The Value of Descriptive Analytics: Evidence from Online Retailers

被引:27
作者
Berman, Ron [1 ]
Israeli, Ayelet [2 ]
机构
[1] Univ Penn, Wharton Sch, Mkt, Philadelphia, PA 19104 USA
[2] Harvard Sch Business, Mkt Unit, Boston, MA 02163 USA
关键词
descriptive analytics; big data; difference-in-differences; synthetic control; e-commerce; online retail; martech; BIG DATA; FIRM PERFORMANCE; LONG TAIL; IMPACT;
D O I
10.1287/mksc.2022.1352
中图分类号
F [经济];
学科分类号
02 ;
摘要
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an increase of 4%-10% in average weekly revenues postadoption. We demonstrate that only retailers that adopt and use the dashboard reap these benefits. The increase in revenue is not explained by price changes or advertising optimization. Instead, it is consistent with the addition of customer relationship management, personalization, and prospecting technologies to retailer websites. The adoption and usage of descriptive analytics also increases the diversity of products sold, the number of transactions, the numbers of website visitors and unique customers, and the revenue from repeat customers. In contrast, there is no change in basket size. These findings are consistent with a complementary effect of descriptive analytics that serve as a monitoring device that helps retailers control additional martech tools and amplify their value. Without using the descriptive dashboard, retailers are unable to reap the benefits associated with these technologies.
引用
收藏
页码:1074 / 1096
页数:24
相关论文
共 49 条
[1]   Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects [J].
Abadie, Alberto .
JOURNAL OF ECONOMIC LITERATURE, 2021, 59 (02) :391-425
[2]   Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program [J].
Abadie, Alberto ;
Diamond, Alexis ;
Hainmueller, Jens .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2010, 105 (490) :493-505
[3]   How to improve firm performance using big data analytics capability and business strategy alignment? [J].
Akter, Shahriar ;
Wamba, Samuel Fosso ;
Gunasekaran, Angappa ;
Dubey, Rameshwar ;
Childe, Stephen J. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 182 :113-131
[4]  
Anderson SJ, 2020, IMPACT MARKETING ANA
[5]   Synthetic Difference-in-Differences [J].
Arkhangelsky, Dmitry ;
Athey, Susan ;
Hirshberg, David A. ;
Imbens, Guido W. ;
Wager, Stefan .
AMERICAN ECONOMIC REVIEW, 2021, 111 (12) :4088-4118
[6]   The Impact of Big Data on Firm Performance: An Empirical Investigation [J].
Bajari, Patrick ;
Chernozhukov, Victor ;
Hortacsu, Au ;
Suzuki, Junichi .
AEA PAPERS AND PROCEEDINGS, 2019, 109 :33-37
[7]   How much should we trust differences-in-differences estimates? [J].
Bertrand, M ;
Duflo, E ;
Mullainathan, S .
QUARTERLY JOURNAL OF ECONOMICS, 2004, 119 (01) :249-275
[8]   Does the internet defy the law of gravity? [J].
Blum, Bernardo S. ;
Goldfarb, Avi .
JOURNAL OF INTERNATIONAL ECONOMICS, 2006, 70 (02) :384-405
[9]  
Borusyak K, 2021, PREPRINT
[10]   The Role of Big Data and Predictive Analytics in Retailing [J].
Bradlow, Eric T. ;
Gangwar, Manish ;
Kopalle, Praveen ;
Voleti, Sudhir .
JOURNAL OF RETAILING, 2017, 93 (01) :79-95