If you go to any website, or online store specifically, your behaviour is tracked. Landing page, time spent, clicks, exit page: you name it, it is tracked. But even when you leave a page, a company does not really leave you: they saw what you clicked on, and based on your browsing behaviour, they retarget you: they show you a (sometimes personalized) advertisement on another channel, hoping you will come back and purchase the product you viewed.
Retargeting can either be done during or after a website visit, and is done based on a customer’s visit. When showing personalized recommendations, for example, it is important to take into account the quality of the recommendation, the level of personalization and the timing. This is what Bleier & Eisenbeiss (2015) looked at: what should they show, when should they show it, and where should they show it.
As with any academic article, past literature is analysed and hypothesis are developed. In order to test the what, when and where of personalized online advertising effectiveness, Bleier & Eisenbeiss conduct two large-scale field experiments and two lab-experiments. The first field experiment looked at the interplay of degree of content personalization(DCP), state, and the time that has passed since the last online store visit, at a large fashion and sports goods retailer, who carries over 30000 products. The second field experiment, conducted at the same retailer, looked at the interplay of placement and personalization. Based on the results from these two field experiments, two lab experiments were designed: one focussing on web browsing in an experiential model, the other focussing on goal-direct web browsing.
Within this paper, thus, many things are studied and confirmed. The papers shows the importance of how to determine the effectiveness of online personalization’s, and which one works best when. When a customer sees a personalized ad right after his/her website visit, the ad becomes more effective. This is mainly because preferences are not constant: they can over time. Thus if you liked a shirt 5 minutes ago, you will mostly still like it now. Thus if a company is able to directly respond to a consumer’s behaviour, the CTR is expected to be higher.
While the effectiveness of recommendations decreases over time, the level of personalization plays a moderating role. This means that high-level personalization in later stages of the decision making process have lower effectiveness, because of changes in customers tastes’ and preferences. The personalized ad is therefore not applicable anymore. Thus, the more personalized an ad, the sooner after a website visit it should be sent. Moderate personalized ads are thus more effective over time, as they take into account these changes in preferences. As visual recommendations are often highly personalized, these type of recommendations are more relevant shortly after a visit. Cross-sell recommendation, which is a more moderate recommendation type, performs better later in time
So what does this all mean? When retargeting customers and showing them personalized ads, it is important to keep in mind how long ago they visited a website. Given that this research was performed at a large fashion/sports retailer, it would be interesting to see whether the same conclusions hold for other settings. What do you think? And when do you consider (personalized) ads target to you most effective?
Bleier, A., & Eisenbeiss, M. (2015). Personalized Online Adverstising Effectiveness: The Interplay of What, When, and Where. Marketing Science, 669-688.