This article is an excerpt from the Pearson Addison-Wesley book "Pragmatic AI" by Noah Gift. Reprinted here with permission from Pearson and 2019. What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage.
Customer experience personalization is all about data first. Get the data right and you can shape the overall customer experience by applying data science and machine learning. Recommendation engines are very powerful personalization tools because it's a great way to do "discovery" – showing people items they will like, but are unlikely to discover by themselves. They improve a visitor's experience by offering relevant items at the right time and on the right page. In the immortal words of Steve Jobs - "a lot of times, people don't know what they want until you show it to them."
The recommendation engine is one of the biggest martech innovations of the last few years, and it's shaping our entire digital experience. For example, let's say I visit the Harvard Business Review website and read about manufacturing marketing tactics (as one does). When I fire up Netflix, I know that my experience is going to be managed in part because Netflix knows I've been binging on the show Haven and it's going to suggest other supernatural crime fighting series. The development of recommendation engine technology has brought digital personalization to a whole new level. For brands developing content marketing and martech strategies, it's critical to pay attention to this emerging technology and understand where trends are headed when shaping the user experience.
Many years ago (don't ask me how I know this!) the hamburger chain Burger King began branding themselves with this slogan: "Have it your way!" It was pure marketing genius! The idea that you could order something, in this case a hamburger, at a fast food dispensary that would be tailor-made to your specific personal tastes was revolutionary – it set them apart from their competitors. Something similar happened when Amazon.com, one of the first major online stores in the Internet era, began suggesting books (and other products) to their customers that were an amazingly good match to each individual's personal tastes. Of course, Amazon accommodated its customers with this value-added service by invoking a scientific procedure, data science applied to customer data, not by asking customers directly (as did Burger King).