You’re probably familiar with a content recommendation engine in some shape or form. A content recommendation engine offers suggested content in specific areas on a webpage. The area, if personalized, is often labeled as “Recommended for you” or “You may be interested in.”
A content recommendation engine collects and analyzes data based on users’ behavior. This data is then used to offer personalized and relevant content or product recommendations. Predicting the preferences of the user gives site visitors a better and more effortless customer journey.
Large companies like Spotify and Amazon do this really well. These massive organizations have their own recommendation systems in place to boost revenue and customer loyalty.
Here’s an example: You’ve searched for toothbrushes on Amazon. Amazon understands that if you are searching for toothbrushes, you may likely be needing other related items like floss and toothpaste.
Amazon also has a “frequently bought together” section and a “customers who viewed this item also viewed” section. Both of these strategies continuously work to increase purchases.
While it may seem impossible to offer the same level of personalization as Amazon, think again. Implementing a personalization engine that leverages machine learning allows businesses of all sizes to take their personalization strategy to the next level.
If you aren’t already implementing a recommendation engine, you’re missing out. Companies who dedicate a portion of their resources to personalization strategies are more likely to see a greater payoff with their marketing efforts.
In 2018, 93% of businesses who implemented advanced personalization strategies increased their revenue. An additional 77% of businesses who exceeded revenue goals in 2018 had a documented personalization strategy in place.
Not All Recommendation Engines Are Created Equal
Some recommendation engines follow you around the web and track what you do on other sites. They may only suggest content based purely on your location, or your past interactions on the site.
A content recommendation engine may also rely entirely on manual inputs, personal information, or predefined rules. Using these strategies alone can cause your recommendations to become irrelevant and outdated.
Oftentimes, users may find that they can’t even relate to the content suggested because it’s inaccurate or misleading.
Recommending click-bait like content can easily damage a brand image and can deter visitors from revisiting your website. The good news is that there are better ways to recommend high-quality, relevant content.
How to Get Content Personalization Right
When websites feature quality content recommendations customized to their site visitors’ preferences, it’s a win-win situation for everyone involved. Site visitors will stay on your page for longer, naturally increasing conversions and page views.
But what makes a personalization engine worth investing in?
Here are five things to consider when choosing the right third-party solution:
1. Power your recommendation engine with real-time analytics:
To most effectively predict what people want, your information can’t be outdated. People can change their preferences in an instant, therefore the most predictive signal of intent comes from what the user is doing at that moment. That’s why real-time analytics is the most effective way that website owners can offer personalized recommendations.
A recommendation system powered by machine learning allows you to deliver personalized content recommendations to each visitor in real-time. In order to truly speak to your visitors one on one, your personalization engine must be able to constantly collect and analyze massive amounts of information for all users, not just for some.
While a person is browsing on the site, the system collects information based on behavior data. The more interactions a user has on a site, the more accurate and personalized the recommendations become.
2. Keep the users’ privacy in mind:
Using website recommendations to personalize the user experience is a tricky task. Personalizing too much can come off as invasive and creepy. But personalizing too little can cause a site visitor to exit your website before they have had a chance to convert.
Don’t track users around the web with cookies. Instead, we advise using first-party behavioral data to best personalize the user experience.
With anonymous user IDs, you aren’t tracking any personally identifiable information. Instead, pick up on behavioral signals to recommend the most relevant content at the right time to your site visitors.
This means that even first-time site visitors receive the same level of personalization as anyone else (See #4).
3. Rules-based recommendations aren’t enough:
Many companies rely only on static rules or lookalike audiences taken from historical data. Hard-coding rules causes you to miss out on a large part of your audience. Every moment counts when it comes to website recommendations.
Take this example: a female customer lands on your eCommerce website that sells men, women and children’s clothing. In the past, she has only looked at clothing for herself on your website. This time though, she’s looking for a gift for her daughter.
If you have already made a predetermined assumption that she is looking for women’s clothing, you have made a mistake. And, it could easily cost you a sale.
To optimize conversions and improve the user experience, avoid using only rules-based recommendations. Instead use a recommendation engine that learns and acts on changing consumer behaviors.
Note: Some rules based assumptions can be useful. If you are using any sort of predetermined rules, make sure they are accompanied by real-time behavioral based recommendations.
4. Know how to deal with first-time site visitors:
If a new visitor lands on your site and you know nothing about them, how can you possibly personalize their experience? We call this the cold start problem. If your recommendation engine relies solely on rules, customer logins, and/or purchase history alone, then this problem remains unsolved.
- But, using behavioral based real-time machine learning fixes this common issue. Therefore, you can start personalizing the experience for your new users almost instantly.
5. Don’t rely solely on metadata for recommendations:
Many third-party solutions rely on collaborative filtering to power their recommendation engines. Collaborative filtering is based on historical data and assumes that if someone took a certain action in the past, the next person will want to do the same.
Collaborative filtering only picks up on positive signals, which means that data is only collected when a user completes a specific action. But only using positive signals makes incorrect assumptions about what content may be relevant to a user.
What is just as important - and what many third-party solutions fail to consider - is negative signals as well. A negative signal means that what someone didn’t click or select has just as much meaning as a positive signal. .
Here at LiftIgniter, we use a combination of both positive and negative signals to power our recommendations. Instead of relying solely on metadata, we use Machine Learning techniques to determine intrinsic relationships.
Does your recommendation engine fit your needs?
If your recommendation engine does not give you all of this, contact us now. Using real-time machine learning, LiftIgniter increases conversions through relevant and timely recommendations without compromising the end user’s privacy. LiftIgniter combines machine learning on top of human intuition to provide site visitors with truly personalized recommendations.
Start by simply adding the LiftIgniter tag to your site and start collecting user behaviors immediately. Within our easy-to-use console, you can control results and view performance with inventory and audience insights. Your ultimate goal is to create happy customers who want to come back to your site. Our recommendation engine has the tools to get you there.
