Marketing Success: How The Right Content Recommendation Engine Can Increase Your User Engagements

As promised last week, today we’d be talking about a somewhat unknown catalyst you could use in sparking up user engagements and interactions on your website.

So in case, you missed last week’s post, here’s the link again benefit of machine learning systems.

Give it a read. I’ll be here.

Back already?

Let’s dig in, shall we?

Recommendation Engine

A recommendation engine is also known as recommender systems. The terms are used interchangeably.

So what are recommendation systems anyway? It’s a system that analyzes your activity, data, and profile. And recommends products and/or content that fit within this profile. For example, the people you may know feature on your Facebook profile is an example of a recommender system at work.

At other times, recommender systems come to your rescue when you aren’t really sure of what you want or like, but you only have a vague idea of what it is. And helps you locate items you might prefer.

For example, a customer intends to look up a product on your site, but she doesn’t know which brand or specifications she’d prefer.

So, she types a search term in your online store; the results come in and your recommender system also delivers related products that closely match her initial search query.

The idea here is to narrow down her options, and present options she might choose.

AI in recommendation engines

Types of Recommender Systems

I. Simple Recommendation Engine

A simple recommender system offers generalized recommendations to every user. And it is usually based on metrics such as trends, popularity, genre and user location. Click To Tweet

What this means is that popular events and/or products trending in your user’s location will have a greater possibility of being shown to the viewer.

II. Content-Based Recommendation System

The content-based recommendation engine provides recommendations based around a particular item.

This system applies metadata such as:
content, tag, category, description etc that you previously engaged with to make suggestions.

The concept is simple, if you liked item A, it is likely that you will also like item A1, which is similar to item A. And so on and so forth.

III. Collaborative Recommender Engine

The collaborative recommender system tries to predict the rating or preference that you would give an item or in this case, a movie.

It includes your past ratings. And it also takes individual preferences and rating from other users into consideration.

Finally, it predicts how likely it is for you to choose a set of items over another. And then recommends the top items to you.

One of the advantages of this recommender engine is it does not require item metadata like its content-based counterpart.

IV. Hybrid Recommenders

Hybrid recommenders are a combination of simple, content and collaborative recommender engines.

It harnesses the strengths of these engines, to provide more accurate results. While reducing their individual weaknesses.

Consequently, each method is combined into one powerful hybrid model.

How Recommendation Engines Work

  1. Recommender systems are powered by data. They work by making use of data they glean from your CRM, and they also use the background data you provide.
  2. The above follows the information that the user also provides to the system in order for it to generate a recommendation. And…
  3. An algorithm that combines ( I ) and ( II ) above to arrive at its suggestions. (Source: DataCamp)

How to Set up a Recommendation System

To make recommendation systems work, you need the following:

  1. A large number of historical transactions on your website.
  2. In-depth data of your user’s behavior on other websites. (You’d require the services of 3rd party data providers).
  3. A cloud-based recommendation system provider (Vendor).

There are lots of vendors to choose from. However, I recommend you use your live data to test the effectiveness of each recommendation engine.

I also suggest you gather and use relevant customer data in your company.

You can also expand your customer data with 3rd party data providers.

For example, if a customer of yours has been looking for iPhone covers on another website. Why shouldn’t you show them one when they visit your website? 3rd party data providers can help you with this information.

Selecting a Recommendation Software Vendor

When selecting a vendor, here are a few things to consider:

  1. Consider the vendor’s overall offering and how flexible the recommendations are. Especially if you want to use them on multiple locations, in-store, newsletters, email, push notification, or on promoted content.
  2. Removal of repeated recommendation for a specific user.
  3. Their ability to filter recommendations based on your upsell and cross-sell strategies.

Conclusion

When used properly, AI-powered recommender systems can help you spark up user engagements and interactions on your website.

If you enjoyed this post, I’d really appreciate it if you’d take a second to share it with your friends on social media.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.