Hyper-Personalization

What is Hyper-Personalization?

Hyper-personalization is an approach to marketing where an organization uses real-time customer data to customize the experience of each user. 

This involves collecting customer data from throughout the customer journey and historic interactions and leveraging technologies like artificial intelligence (AI), machine learning, and predictive analytics to tailor digital experiences to each individual user. 

Elements that can be customized include content, messaging, search results, product recommendations, pricing, promotional offers, the product discovery journey, and any type of digital interaction with customers. 

Examples of Hyper-Personalization

Hyper-personalization can be applied in many different ways in a marketing context.

Below are some of the most common examples: 

• Personalized Product Recommendations: Many brands use AI to provide customers with personalized product recommendations and promotion discounts to increase the likelihood of customers making a purchase.
• Targeted Emails: Companies like Starbucks have developed solutions to generate hyper-personalized emails and offers based on customer’s behaviors and preferences.
• Tailored Landing Pages: Brands can change the landing page offered to users based on the search engine keywords or advertisements they used to navigate to the site.  They can also be customized based on recently viewed products or past purchases. 
• Custom Social Media Ads: Another approach is to offer users personalized social media advertising that provides them with messaging and offers that are designed to meet their unique needs as a user. 

In each of these examples, concrete and granular customer data is used to inform the type of experience and messaging offered. 

Why is Hyper-Personalization Important?

Hyper-personalization is important because consumers expect a high level of personalization from modern brands. For instance, McKinsey reports that 71% of consumers expect personalization. 

Fulfilling those expectations is thus essential to increasing customer satisfaction and brand loyalty. After all, if brands can leverage customer data to offer more relevant digital experiences, they can maximize the chance of users converting into long-term customers. 

This is reflected in research released by Twilio, which finds that 56% of consumers say they will become repeat buyers after a personalized experience. In this sense, the level of personalization offered by brands is a key point of differentiation for customers in a market. 

At the same time, for decision-makers, investing in hyper-personalization technologies like AI, machine learning, and analytics can help surface customer insights. These insights are invaluable to discovering new ways to engage customers as individuals and for optimizing an organization’s messaging as a whole. 

Hyper-Personalization vs Personalization

Hyper-personalization is a distinct concept from personalization. The main difference is that personalization is about offering users a personalized online experience based on personas that have been taken from customer segments. 

On the other hand, hyper-personalization is about using real-time customer data to provide users with an experience that’s tailored to them as an individual. It’s a more granular approach that customizes messaging based on a customer’s unique data signals rather than attempting to market to customers based on generic personas. 

Using hyper-personalization is the best approach because it allows a brand to understand each user’s preferences. While personas are extremely useful, they run the risk of obscuring the unique needs of users in a given customer segment. 

Ultimately, the better organizations and brands understand user preferences, the more chance they have of increasing their overall conversion rate and revenue. 

Benefits of Hyper-Personalization

Implementing hyper-personalization offers a number of key benefits.

The Bottom Line

Hyper-personalization is about using customer data signals to get to know each individual user’s needs and preferences.

Using technologies like AI and analytics can help to enhance a brand’s understanding of what type of experiences and offers users respond to.