Remarketing seems deceptively simple. It’s a smart way for brands to reconnect with consumers who may have visited a website, but not necessarily pulled the trigger to make a purchase. It also allows you to position targeted ads to an audience who has previously expressed interest in products on your website, while they are simultaneously browsing the rest of the internet.
On top of that, it offers perfect targeting, where every single user can be lined up against a value proposition that needs to be delivered to them. Not only are the conversion payoffs high through this strategy, but the prevalence of second price auctions on exchanges ensures that both win rates and margins are healthy.
At first glance, it doesn’t seem like artificial intelligence (AI) or advanced machine learning (ML) techniques would have a major role to play here. Surprisingly, this is far from the reality.
Machine learning is a key ingredient involved in making remarketing effective. AI and ML solutions are often required when one encounters large scale, highly fragmented structures in very dynamic environments. This is especially true in the case of remarketing on both desktop and mobile. As such, there are four key ways in which AI and ML can be leveraged to make remarketing effective.
1) Needle in the haystack
The problem with perfect targeting is scale. If an advertiser has a named list of users, then it becomes imperative to find as many of them in all the places in the ecosystem where they might show up. In practical terms, this means having a seat at all the ad exchanges that could potentially get requests from mobile apps these users are running, and may involve looking up to tens of billions of ad requests every day.
Furthermore, this also means processing all that data on a regular basis. Not surprisingly, this can become very expensive, very quickly. Machine learning comes to the rescue in this situation by modelling where the targeted users are likely to appear. This reduces the number of requests that need to be examined, bringing it down from 100,000 requests-per-second, to a more manageable 1,000 requests-per-second. Typical techniques…
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