Customer Experience

Is Hyper-Personalization The Next Big Thing In Business?

August 27, 2021
12:00 am

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.

What is hyper personalization?

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.

hyper-personalization Screenshot from Spotify
Screenshot from Spotify

No matter which personalization approach your business prefers, it all revolves around customer data.

Here's how.

Using customer data for hyper-personalization

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.

Benefits of hyper-personalization

By offering hyper-personalized experiences to the customer, businesses can achieve the following results:

  • Increased revenue. Revenue is closely tied to personalization – customers are reportedly more likely to do business with companies that provide personalized experiences.
  • Smoothen customer journeys and improve experience. The more relevant your offerings and content are, the less time and effort your customers will spend sifting through information and products they’re not interested in. This way, you also show customers you value them as individuals – which can work wonders for customer retention and word-of-mouth marketing.
  • Shift the focus from static to dynamic. Even when you’ve identified your customer segments or personas, you can’t rely on that interpretation forever. People change, their habits change. If you let hyper-personalization drive your customer engagement strategies, you’ll be more likely to recognize those changes early and make the right adjustments (even in real-time).
  • Be a motivator for increasing data quality. Great hyper-personalization relies on clean, high-quality data. By focusing on making the necessary data unified, organized, and easily accessible for your personalization strategies, you’ll also achieve better data integrity and connection among your data sources.
  • Get you thinking about channels and timing. Traditional marketing campaigns may blast out messaging without really tailoring it to the recipient. Sure, we send newsletters based on time zones and try to be mobile-friendly, but is that really enough? With hyper-personalization tactics, you reach each individual customer where and when it’s most likely to resonate.
  • Make conversations more meaningful. Hyper-personalization enables your team to speak to customers as individuals. As an example, imagine someone uses live chat software to ask questions about products or support. And thanks to technology, the agent knows this customer’s purchase history, which pages they visited, what questions they’ve asked before, and more. That way, they can tailor the conversation to the customer’s true needs and offer truly personalized customer service.

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Good and bad examples of hyper-personalization

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.

When personalization works well: Netflix

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”.

hyper-personalization example from netflix
From Netflix notifications

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.

When personalization doesn't quite work: Walgreens

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.

hyper-personalization example from Walgreens
An experiment of promotional cooler doors at Walgreens

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.

Hyper-personalization is worth it

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.


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