Mobile advertisement campaigns for boosting in-store visits: A design framework and case study

Brick-and-mortar retailers seek higher foot traffic in their stores to improve their sales opportunities. In this quest, location-based advertising on mobile devices has emerged as an important marketing tool for targeting potential customers. The design of such advertising campaigns is complex, and their effectiveness depends on the ability to collect and examine data that aids in targeting the right customers at the right time and place. We develop a campaign design framework that explicitly accounts for the costs of acquiring and utilizing targeting data and the heterogeneous effects of such data in affecting the performance outcomes of mobile advertising campaigns. We illustrate the application of our campaign design framework through a real-world case study of a mobile advertising campaign undertaken by a large global retail firm. Our findings suggest that the optimal set of attributes to use for effectively targeting the potential customers of a brick-and-mortar retail store varies with the distance between the customers' current locations and that of the store. As a result, mobile campaign design approaches that utilize all or a naive subset of data attributes for targeted advertising yield lower levels of return on investments, relative to our proposed approach. Based on our results, we discuss implications for the design and deployment of mobile advertising campaigns and for further research on targeted advertising.

Automated summary

Physical retailers use mobile location-based advertising to attract potential customers to improve their sales. We propose a campaign design framework that considers the cost and effects of targeting data on the performance of mobile advertising campaigns. Our case study suggests that the optimal attributes for targeting potential customers of a retail store vary based on their distance from the store, and using all or a naive subset of data attributes for targeted advertising results in lower return on investments compared to our proposed approach. Our findings provide implications for the design and deployment of mobile advertising campaigns and further research on targeted advertising.