Predicting Mobile Advertising Response Using Consumer Co-Location Networks

Peter Pal Zubcsek, Zsolt Katona, & Miklos Sarvary
Academic
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Key Takeaways

​What? Targeting mobile coupons by distance to stores has been confirmed to be quite effective in certain contexts.

So What? Consumers who go to similar places may exhibit commonalities in their tastes.

Now What? Marketers can monetize the location data collected via smartphone apps by connecting consumers who appear at the same place at approximately the same time.

​Article Snapshot: Executive Summaries from the Journal of Marketing

Consumers' dynamic location patterns are informative about their product preferences, and marketers can monetize the location data collected via smartphone apps by constructing "co-location networks" that connect consumers who appear at the same place at approximately the same time.


We discover a significant positive link between the co-location of consumers and their response to coupon-based promotions, even after controlling for coupon characteristics, demographic and psychographic differences, referral network effects and unobserved individual heterogeneity.

We find that variables derived from consumers’ location history may be more effective predictors of their purchase behavior than traditional variables.


Research Question

Mobile op. ran in-app mobile ads to study if consumers' location histories are informative of their preferences. Existing location-based (LB) methods partition a map into contiguous regions & segment the market based on consumers' observed locations. Even the most popular LB mobile marketing method, geo-fencing, maps subs' current location dynamically onto the _static partitioning of regions. Such methods ignore subs far away from the store, leaving money on the table. Consumers who attend the same venue have correlated preferences, & "co-location networks" can capture this.

Methods

The authors study mobile advertising response in a panel of 217 subscribers. The data set spans three months during which participants were sent mobile coupons from retailers in various product categories through a smartphone application. The data contain coupon conversions, demographic and psychographic information, and information on the hourly GPS location of participants and on their social ties in the form of referrals.

Findings

The authors find a significant positive relationship between colocated consumers’ response to coupons in the same product category. In addition, they show that incorporating consumers’ location information can increase the accuracy of predicting the most likely conversions by 19%. These findings have important practical implications for marketers engaging in the fast-growing location-based mobile advertising industry.

Implications

Consumers' movement patterns may indicate their preferences in various product categories. Co-location events, when two consumers are at the same place at about the same time, therefore indicate similarities of preferences. Marketers can monetize this information by constructing dynamically evolving consumer co-location networks to capture the similarities in consumer preferences. Co-location events explain preference similarities over and above what demographic, psychographic, and social relationship variables say about consumers.

Questions for the Classroom

  • Is excess supply (e.g., Starbucks during off-peak hours) the only good reason to use mobile push advertising?

  • Do price-sensitivities differ inside and outside the fence for the geo-fencing method? [note: there is ample evidence in the literature documenting this increased promotion-sensitivity of closer-by prospects]

  • What about consumers "not passing by?" Could they be valuable to target with promotional offers?


Article Citation: Peter Pal Zubcsek, Zsolt Katona, and Miklos Sarvary (2017) Predicting Mobile Advertising Response Using Consumer Colocation Networks. Journal of Marketing: July 2017, Vol. 81, No. 4, pp. 109-126.

doi: http://dx.doi.org/10.1509/jm.15.0215



Author Bio:

 
Peter Pal Zubcsek, Zsolt Katona, & Miklos Sarvary
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