo Olivares & Eric T. Bradlow & Christian Terwiesch & Daniel Corsten, 2010. "Structural Estimation of the Effect of Out-of-Stocks," Management Science, INFORMS, vol. 56(7), pages 1180-1197, July.
    21. Robert P. Rooderkerk & Harald J. van Heerde & Tammo H. A. Bijmolt, 2013. "Optimizing Retail Assortments," Marketing Science, INFORMS, vol. 32(5), pages 699-715, September.
    22. Tonya Boone & Ram Ganeshan & Robert L. Hicks & Nada R. Sanders, 2018. "Can Google Trends Improve Your Sales Forecast?," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1770-1774, October.
    23. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
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    1. Sushil Gupta & Carlos M. Parra & Subodha Kumar, 2022. "Emerging research problems in different business domains: An analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3647-3650, October.

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