What are personalized recommendations?
Whether you’re reading news, watching videos on YouTube, or shopping on Amazon, you’ve probably noticed rectangular boxes, pop-ups, or side menus at the end of a page that suggest content you should look at next. Those content recommendations come in different shapes and have different goals, depending on the type of site you’re on.
On media sites, news sites and blogs, typical content recommendations come in the form of banners at the end of an article, or pop-ups suggesting the next article or video you should read or watch.
The goal for media sites is to try to guess what you are interested in and get you to read more articles and/or watch more videos. The more articles you read, the more pages you load – and the more ads that can be displayed. This makes sense, as one common business model of media sites (also known as publishers) is to get paid on CPM – cost per 1,000 ad impressions.
Therefore, the critical question every media site should be asking is: "What’s the best article recommendation we can give to this specific user on the specific page she or he is currently looking at, in order to maximize our chances of the user reading another article?"
On online shops, the challenge is slightly different compared to media sites. Not only do you want to increase the number of product pages that each visitor sees, but you also want each visitor to discover products she or he will like and ultimately buy one of them. A visitor rarely comes to an online shop with the strong intent of buying a product: the typical conversion rate of online shops is only between 1–3%. In addition, most visitors don't stay for long; they look at two or three product pages, and then leave. It is therefore crucial that the first page visitors see is as effective as possible in guiding them to a product they will ultimately buy.
Brick-and-mortar stores face the same type of challenge. In order to increase their customers' average cart value, stores attempt to make data-driven decisions regarding which products to place next to each other. Based on receipts, they can understand which products are bought together and optimize product placement accordingly.
This lecture by Alan Penn of the UCL Bartlett School of Architecture, and the following picture, illustrate how much effort is put in the design (or shall we say "maze"?) of Ikea stores.
What a brick-and-mortar store cannot do is rearrange its whole shop in order to fit the tastes of each individual shopper, even if it knew each shopper very well. However, online shops can do this, with personalized product recommendations.
Amazon, a pioneer of personalized product recommendations, can be regarded as the online equivalent of Ikea in this respect. Personalized product recommendations are displayed throughout the site. Indeed, personalized recommendations have been a vital part of Amazon's success story. According to a McKinsey retail report, “35 percent of what consumers purchase on Amazon... come from product recommendations.”
This information agrees with our own observations that personalized recommendations have an overwhelmingly positive effect.
How useful are personalized recommendations?
Obviously, the performance numbers vary between customer profiles (number and type of products, traffic), but here are some findings which are a general indicator across a number of shops I’ve worked with.
- Visitors who click on recommendations are better customers: they visit more product pages, put a higher number of products into their carts, and are more likely to come back.
- Visitors who never click on recommendations often visit just one product page, whereas users who do click on recommendations typically visit between 3–6 product pages. These numbers confirm that the recommendations must have been perceived as interesting by the visitors.
This effect of recommendation systems is well established and has been observed by others on media sites, among other examples.
Visitors who click on recommendations are also more likely to add more products to their cart: between 20–90% more, depending on the type of products the shop is selling (the lower the price of the product, the higher the increase).
How are personalized recommendations generated?
Giving good recommendations is not easy. This is because not only is each visitor unique with specific interests, but also because each recommendation has to be served very quickly (within a few tens of milliseconds) every time a new page is loaded. And this typically happens millions of times per day!
An additional difficulty is that the recommendation system should work well, no matter what kind of products an online shop sells; whether it’s movies, books, clothing, electronics, or toys.
Finally, the recommendations generated by the system should reflect actual visitor behavior on the site. It is always possible to define hand-crafted recommendation rules, but there is no a priori way of determining if these rules are effective or not. This problem can be solved by relying on actual visitor behavior and machine learning techniques which analyze and make predictions based on this data in real time. We’ll look at this further in a future blog post.
When trying to increase the conversion rate of your online shop, you should consider using personalized product recommendations. Marketing Platforms such as Selligent Marketing Cloud use anonymous visitor behavior data to understand the behavior of each visitor and make relevant recommendations.
The increase in conversion is only one of the direct benefits. Personalized product recommendations also help visitors navigate your site more comfortably and make them feel more valued.