While most digital marketers are intimately familiar with using recommendations to promote products and other content, it’s important to look at what makes for effective—or ineffective—recommendations strategies.

Successful recommendations that deliver real, measured returns are built on understanding how products are related to each other in various ways, versus just looking for products that are categorically similar. This means making recommendations based on many contextual factors, from a person’s past buying history to where they are from to what items they are viewing in their current session.

Marketers need a system that can make powerful recommendations based on: 

Contextual similarity.

When solving contextual similarity problems, a recommendations engine should take less obvious relationships into account: Do the two products belong to the same brand? Are they in the same price range? Do they have similar ratings from similar users? These types of decisions are best made by a search engine that can quickly determine contextual similarity between semi-structured data objects.

Affinity between items.

Products can also be “linked” by users’ browsing and purchase behavior. Consider the classic association-rule example of beer and diapers: during certain times of the day, male shoppers tend to buy them together, even though on the surface, the items are completely unrelated. Valuable insights can be gained from uncovering these unexpected associations. Understanding the affinity of one item to another based on observed user behavior can be a significant analytics challenge—especially when you’re dealing with massive scale—and requires the use of multiple algorithms.

Rich data.

Your recommendations will be much more powerful if the corresponding user data is rich and detailed. The more demographic and behavioral information that can be associated with a user profile, the better, since the recommendations engine will be better equipped to draw the right conclusion about what product suggestions a user is most likely to respond to.

In part II of our discussion on Getting the Most from Recommendations, we’ll take a deeper dive into leveraging machine learning in recommendations to increase conversion and boost revenue.