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What is hyper-personalization?

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    Szymon Lewandowski

Hyper-personalization becomes more and more popular among marketers. With the huge development in big data and personalization over the years, hyper-personalization offers huge improvements in customer experience, especially in the recommendations area. But what is hyper-personalization, how we can use it, and who is already using this method?

Hyper-personalization definition

The first definition of hyper-personalization comes from the mid-2010s, many research articles refer to the definition from the Business2Community article, released in 2014 by V. Subramanyan, then updated by A. Draper. Authors in their definition focused on the big data aspect of hyper-personalization:

Hyper-personalization is the use of big data to provide more specialized and personalized products, services, or information to the targeted segment.

Gathering more data about the customer, analyzing this data, and using this data in the recommendations are the 3 keys to hyper-personalization. A more concrete and "business-friendly" definition of hyper-personalization is the one proposed by G. Błażewicz, which sounds like this:

Hyper-personalization involves the use of customer data and their analysis by machine learning and artificial intelligence solutions.

An even more specific definition (in terms of tools) comes from the Deloitte report about hyper-personalization:

Hyper-personalization is done by creating custom and targeted experiences through the use of data, analytics, AI, and automation. Through hyper-personalization, companies can send highly contextualized communications to specific customers at the right place and time, and through the right channel.

Simply we can say that hyper-personalization is just a personalization with more data and better tools. Which results in better recommendations for our customers.

How hyper-personalization works

Collecting data

The key process in hyper-personalization is the collecting data phase. Companies usually use dedicated systems for gathering user data, such as analytics systems and tag managers. With these tools, you can easily track user behavior on the website and in the mobile app. There are plenty of data that you could get. For example:

  • Technical data – IP address, used browser, operating system, referral sources, etc.
  • UTM data – referral source, medium, campaign, keywords, etc.
  • User demographics – location, age, gender, language, etc.
  • Personal data – name, surname, address, phone number, etc.
  • User preferences – favorite categories, watched products, search history, etc.
  • Conversions – form submissions, newsletter registrations, add to cart, order, etc.
  • Browsing patterns – visits frequency, time spent on pages, favorite categories, etc.
  • Payment information – preferred payment method, shipping method, etc.
  • Social media profile information - data from Facebook, Twitter, Instagram, etc.

For omnichannel data collection, the best idea to properly aggregate data is to use dedicated tools, such as Customer Data Platforms. They could help to create a holistic view of customers, using data across all used channels, such as websites, mobile apps, emails, CRM systems, ERP systems, and others. They can significantly improve targeting and personalization.

Analyzing data

Usually this process is performed by analytical tools built into the analytical and personalization systems. During this phase, you can find user behavior patterns, determine user segments based on their preferences or other data and prioritize the most relevant data.

In modern systems, the analysis process is performed all the time, with new data coming from users. Some systems can prepare specified reports for usage behavior, most popular products and so on.


With analyzed data system can easily prepare recommendations for specific users, based on their preferences. When it comes to "providing more specialized and personalized products", the first thing that comes to mind is recommendations. We know several recommender systems that use different methods, such as:

  • Content-based filtering – recommending items based on user preferences, for example showing bedding sets for people who recently searched for the bedding set.
  • Collaborative filtering – recommending items based on preferences of users that are similar to the specific user, for example suggesting a new song to the specific user based on the listening habits of similar users.
  • Rule-based personalization – recommending items based on user segments, for example sending a birthday e-mail with a discount code for all users that got their birthday today.

These methods provide more personalized recommendations, based on current user preferences or similar users preferences. More advanced types of each recommendation methods often use machine learning and AI to provide better output for each customer.

Omnichannel approach

Hyper-personalization also focuses on the omnichannel aspect. The best way to deliver personalization is to deliver it at every customer touchpoint. Omnichannel personalization is personalization used not only in one place but in as many places as it could be implemented. For example:

  • E-mail – e-mails with product recommendations, special discounts, personalized newsletter, etc.
  • Mobile app – in-app messages, push notifications, banners, etc.
  • SMS – messages, reminders, etc.
  • Website – banners, pop-ups, product recommendations, etc.
  • Targeted Ads – product recommendations, slogans, special offers, etc.
  • Physical store – personalized customer approach, etc.

Hyper-personalization examples

Hyperpersonalization can help businesses increase customer loyalty, retention, satisfaction, and revenue. It can also help them stand out from the competition and create a strong brand identity. Here are three examples of how some well-known brands are using hyperpersonalization to enhance their customer experience and achieve their business goals:

  • Amazon: The e-commerce giant is powered by its recommendation engine that creates unique, hyper-personalized experiences for each consumer. Based on their browsing history, purchase history, ratings, reviews, and other factors, Amazon recommends products that are relevant and appealing to each customer. Amazon also uses hyperpersonalization to send personalized emails, display personalized ads, and offer personalized deals and discounts.

  • Starbucks: The coffee chain stepped up its personalization game with AI, using real-time data to send personalized food and beverage offers to its customers via its mobile app. Based on their location, purchase history, preferences, weather, time of day, and other factors, Starbucks delivers tailored offers that increase customer engagement and loyalty. Starbucks also uses hyperpersonalization to create personalized playlists for its customers based on their music tastes and mood.

  • Spotify: The music streaming service implements hyperpersonalization in its marketing campaigns with the feature “Spotify Wrapped”. This feature allows users to see their personalized summary of their listening habits throughout the year. Based on their favorite artists, genres, songs, podcasts, and other metrics, Spotify creates a customized report that users can share on social media. Spotify also uses hyperpersonalization to create personalized playlists for its users based on their mood, activity, or occasion.

These are just some of the examples of how hyperpersonalization can help businesses create memorable and meaningful customer experiences that drive growth and success.