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.
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.
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.
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.
There are some obvious and some less obvious benefits of hyper-personalization. For example, the hyper-personalization approach can:
Wondering how to keep omnichannel data and use them to have meaningful conversations? Learn more about technology to help you in your personalization quest.
That’s right – let’s look at one successful and one controversial example.
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.
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.
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.