Further reading section

Order and naming
In one of the earliest editions of this newsletter we looked over all of the elements that surround an article. Among them: the “Recommended reads” section. We concluded that it was best to keep these below the article so it won’t distract the reader.

But there’s some intricacies related to these as well.

First, how do we name the section?

Each of these names gives a slightly different idea to readers. For instance, “What to read next”, “Keep reading”, and “Related content” clearly show that the posts under the section are general. On the other hand, “Hand-picked related articles” and “You might also enjoy” imply that the company has chosen which articles they want displayed.

So before naming the section with a random pick, think about what you want to display. Do you just want to list the latest posts to grow them? Do you want to build more traffic for your most popular articles? Or do you want to recommend articles based on what a user is currently reading and previous posts they were interested in?

Second, what’s the order of these posts?

Most websites have their CMS randomly choose the posts — either because they are the most recent ones, the most popular, or have the same category/tag.
  1. Your first option is to display the same featured articles on all blog posts. The problem with this approach is that when you’re reading an article from those selected picks, you’ll get the same article recommended on the side. Here’s an example.
  2. The second solution is to list articles from within the same category. Check this example on the Groove blog. Similarly, you can work with any metric and display the most visited articles within a category, the most shared ones, or just order them by publishing date.
  3. The third and best [although time-consuming] solution is to manually choose what you want to display. Look through some of the Kinsta posts to see some good examples. With this approach, you take every article on your blog and mention roughly three other recommendations you want below. You can opt to promote a big piece of content like an ebook or guide on all of them and manually select the two other posts. Another way of doing this is to select one piece of content you want on all posts and then make aleatory recommendations from within the same category.
  4. Finally, if you want to get super accurate, turn to machine learning to put together a recommender system just like you would for an ecommerce store. We could talk for days on this topic so I’m leaving some good reads below.
Further reading: But did you know you can handpick these to truly reflect content with the same theme and purpose?

Also, keep in mind you don’t always need this section. If you want people to focus solely on the post and maybe one CTA to your product, you can ditch it like Ahrefs and Unbounce do.
If you enjoyed this edition, don't forget to send it to a friend!

Until next time,
Alexandra Cote


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