Alexandre Robicquet: In short, a personalized recommendation platform should leverage on-site actions, like clicks, to deliver perceptive, tailored product suggestions to a user. The most advanced platforms are able to accomplish this kind of recommendation without the use of third-party cookies or personal identifiable information (PII). I call this strategy “behavior-based recommendations,” and it helps drive online discovery and engagement. The technology is simple to understand — but it packs a real punch for retailers.
How does it work in practice? Let’s say you’re shopping online for a pair of jeans. On the website’s homepage, you click on the “jeans” tab, filter by the style you’re looking for, and start browsing. A non-personalized recommendation system might show you a carousel with jeans simply titled “trending now” — which only tells you what other users are looking for, or what the company wants to push the most. But a truly personalized recommendation system is able to analyze each click you made on the website, the amount of time you spent looking at a specific pair, and other on-site behaviors to provide you with ultra-tailored suggestions of what you’re actually looking for. A personalized recommendation platform can also go even further than non-personalized systems by customizing follow-up touchpoints like emails with recommendations tailor-made for each specific user.
In the end, both you and the business walk away satisfied. Plus, you feel more loyal to the brand, since you’ve been treated like a unique individual with unique tastes — just like you would in a brick-and-mortar store with the help of a friendly sales associate.
RL Pro: What can a tailored recommendation pipeline do for consumer engagement, conversion and retention metrics?