Online-to-offline advertisements as field experiments

Abstract

Online advertisements have become one of today’s most widely used tools for enhancing businesses partly because of their compatibility with A/B testing. A/B testing allows sellers to find effective advertisement strategie,s such as ad creatives or segmentations. Even though several studies propose a technique to maximize the effect of an advertisement, there is insufficient comprehension of the customers’ offline shopping behavior invited by the online advertisements. Herein, we study the difference in offline behavior between customers who received online advertisements and regular customers (i.e., the customers visits the target shop voluntary), and the duration of this difference. We analyze approximately three thousand users’ offline behavior with their 23.5 million location records through 31 A/B testings. We first demonstrate the externality that customers with advertisements traverse larger areas than those without advertisements, and this spatial difference lasts several days after their shopping day. We then find a long-run effect of this externality of advertising that a certain portion of the customers invited to the offline shops revisit these shops. Finally, based on this revisit effect findings, we utilize a causal machine learning model to propose a marketing strategy to maximize the revisit ratio. Our results suggest that advertisements draw customers who have different behavior traits from regular customers. This study demonstrates that a simple analysis may underrate the effects of advertisements on businesses, and an analysis considering externality can attract potentially valuable customers.

Effects of Advertising, Online Risk, Perceived Usefulness, and Reliability on Online Shopping Behavior

“…According to Matsui and Moriwaki (2022), there is still inadequate knowledge about customers' shopping behavior. In Nigeria, online shopping has become progressively common (Usman, Kumar, & Ibrahim, 2019), particularly among middle-income earners, elites, technocrats, and students (Adamkolo, Hassan, & Pate, 2018;Ibrahim, Hassan, & Yusuf, 2019).…”