Analytical Model Helps eMarketers Reach Mobile Devices Related to You and Those Like You: INFORMS Journal
CATONSVILLE, MD, November 17, 2015 – Researchers have found a privacy-friendly way to connect the same and similar mobile users based on examining location visitation data, thus allowing e-marketers to target the smart phones and mobile devices of the same person and of those with related interests, according to a new study published in Information Systems Research, a journal of the Institute for Operations Research and the Management Sciences (INFORMS).
The study was recently honored as the INFORMS President’s Pick by L. Robin Keller, University of California, Irvine.
Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design is by Foster Provost, New York University Stern School of Business; David Martens, University of Antwerp; and Alan Murray, Coriolis Labs.
The authors investigate “geosimilarity”—the similarity of instances of consumers based on the distribution of the locations they have been observed to visit. Two users are similar, and thereby connected in a geosimilarity network, when they share at least one visited location. Users are more similar as they visit more shared locations and locations that are less frequently visited.
The authors address current data limitations that make mobile marketing more difficult than online marketing.
“Mobile ad targeting could be based on context, demographics, or psychographics, if such data were available,” the authors write. Because such data are largely unavailable on mobile consumers in the advertising ecosystem, ad targeting firms increasingly are looking to other means of finding suitable candidates for ads.
Also, the authors note, mobile advertisers need to deal effectively with the problem of “consumer fragmentation.” Individuals are observed only through the multiple information systems that comprise the digital advertising ecosystem. As a result, for marketers, an individual may correspond to various “user instances.” An individual may have different instances because she is observed on different devices, such as mobile phones, tablets, laptops, and PC’s.
The authors note that geosimilarity can also be used for privacy-friendly “hyperlocal” targeting: targeting people who frequent a precise location without needing to store data on the actual locations of the users. For example, consider a coupon campaign for the local corner restaurant. Targeting the geosimilarity neighbors of existing loyal customers can anonymously send coupons both to the existing customers themselves on their various devices, and also to others who frequent the same (anonymized) locations as the loyal customers—such as people who work in the same office.
Geosimilarity can be used for targeting a mobile audience by finding individuals who are closely linked to individuals already known to have desired characteristics such as prior purchase history, brand affinity (“liking” a brand or product, or clicking on an ad), or key demographics.
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