When done right, personalized content recommendations can reduce your bounce rate, stimulate conversions and increase your website’s overall engagement metrics. How do you craft a personalized content experience for every visitor? Doing this manually is practically impossible. Thankfully, we can count on personalization engines these days.
A recommendation engine for personalized content enables you to serve up the most relevant information for each user. To get started, you’ll need data, a clear understanding of your audience groups (or segments) and, as a best practice, pre-defined goals.
How do content recommendation engines work?
Personalized content recommendation engines typically rely on machine learning algorithms to dynamically serve up the most relevant types of content to each individual. Evergage does a great job at explaining how these algorithms work (see chapter 2 of their ebook).
In general, we see two types of algorithms in use: basic and advanced. One example of a basic algorithm in use is when you land on a publisher’s site and see the most recently published content. But marketers can get a lot more nuanced with advanced algorithms these days. For instance, collaborative filtering algorithms leverage data from a visitor’s engagement with different items to place them in a group of people with similar tastes. To put it simply, the visitor would then see items that fall into this taste categorization. Netflix is known for using this technique.
Before you get bored by the tech speak, let’s take a look at how you can put this into practice.
Which personalization engine is right for you?
There’s a wide range of vendors out there. Some offer sophisticated automation and analytics features at a fairly high price point. Others require a bit more manual work up-front.
We analyzed the top personalized content recommendation engines by:
- Implementation time and requirements
- Range of features offered
- Level of support
- Degree of expertise needed to use it