The Value of Descriptive Analytics: Evidence from Online Retailers

References

• Abadie A (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects. J. Econom. Literature 59(2):391–425.Crossref, Google Scholar
• Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. J. Amer. Statist. Assoc. 105(490):493–505.Crossref, Google Scholar
• Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016) How to improve firm performance using big data analytics capability and business strategy alignment? Internat. J. Production Econom. 182:113–131.Crossref, Google Scholar
• Anderson SJ, Chintagunta P, Kaul R, Vilcassim NJ (2020) Impact of marketing analytics 1.0 on entrepreneur and firm performance: Field experiment evidence from Rwanda. Working paper, University of Texas, Austin, Austin, TX.Google Scholar
• Arkhangelsky D, Athey S, Hirshberg DA, Imbens GW, Wager S (2021) Synthetic difference-in-differences. Amer. Econom. Rev. 111(12):4088–4118.Crossref, Google Scholar
• Bajari P, Chernozhukov V, Hortaçsu A, Suzuki J (2019) The impact of big data on firm performance: An empirical investigation. AEA Papers Proc. 109:33–37.Crossref, Google Scholar
• Ben-Michael E, Feller A, Rothstein J (2022) Synthetic controls with staggered adoption. J. Royal Statist. Soc.: Series B (Statistical Methodology). Forthcoming.Google Scholar
• Bertrand M, Duflo E, Mullainathan S (2004) How much should we trust differences-in-differences estimates? Quart. J. Econom. 119(1):249–275.Crossref, Google Scholar
• Blum BS, Goldfarb A (2006) Does the internet defy the law of gravity? J. Internat. Econom. 70(2):384–405.Crossref, Google Scholar
• Borusyak K, Jaravel X, Spiess J (2021) Revisiting event study designs: Robust and efficient estimation. Preprint, submitted August 27, https://arxiv.org/abs/2108.12419.Google Scholar
• Bradlow ET, Gangwar M, Kopalle P, Voleti S (2017) The role of big data and predictive analytics in retailing. J. Retailing 93(1):79–95.Crossref, Google Scholar
• Brynjolfsson E, McElheran K (2016) Data in action: Data-driven decision making in U.S. manufacturing. Center for Economic Studies Working Paper 16-06.Google Scholar
• Brynjolfsson E, Hitt L, Kim HH (2011a) Strength in numbers: How does data-driven decision making affect firm performance? Working paper, Stanford University, Stanford, CA.Google Scholar
• Brynjolfsson E, Hu Y, Simester D (2011b) Goodbye Pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Management Sci. 57(8):1373–1386.Link, Google Scholar
• Bughin J (2017) Ten big lessons learned from big data analytics. Appl. Marketing Analytics 2(4):286–295.Google Scholar
• Callaway B, Sant’Anna PHC (2021) Difference-in-differences with multiple time periods. J. Econometrics 225(2):200–230.Crossref, Google Scholar
• Datta H, Knox G, Bronnenberg BJ (2018) Changing their tune: How consumers’ adoption of online streaming affects music consumption and discovery. Marketing Sci. 37(1):5–21.Link, Google Scholar
• de Chaisemartin C, d’Haultfoeuille X (2020) Two-way fixed effects estimators with heterogeneous treatment effects. Amer. Econom. Rev. 110(9):2964–2996.Crossref, Google Scholar
• Delen D, Ram S (2018) Research challenges and opportunities in business analytics. J. Bus. Analytics 1(1):2–12.Crossref, Google Scholar
• Deng Y, Lambrecht A, Liu Y (2019) Spillover effects and freemium strategy in mobile app market. Working paper, University College London, London.Google Scholar
• Germann F, Lilien GL, Rangaswamy A (2013) Performance implications of deploying marketing analytics. Internat. J. Res. Marketing 30(2):114–128.Crossref, Google Scholar
• Germann F, Lilien GL, Fiedler L, Kraus M (2014) Do retailers benefit from deploying customer analytics? J. Retailing 90(4):587–593.Crossref, Google Scholar
• Goodman-Bacon A (2018) Difference-in-differences with variation in treatment timing. Technical report, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Han C (2016) Efficiency comparison of random effects two stage least squares estimators. Econom. Lett. 148:59–62.Crossref, Google Scholar
