What are Behavior-Based Recommendations?

Today’s high-growth e-commerce businesses are facing a more chaotic retail landscape than ever before, as economic fluctuations, pandemic aftershocks, and a variety of other factors influence and alter typical consumer behavior. Still, one thing is certain: demand for high-quality online experiences isn’t going anywhere—and personalization is the key. But not all types of “personalization” are built the same way, nor will they lead to the same results for either the business or the consumer.

Retailers with hundreds or thousands of products have a great challenge: to surface the most relevant items for each customer. The consequence of not proactively helping customers find relevant products is that they churn and abandon your store without finding something they like. Effective personalization fuels the difference between e-commerce stores that triumph and those that get left behind. Personalization is a critical part of securing a dependably good customer experience (CX) that inspires loyalty and keeps customers coming back for more. According to Epsilon, 80% of customers are more likely to purchase from a brand that provides a personalized experience, and Gartner reports that brands can lose up to 38% of customers when they fail to personalize effectively. Behavior-based recommendations are the only method of personalization that can help storefronts successfully engage consumers with items they’re interested in, whether they’re a long-time customer or someone who’s just landed on your site for the very first time.

Behavior based-recommendations are suggestions powered by artificial intelligence (AI) that leverage user browsing behavior to provide highly accurate recommendations without cookies or personally identifiable information (PII). This enables businesses to provide personalized experiences even for new and unknown users, which normally account for a major percentage of all traffic. The behavior-based recommendation method allows for that highly tailored experience in just a matter of seconds because it prioritizes the importance of clicks and on-site actions over things like age, gender, location, or other PII. This gives retailers an opportunity to convert visitors even without the luxury of a person’s previous browsing history or past purchases. In fact, an estimated 68% of users on a given e-commerce site are first-time visitors, and 98% are anonymous or unknown. That means that without leveraging behavior-based recommendations, retailers are more likely to lose out on conversions due to a less-than-personalized CX.

Behavior-based recommendations are different from other recommender systems.
The differences between behavior-based recommendations and other forms of recommendation

Behavior-based recommendations vs. other recommender systems

There’s a pervasive myth within the e-commerce space that all recommendations are personalized. However, there are actually differing levels of personalization depending on the method being used by a given recommender system.

Trending or “most popular” recommendations

One of the most popular forms of recommendation, displaying “trending” or “popular” items aren’t serving up personalized recommendations so much as they are making an assumption that everyone will want to purchase the same items. Alternatively, these recommendations could simply be a reflection of what the company wants to sell most. Though it may be statistically more likely that a popular item will be more enticing to any given user, it isn’t the best example of personalizing a shopping experience based on someone’s taste.

Recently viewed or purchased recommendations

If a site visitor is returning or a previous customer, the business’ recommendation system may have previously captured data about the consumer in order to make better recommendations. With this information, a business is able to achieve a moderate level of personalization by suggesting, for example, that a user views or buys the same product again, or that they browse related items and categories. While this tactic can be somewhat successful, it relies heavily on cookie-based technology that is beginning to fall out of favor in tech spaces, due to consumer security concerns. It also fails to take into consideration new or anonymous users at all, meaning these users won’t get any sort of personalization until they’ve done something like made a purchase or saved an item to the cart.

Cookie-based “AI” recommendations

The closest use of AI in standard recommendations are those made from the use of cookies, or tracking the PII of consumers. With this information, a recommender system can group potential shoppers into broad, often stereotype-based categories built from their PII to serve them recommended items. This method relies primarily on assumptions about what a person of a particular age, for example, would like, rather than the interests of a unique user. It is also contingent on a user having previously interacted with the website in some way, meaning it is not a feasible option for making recommendations to new or anonymous users.

Behavior-based recommendations

Behavior-based recommendations are the newest and most effective way to show relevant products to each customer based on their interests. Rather than using rudimentary techniques like cookies and demographic targeting, behavior-based recommendations leverage onsite actions to create a deep understanding about each user's interests. This valuable information about taste and preferences allows retailers and marketplaces to show relevant products to each user moments into their first visit.

Click me

Focusing on what customers want, not who they are

While the current status of recommendation technology tends to be “one-size-fits-all,” it should really have the ability to adapt and meet the unique needs and goals of each business.

Just as no two businesses are the same, no two customers are the same, which makes relying on PII to ascertain shopping preferences ineffective. In order to have the desired effect of sparking customers’ genuine interests, algorithms must transition to a behavior-based recommendations model. 

Understanding the uniqueness of each customer is the crux of delivering behavior-based recommendations, which prioritize on-site actions and clicks rather than customers’ PII. Think of it as helping to set up two of your friends on a blind date. You wouldn’t pair the two of them based only on demographic data, but rather based on complementary personality traits and shared interests. The behavior-based approach analyzes real-time behaviors, rather than demographics, to ensure the best possible chemistry results between customer and product, and ultimately customer and brand. 

Why are behavior-based recommendations so effective?

For retailers scaling their e-commerce presence, behavior-based recommendations can be transformational for sales and customer satisfaction. While the e-commerce space has no shortage of recommendation solutions available to businesses, they all rely heavily on third-party cookies and the purported value of a consumer’s PII. 

Not only are cookie-based recommendations invasive to consumers, they’ve also proven to be less than ideal when it comes to conversion. We now live in a world where consumers can feel confident that their privacy isn’t being violated, still get served the most relevant items or content, and also lead to better outcomes for sellers. For example, businesses using Crossing Minds’ recommendation platform see an increase of 96% in sales and 120% in click-through rates. 

Click me