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