Remember when personalization was the next big thing in marketing and customer service? Well, technically it still is, it just went up a notch. 

While many of us were still wrapping our heads around personalization techniques, technology kept marching forward to enable “hyper-personalization.” 

And it’s actually a pretty exciting step for digital customer experience. 

What is hyper personalization?

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, personalization can also be as specific as Spotify’s recommendation engine or advertising based on location tracking. 

And that’s hyper-personalization.

The art of maximizing relevance

Hyper-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.

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”.

One of the most hi-tech examples is face-recognition technology used in retail stores that assesses the passing moods of customers. This data is then used to provide real-time adverts on fridge windows or billboards (and also prevent shoplifting, but that’s a story for another time).

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.

It’s here, but not everywhere

Understandably, not all companies have reached this level of personalization. In fact, there are multiple phases of maturity companies must move through which are clearly connected to revenue, as you can see in the diagram below from Deloitte.

According to some research, 62 percent of marketing professionals see hyper-personalisation as critical but only nine percent have successfully implemented it.

So, we can expect to see more and more forward-thinking companies implement hyper-personalization as they grow and reap the financial benefits.

Customers love it, but what can hyper-personalization do for you?

There are some obvious and some less obvious benefits of hyper-personalization. For example, the hyper-personalization approach can:

• Increase revenue. This is one of the obvious ones. Revenue is closely tied to personalization (as indicated above) – 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 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 more unified, more organized, and more 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. Usually, when talking about hyper-personalization, we think about automatic data collection and distribution of content (be it ads, or other). But, that’s not all there is. Hyper-personalization enables your team to speak to customers as individuals as well. As an example, imagine someone uses live chat or phone 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.

Wondering how to keep omnichannel data and use them to have meaningful conversations? Learn more about technology to help you in your personalization quest.

1 + 1 Examples of hyper-personalization

That’s right – let’s look at one successful and one controversial example. 

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”.

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.

Walgreens

Back in 2019, Walgreens partnered with a startup called “Cooler Screens” to revamp its...uhm, cooler doors by turning them into screens. (You saw an example in the image in the previous section).

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.

Go for 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.