Technology is constantly upgrading which created a new type of personalization that takes things one step further "hyper-personalization".
If you're a marketing professional, you've probably been using standard personalization practices to attract, engage, and convert visitors into customers. Tactics such as simply using the customer's first name in an outreach email or welcoming them with a "Hey Kevin!" when they log in to your product.
But, there's always room for improvement, especially with technology. And it’s actually a pretty exciting step for digital customer experience.
So, let's dive in.
Hyper-personalization is the use of artificial intelligence (AI) and real-time customer data to display relevant content, products, services, and information to each individual user or customer.
This new version of personalization is part of the so-called “hyper-relevance” of the digital era. It’s about using data, AI machine learning, and predictive analytics to understand your audience’s individual behaviors and make interactions more relevant to them.
Personalization encompasses many practices. It can be generic – the most typical example being the use of a customer’s first name in emails. Another example can be displaying ads that target a particular audience segment based on demographics or other basic characteristics.
But, hyper-personalization would be something like Spotify’s recommendation engine or advertising based on location tracking. These suggestions change in real-time based on the actions a user takes while interacting with a product or service.
No matter which personalization approach your business prefers, it all revolves around customer data.
In order to provide hyper-personalized experiences to the customer, businesses need to focus on data.
The data used in this case goes beyond basic demographics and general preferences – they are a product of analyzing the customer journey and individual customer profiles.
This means unifying data from multiple sources and devices – like social media, mobile browsing, purchase history, consumer trends, or data from IoT devices, to create “segments of one”.
So, after we’ve leaned so heavily on data, why is hyper-personalization often thought of as an “art” instead of a “science”?
Well, in reality it’s both. Science, because it’s not random, but rather based on careful analysis. Art, because the way to achieve hyper-personalization varies from company to company, and relies on a different interpretation for each customer base.
Let's see why using hyper-personalization in business is important.
By offering hyper-personalized experiences to the customer, businesses can achieve the following results:
Hyper-personalization is still a relatively new concept, with some businesses nailing it and others missed the mark. Most of the digital-based businesses had no problem enhancing their approach to personalization, but the companies that were more "physical" had a hard time keeping up.
Let's check out an example of each.
As with most online entertainment companies, Netflix has built a recommendation algorithm to make experiences more relevant to its customers. If you use the platform, you’ll have seen the “recommended” lists throughout your dashboard: “Because you watched X:” or “Top picks for you”.
Netflix estimates that only 20 percent of its subscriber video choices come from search, with the other 80 percent coming from recommendations – saying that if they were to lose their personalized recommendation engine, they’d also stand to lose more than $1 billion every year.
How they did it right: Their recommendations algorithm sends users suggestions of shows or movies based on their watching habits. This opens them up to discover new shows, movies, and genres they might not have been aware of. By providing them with relative content, it keeps the users engaged and hooked on the product.
Back in 2019, Walgreens partnered with a startup called “Cooler Screens” to revamp its cooler doors by turning them into screens. One of the most hi-tech examples of facial-recognition technology used in retail stores that assesses the passing moods of customers.
The tech behind this involved motion sensors, cameras, and eye tracking devices to collect data needed. This data would then be used to change the display of the screen to advertise. The CEO of Cooler Screens, Arsen Avakian, told FastCompany:
“You could pass by the beer door, and [the door] may notice that you’re picking up a six-pack of Miller Coors. It’s 4 p.m., so it’s near dinner time. [It might] offer to you, buy a DiGiorno pizza for a special price if you’re buying a six-pack of Miller Coors.”
The experiment was met with some interest, but also a fair amount of concern. Privacy considerations are on the rise, especially with in-store tracking. It remains to be seen whether consumers will accept this mode of advertising, although transparency about data collection is a first step toward that.
Why it didn't work: As mentioned, privacy around customer data and habits has always been a hot topic. Now imagine you're in Walgreens and you had something private in your hands that the screens pick up on. They would then offer "complimentary" products or services that are private as well for the whole store to see. Not an ideal situation to be in when a customer is discretely purchasing a product.
Privacy, data collection, leveraging AI machine learning, and creating the right hyper-personalization strategies certainly take some planning.
Think carefully about the data you have and the data you need, and brainstorm ideas that will resonate with your audience. Going through with digital transformation plans is essential.
The most important thing is: don’t be afraid to experiment. Getting into the minds of your customers may be tough, but finding the sweet spot is more than enough reward.
And if done correctly, can significantly improve their experience creating life-long customers.
So, what did you think? Do you use hyper-personalization in your customer experience approach? Let us know in the comments.
Share it on social media and let others see the next wave of personalization.
Nikoletta is a Content Specialist at Acquire. She's a writer and editor with an avid interest in data, tech, communication, and the customer journey.