Introduction

In an age where 'Super,' 'Mega,' and 'Ultra' have found their way into everything from detergents to diet plans, it's only fitting that you’ve ended up here. Reading an article about something as specific as 'Hyper-personalization.' 

As with most prefix-noun combinations, Hyper-personalization is equal parts marketing jargon and actual added descriptive value. In this article we’ll try to look closer at the latter, and find out what makes hyper-personalized content hyper.

Common elements of Hyper-personalization

Hyper-personalization aims to go beyond traditional personalization methods by leveraging user behavior, preferences, demographics, past interactions, browsing history, location, and other contextual information to create highly customized experiences. 

The goal is to deliver the right content to the right person, at the right time. Some common elements of Hyper-personalization include:

• Real-time data analysis: The system continuously analyzes user data in real-time to understand their current preferences and needs, allowing for dynamic adjustments and personalized recommendations on the fly.
• Individualized content: Content is tailored to each individual user's interests, preferences, and behaviors, ensuring relevance.
• Predictive analytics: The use of machine learning algorithms to predict future user preferences and behaviors based on historical data.
• Omni-channel experience: A seamless and consistent personalized experience across various touchpoints.
• Context-awareness: Consideration of various contextual factors such as location, time of day, device used, and user intent.

Hyper-personalization can enhance customer experiences, increase engagement, and drive conversions. However, it also raises ethical concerns regarding data privacy and security, which must be carefully addressed to build and maintain user trust.

Hyper-personalization vs. Personalization

Hyper-personalization is a subset of personalization, and the phrases are often used interchangeably. If we were to dig a bit deeper, we can see that the two differ in their scope and degree of customization.

Scope of customization

• Personalization: Tailors content based on user characteristics within predefined segments.
• Hyper-personalization: Provides highly specific and relevant recommendations or content to each individual based on real-time data analysis and granular user data.

Data utilization

• Personalization: Relies on historical data and user attributes. It may not consider real-time data or a wide range of contextual factors.
• Hyper-personalization: Utilizes both historical and real-time data, allowing for immediate adjustments and up-to-date recommendations.

Recommendation precision

• Personalization: Offers relevant recommendations based on the user's segment, but may not always capture individual preferences.
• Hyper-personalization: Delivers highly accurate and precise recommendations that match individual preferences.

Dynamic vs. static

• Personalization: Often relies on predefined rules or segments, which may not frequently change.‍
• Hyper-personalization: Adapts in real-time as the user's preferences and behavior evolve.

So in summary: While personalization focuses on tailoring content based on user segments, hyper-personalization provides a more individualized and context-aware approach, making it more effective in delivering a personalized user experience.

Hyper-personalized video

Video is just one of the many media that can be used in hyper-personalization. Like text, images, and other forms of content, videos can be tailored based on individual user data to provide a more engaging and personalized experience. However, it's important to remember that the crux of hyper-personalization lies in the input data and not the media type.

The one thing that is worth considering if you aim to hyper-personalize a video is how quickly that video can be rendered and delivered after you first receive the data and trigger. Unlike SEEN, some providers in this space can be quite slow and you may well end up missing the window of opportunity that was the entire reason for personalizing in the first place.

If you want to explore the concept of data-driven video personalization further, we suggest that you take a look at this article on what video personalization is.

Examples of hyper-personalized video

ICA's personalized year in review

In 2022, ICA, the largest food retailer in Sweden, decided to explore the potential of personalized, data-driven video as a new tool for customer communication and engagement.

After delivering over 2 million hyper-personalized videos to their customers, an audience survey showed that 85% of respondents had a favourable or very favourable view of the communication. Of those respondents, three out of four gave the highest possible score.

For more details, head over to the ICA case study.

Eplehuset's personalized greeting to new customers

In early 2021, Eplehuset created a personalized video that was automatically delivered to new customers by SMS, shortly after they had first visited one of the physical stores. In the video Eplehuset’s CEO sincerely and excitedly thanked the customer for their business, addressing them by their first name – with the very store they had just visited in the background.

30% of the customers that watched their video signed up to the newsletter. In previous campaigns, the conversion rate had been only about 2%.

For more details, head over to the Eplehuset case study.

Common questions about Hyper-personalization

How does Hyper-personalization benefit businesses and customers?

Hyper-personalization enhances customer engagement, increases conversion rates, and improves customer satisfaction. Customers benefit from a more tailored and seamless experience.

What data is used for Hyper-personalization, and how is it collected?

Hyper-personalization uses data sources such as user behavior data, browsing history, preferences, location information and purchase history. Data is generally collected through cookies, user accounts, tracking pixels, and consent mechanisms.

What technologies and tools are involved in implementing Hyper-personalization?

Machine learning algorithms, artificial intelligence, big data analytics, real-time data processing, customer data platforms (CDPs), marketing automation and personalization platforms are used.

Can I create a Hyper-personalized video with SEEN?

Yes you can! We’ve got the speed and the expertise – so as long as you’ve got the data we’d be happy to help you get started with hyper-personalized & data-driven video. 

Other examples of successful Hyper-personalization

Hyper-personalization can help guide customers to what they truly want. Whether it's Amazon pointing to the perfect product, Netflix lining up the next binge-worthy series, Spotify tuning into each listeners musical tastes, or a retailer like ICA joyfully summing up the past year, personalized experiences are transforming the way we shop, watch, and listen.

Curious to take a closer look? Here are some standout examples:

• Amazon's product recommendations based on user browsing and purchase history. 
• Netflix's personalized recommendations based on viewing history and preferences.
• Spotify's personalized home section and Discover Weekly feature, which suggests songs based on users' listening habits.
• ICA’s Year in Review film based on customer purchase history, store location and more (powered by SEEN).

Remember, as with any form of personalization, the key to hyper-personalization is respecting user privacy and making sure to provide value in return for the data used.