Start Date
12-13-2015
Description
We study the effectiveness of different types of mobile recommendations and their impact on consumers' utility and product demand, using a privacy-preserving econometric analysis. We find that an increase by 10% in the number of recommendations raises demand by about 6.7%. Our findings highlight the importance of "in-the-moment" marketing. In particular, trending recommendations that provide "in-the-moment" content have a much stronger effect compared to traditional recommendations. This effect is stable across various levels of popularity, whereas traditional recommendations contribute to the rich-get-richer phenomenon. We also examine various moderating effects; for instance, we find that recommendations based on just the novelty of the alternative do not have a significant effect but novel alternatives accrue greater benefits from recommendations when the quality of the item is also taken into consideration. We validate the robustness of our findings using instruments based on a deep learning model as well as different treatment-effects estimators.
Recommended Citation
Adamopoulos, Panagiotis and Tuzhilin, Alexander, "The Business Value of Recommendations: A Privacy-Preserving Econometric Analysis" (2015). ICIS 2015 Proceedings. 12.
https://aisel.aisnet.org/icis2015/proceedings/GeneralIS/12
The Business Value of Recommendations: A Privacy-Preserving Econometric Analysis
We study the effectiveness of different types of mobile recommendations and their impact on consumers' utility and product demand, using a privacy-preserving econometric analysis. We find that an increase by 10% in the number of recommendations raises demand by about 6.7%. Our findings highlight the importance of "in-the-moment" marketing. In particular, trending recommendations that provide "in-the-moment" content have a much stronger effect compared to traditional recommendations. This effect is stable across various levels of popularity, whereas traditional recommendations contribute to the rich-get-richer phenomenon. We also examine various moderating effects; for instance, we find that recommendations based on just the novelty of the alternative do not have a significant effect but novel alternatives accrue greater benefits from recommendations when the quality of the item is also taken into consideration. We validate the robustness of our findings using instruments based on a deep learning model as well as different treatment-effects estimators.