TY  - JOUR
A1  - Ahmadi, Iman
A1  - Abou Nabout, Nadia
A1  - Skiera, Bernd
A1  - Maleki, Elham
A1  - Fladenhofer, Johannes
T1  - Overwhelming targeting options: Selecting audience segments for online advertising
T2  - International Journal of Research in Marketing
N2  - Even as online advertising continues to grow, a central question remains: Who to target? Yet, advertisers know little about how to select from the hundreds of audience segments for targeting (and combinations thereof) for a profitable online advertising campaign. Utilizing insights from a field experiment on Facebook (Study 1), we develop a model that helps advertisers solve the cold-start problem of selecting audience segments for targeting. Our model enables advertisers to calculate the break-even performance of an audience segment to make a targeted ad campaign at least as profitable as an untargeted one. Advertisers can use this novel model to decide whether to test specific audience segments in their campaigns (e.g., in randomized controlled trials). We apply our model to data from the Spotify ad platform to study the profitability of different audience segments (Study 2). Approximately half of those audience segments require the click-through rate to double compared to an untargeted campaign, which is unrealistically high for most ad campaigns. Our model also shows that narrow segments require a lift that is likely not attainable, specifically when the data quality of these segments is poor. We confirm this theoretical finding in an empirical study (Study 3): A decrease in data quality due to Apple’s introduction of the App Tracking Transparency (ATT) framework more negatively affects the click-through rate of narrow (versus broad) audience segments.
KW  - Targeting
KW  - Audience Segments
KW  - Online Advertising
KW  - Third-Party Data
KW  - Facebook
KW  - Spotify
KW  - Apple
KW  - App Tracking Transparency Framework
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/79086
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-790865
SN  - 0167-8116
VL  - 2023
IS  - In Press, Corrected Proof
PB  - Elsevier
CY  - Amsterdam
ER  -