The Rise of Personalization

Personalization is usually discussed as if it’s a recent invention, something that arrived alongside machine learning and big data. In reality the core idea, using what’s known about a specific customer to change what they’re shown, has a traceable history stretching back decades before personalization was a marketing buzzword.

Amazon’s early recommendation engine

Amazon began experimenting with automated product recommendations in the late 1990s, and by the early 2000s its engineers had published details of the item-to-item collaborative filtering approach that powered the “customers who bought this also bought” recommendations. The technique didn’t need to understand what a product was. It only needed to know which items were frequently purchased or viewed together across millions of past sessions, and it used that statistical co-occurrence to predict what an individual customer might want next. This remains one of the most widely cited examples of large-scale personalization built on behavioral data rather than manually written rules.

The Netflix Prize made recommendation algorithms a public sport

In 2006, Netflix launched an open competition offering a million dollar prize to whichever team could improve the accuracy of its recommendation algorithm by at least ten percent, using an anonymized dataset of real customer ratings. The competition ran until 2009 and drew thousands of teams worldwide, and while the winning approach was more complex than what Netflix ultimately deployed, the contest did more than almost anything else to turn recommendation and personalization techniques into a mainstream field of applied research rather than a niche concern of a handful of e-commerce companies.

Email personalization and the mail-merge era

Long before real-time recommendation engines existed, email marketing tools offered basic personalization through mail-merge style insertion, dropping a subscriber’s name or a past purchase into a template. It was crude compared to what came later, and it’s mostly remembered today as an example of superficial personalization, but it established the baseline expectation, that marketing could and should differ based on who was receiving it, that later, more substantive systems built on.

Mobile and real-time decisioning

As smartphones became the primary way people shopped and browsed during the 2010s, personalization shifted from something decided ahead of time in batches to something decided in real time, at the moment a page loads or a push notification is triggered. This required customer profiles to be queryable fast enough not to add noticeable delay, a meaningfully harder engineering problem than the overnight batch processing earlier recommendation systems used, and it’s a large part of why dedicated real-time personalization platforms took years longer to mature than basic product recommendation widgets did.

Where it arrived by 2021

By 2021, the techniques pioneered by Amazon’s collaborative filtering and refined through the Netflix Prize era had become table stakes across e-commerce, streaming and content platforms, built on infrastructure, cloud computing, unified customer data platforms and real-time decisioning, that simply didn’t exist when those original systems launched. The underlying idea hadn’t changed much since the late 1990s. What changed was how fast a system could act on it and how much of a customer’s behavior it could draw from to do so.