Not All Recommendation Engines Are the Same

AI & Machine Learning|[wtr-time]

Not All Recommendation Engines Are the Same

Many brands are investing in recommendation engines: tools that drive personalized information to existing and potential customers. However, many of their efforts fall short.

According to Adobe, 60% of marketers struggle to personalize content in real time.

And yet, 77% of these marketers think real-time personalization is vital for their businesses.

Most marketers provide recommendations based on the content or products that receive the most clicks or views across their entire audience or very large segments of their total audience, rather than making suggestions based on each individual’s interests.

This is essentially the difference between Amazon’s pretty bad recommendations and Netflix’s pretty-solid ones, which are cited as the gold standard for tailored suggestions.

(Don’t even get us started on the almost ubiquitous “around-the-web” recommendations.)

What a good recommendation engine looks like for companies today

The best recommendation engines deliver results against business priorities based on:

  • 1) Individual customer attributes, including her interests, preferences, and behaviors
  • 2) Inventory, based on available content and product inventory and key areas of focus

Traditional recommendation engines don’t have access to all of the first-party data required to use all of a customer’s attributes as inputs, and legacy marketing databases don’t have the delivery capability to feed all of the data to a recommendation engine in real time.

That’s why BlueConic offers a solution that can do both.

recommendation engines

A product recommendation engine for better personalized marketing

Using machine learning, BlueConic’s Recommendations Engine combines individual profile data to deliver recommendations that not only align with the customer’s interests, preferences, and behaviors, but also with your content or product priorities.

There are basically three phases for using using a recommendation engine like BlueConic’s:

  • 1) Exploration phase: It’s important to try some different variants with different algorithms to quickly test and learn what is the ideal algorithm combination to start with.
  • 2) Exploitation phase: During this phase, use what you learned during the exploration phase to get the maximum amount of value out of BlueConic’s Recommendations Engine. This means turning off all variants except the best performing one.
  • 3) Continuous improvement phase: Once you’ve figured out the best algorithm combination across a large audience, it’s time to start thinking about testing algorithm combinations for specific audience segments, and then repeating the process again.

As with countless other components of your digital marketing, routine testing and refining are critical to your success with our Recommendations Engine (or any other, for that matter).

To succeed with this granular level of personalization, you must:

  • Account for individual interests and preferences as well as inventory and brand priorities
  • Offer the flexibility to appear at the right moment without specific placement restrictions
  • Support different algorithm combinations for personalized placements across channels
  • Give your team full control over the algorithms to test and optimize as often as you want

Once you have these processes down pat, you’ll see the fruits of your personalized recommendation labor and really start to gain useful insights into your customers’ behaviors.

Learn how our CDP can help you deliver personalized recommendations and experiences to your prospects and customers. Request a BlueConic demo today.


See what BlueConic can do for you

Whether you’re looking for operational efficiencies or improved marketing effectiveness through data activation, our customer data platform can help.