Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting

Abstract

User profile is a summary of a consumer’s interests and preferences revealed through the consumer’s online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock “big data’s” potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0956 .

A multidisciplinary perspective of big data in management research

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Topic modeling in marketing: recent advances and research opportunities

“…While the former three represent about 89% of publications, the latter four comprise of 7 papers. In general, 10 of 61 papers were published in (mostly quantitative) marketing journals, of which 5 can be assigned to sales / retailing (Blanchard et al 2017;Hruschka 2014;Jacobs et al 2016;Schröder 2017;Trusov et al 2016), 3 to online textual consumer reviews and services research (Büschken and Allenby 2016;Calheiros et al 2017;Tirullinai and Tellis 2014), 1 to social media (Song et al 2017), and 1 to research in marketing literature (Amado et al 2017). Furthermore, we transferred the sum (numbers), and the relative importance (color) of the methodological strategies exerted by scholars into a matrix (Table 6) to better detect patterns and predict trends in research.…”

Editorial—Marketing Science and Big Data

“…Ringel and Skiera (2016) develop innovative mapping methods to help visualize complex market structures among more than 1,000 products. Trusov et al (2016) develop and implement a targeting algorithm that reads consumers' web-surfing behavior, profiles potential consumers, and enables improved behavioral targeting. Indeed, they demonstrate that their approach is superior to existing methods.…”