• Hanssens DM, Pauwels KH (2016) Demonstrating the value of marketing. J. Marketing 80(6):173–190.Crossref, Google Scholar
• Imai K, Keele L, Tingley D (2010a) A general approach to causal mediation analysis. Psych. Methods 15(4):309–334.Crossref, Google Scholar
• Imai K, Keele L, Yamamoto T (2010b) Identification, inference and sensitivity analysis for causal mediation effects. Statist. Sci. 25(1):51–71.Crossref, Google Scholar
• Imai K, Kim IS, Wang E (2022) Matching methods for causal inference with time-series cross-section data. Amer. J. Political Sci. Forthcoming.Google Scholar
• Jin Y, Sun Z (2019) Information acquisition and the return to data. Working paper, University of Toronto, Toronto.Google Scholar
• Koning R, Hasan S, Chatterji A (2022) Experimentation and startup performance: Evidence from a/b testing. Management Sci. Forthcoming.Link, Google Scholar
• Lambrecht A, Tucker C (2013) When does retargeting work? Information specificity in online advertising. J. Marketing Res. 50(5):561–576.Crossref, Google Scholar
• LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Management Rev. 52(2):21–31.Google Scholar
• Lismont J, Vanthienen J, Baesens B, Lemahieu W (2017) Defining analytics maturity indicators: A survey approach. Internat. J. Inform. Management 37(3):114–124.Crossref, Google Scholar
• Manchanda P, Packard G, Pattabhiramaiah A (2015) Social dollars: The economic impact of customer participation in a firm-sponsored online customer community. Marketing Sci. 34(3):367–387.Link, Google Scholar
• Mintz O, Bart Y, Lenk P, Reibstein D (2019) Drowning in metrics: How managers select and trade-off metrics for making marketing budgetary decisions. Preprint, submitted January 1, 2020, https://dx.doi.org/10.2139/ssrn.3502600.Google Scholar
• Müller O, Fay M, vom Brocke J (2018) The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. J. Management Inform. Systems 25(2):488–509.Crossref, Google Scholar
• Oestreicher-Singer G, Sundararajan A (2012) Recommendation networks and the long tail of electronic commerce. Management Inform. Systems Quart. 36(1):65–83.Crossref, Google Scholar
• Pauwels K, Ambler T, Clark BH, LaPointe P, Reibstein D, Skiera B, Wierenga B, Wiesel T (2009) Why, what, how, and what research is needed? J. Service Res. 12(2):175–189.Crossref, Google Scholar
• Pearl J (2014) Interpretation and identification of causal mediation. Psych. Methods 19(4):459–481.Crossref, Google Scholar
• Runge J, Nair H (2021) The role of randomized control trials in online demand generation: Exploratory evidence from Facebook. Preprint, submitted November 16, https://dx.doi.org/10.2139/ssrn.3794028.Google Scholar
• Seddon PB, Constantinidis D, Tamm T, Dod H (2017) How does business analytics contribute to business value? Inform. Systems J. 27(3):237–269.Crossref, Google Scholar
• Shopify (2019) Shopify’s state of commerce report, June 2019. Accessed November 1, 2020, https://news.shopify.com/shopify-unveils-first-state-of-commerce-report.Google Scholar
• Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. Internat. J. Production Econom. 165:234–246.Crossref, Google Scholar
• Wang K, Goldfarb A (2017) Can offline stores drive online sales? J. Marketing Res. 54(5):706–719.Crossref, Google Scholar
• Wedel M, Kannan P (2016) Marketing analytics for data-rich environments. J. Marketing 80(6):97–121.Crossref, Google Scholar
• Wooldridge J (2005) Fixed-effects and related estimators for correlated random-coefficient and treatment-effect panel data models. Rev. Econom. Statist. 87(2):385–390.Crossref, Google Scholar
• Wooldridge J (2010) Econometric Analysis of Cross Section and Panel Data (Chapter 21) Estimating Average Treatment Effects (The MIT Press, Cambridge, MA), 937–940.Google Scholar
• Wooldridge J (2019) Correlated random effects models with unbalanced panels. J. Econometrics 211(1):137–150.Crossref, Google Scholar
• Wooldridge J (2021) Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. Preprint, submitted August 18, https://dx.doi.org/10.2139/ssrn.3906345.Google Scholar