What is content personalization? Why is it important, and what makes smart content recommendations stand out? A guide to understanding recommendation engines from industry experts at Recombee, and a showcase of Kentico and Recombee partnership that made AI personalization integration easy.
What is personalization?
Typically, personalization is referred to as tailoring products, content, experiences, or communication to user’s tastes and preferences. With the growing number of sites and platforms, as well as the rising pool of content online, personalization has become the driving force of customer acquisition and loyalty. An easily understood example of a personalized user experience could be with Netflix, where the users are suggested movies specific to their likes, based on recently watched features.
Personalizing with AI recommender engine
One way of achieving the highest quality and precision of personalization is using an AI-powered recommendation engine. Analyzing on-site behavior and item attributes provides insights into which content or items are most likely to be engaging to users. Knowing this information, the engine can “filter out” the undesired content and push forward the relevant content your user was hoping to find. This saves time between the moment when the user first enters your site and the moment when captivating entertainment is found.
The advanced recommendation engines process the user’s onsite behavior and given product or content characteristics in real-time. That means that they react to every new click or a new change in the catalog immediately. The examples of user behavior are interactions such as clicks, likes, purchases, or watch portions, while the content or product attributes analyzed are categories, images, price, or text.
All provided data is analyzed by sets of smart algorithms to provide the most accurate and reliable personalization services to each user. Recombee’s real-time recommendation engine has been developed by a team of data scientists to utilize the newest findings in artificial intelligence (AI) and machine learning (ML), which enables the engine to process image similarities, text in more than 80 languages or even recognize emerging trending content or repeating patterns in user behavior.
In such a way, usage of AI and other advanced models allows Recombee recommender engine to provide clients all around the world with personalization services in the form of highly tailored content and product recommendations. Content recommendations can be utilized all across the customer journey and applied to places such as clients’ homepage, read next and watch next sections or category sorting, as well as personalized internal search, push notifications, or emailing. Product recommendations can be used for upselling or cross-selling, as well as other use cases, e.g., geo-location-based offers.
Benefits of using a recommendation engine
The main objective of a recommendation engine is to fully engage your visitors and create a foundation of satisfied customers that become loyal supporters.
One of the main benefits of personalization is increased user engagement. By showing each user content that is most likely to spark their interest, you make them engage and consume more and thus stay on your site longer. Some of Recombee’s clients go as far as reporting a 50% increase in their CTR, while others report 2–3 times higher conversion rates.
What is more, personalization creates a unique bond with each user, making them more loyal and prone to return to your site repeatedly. This bond helps overcome the common challenge of retaining users and helps to decrease the bounce rate—the percentage of users that are fast to leave the site.
Manual vs. predictive personalization
Before we take it a step forward and explain how Recombee and Kentico integration works, it may be beneficial to understand how personalization through a recommender engine is different from the alternative way of personalization done through pre-set rules.
Manual personalization—personalization governed by fixed, pre-set rules—was and still is a common practice in the industry. Usually, manual personalization means for an online marketer to manually create personas—types of customers that visit your site. Depending on the individual user’s onsite behavior, the user is categorized under these personas and is offered content that others under this cluster have enjoyed.
With bigger clients that require a more complex degree of personalization, this manual approach may no longer be sufficient. This is a common reason why clients could turn to AI-powered engines that can process bigger catalogs of items and higher numbers of users on their sites.
The manual process may lead to many opportunity costs that can be avoided with predictive personalization based on flexible machine learning algorithms—recommendation engine. While manually creating personas requires time, resources, and extended research, Recombee’s solution is incredibly efficient, while enabling a minimal time to market, thanks to Kentico’s pre-prepared integration.
Once integrated, the recommendation engine works automatically and provides individual personalization, specific to each customer, right from the first click. Unlike in the case of preset rules, Recombee is also adjusting to user’s likes and clicks in real-time and factoring in changes in preferences.
By pairing up with Recombee, Kentico offers an upgrade that saves marketers and developers time, while providing more accurate results in personalized recommendations.
How to integrate Recombee through the Kentico digital experience platform?
Thanks to partnering together, Recombee’s content recommendations can be currently easily integrated through the Kentico platform. There are three main steps to get AI-powered recommendations of your content, such as articles, blog posts, or videos. Firstly it is sending the catalog of the items, secondly sending the interactions—views made by the users, and thirdly showing the recommendations to the end-users. All available through the Kentico Recombee module.
Once you have the integration in place, you can configure many aspects of the recommendations in the Recombee Admin UI, with no further need for changes in the code.
The integration also enables you to set different recommendation behaviors for each Scenario—a place on your webpage where you show the recommendations, and also apply different business rules—an additional command to filter or boost desired content. For example, you may want to recommend items that are currently trending now, visually similar, or recommend articles written by the same author.
Advanced artificial intelligence in use
After the integration is finalized, Recombee’s solution utilizes a second layer of AI specifically designed to automatically fit each specific platform and use case. This internal layer of AI conducts continuous AB testing to ensure the recommender is optimized to maximize your main KPIs and considers ever-changing user behavior and market trends over time.
It is worth noting that Recombee’s engine with direct integration is capable of processing a wider variety of data. In addition to views, Recombee can also analyze user purchases, bookmarks, likes, cart additions, or view portions, such as which articles were read until the end, or which were quickly visited and abandoned. Such information provides additional insights into user behavior that helps tailor content with a higher level of precision, achieving further improved user experience.
Since the engine is provided information from more than one channel, Recombee can tailor its recommendations in real-time according to users’ preferences—factoring in changes in users’ interests that could occur in time. For example, the reader could previously spend more time reading political articles and skipping all other categories. After some time, the reader may decide to skip previously read political articles and concentrate on fashion. Thanks to factoring in time spent on each article, the engine can recognize the change in preferences and bend its recommendations accordingly.
Drive outstanding business results with accuracy and precision
After the data is uploaded and adjusted to specific use cases, you can enjoy a fully personalized experience for each of your users. The users are engaged with content tailored to their tastes, and the developers are freed from previously manual work—achieving better and more accurate results!
Still curious to learn more about how Recombee personalization works and how you can leverage it in Kentico? Do not hesitate to contact us at email@example.com, and enjoy the perks of AI recommendations in your DXP from Kentico!