According to Salesforce, 51% of customers expect that brands will anticipate their needs and make relevant suggestions before they make contact–by 2020. Well, here we are!
It’s amazing how much business has changed in the last 20+ years, and yet, it’s incredible how much it hasn’t. Take for example the concept of 1:1 marketing, introduced by Don Peppers and Martha Rogers in the late 90s.
They defined 1:1 marketing (also called relationship marketing or customer-relationship management) as “being willing and able to change your behavior toward an individual customer based on what the customer tells you and what else you know about that customer.”
Sounds like something that has been referred to as “personalization” in recent years but has evolved even further to “hyper-personalization” in 2020.
Let’s start with some definitions.
What is hyper-personalization?
Interestingly enough, there is not a clear definition of hyper-personalization; my search online found several very different definitions, some of which didn’t sound like anything much different from the definition of personalization. Here are two that I felt fit what I think of as I consider the meaning of this term.
Capgemini defines hyper-personalization as “an advanced and real-time customization of offerings, content, and customer experience at an individual level. Designed to perfectly match a customer, hyper-personalization leverages Big Data to deliver such tailor-made solutions in real time.” And, they note that hyper-contextualization is an integral part of hyper-personalization. Clearly, there’s also an element of speed included in this concept.
Three Deep says that “hyper-personalized marketing is the process of a brand speaking directly to one customer.” I like this definition and would only clarify it by putting quotes around “speaking” because hyper-personalization doesn’t just have to be about content and messaging.
Hyper-personalization includes–but also moves beyond–data-driven to analytics-driven. Artificial intelligence, machine learning, predictive analytics, and prescriptive analytics are important tools in the hyper-personalization toolbox.
How does it differ from personalization?
Personalization also takes many forms and has many definitions. Some will define it as simply as addressing customers by their names in emails and recognizing or acknowledging products they’ve purchased in the past. These are obviously simplified examples, but they are meant to be so as to clearly differentiate between the more-advanced and artificial-intelligence-driven nature of hyper-personalization.
Just addressing emails to customers by name is no longer adequate. Customers have changed. Customer behaviors have changed. Customer expectations have changed. They no longer say, “Know me.” Now it’s “Hear me. Know me. Understand me. Show me.” And “show me” translates to a lot of different things, like context, relevance, and timeliness.
Context, as noted previously, is important to hyper-personalization. Context can come in the form of location, channel, time of day, product category, previous brand interactions, why a customer is buying, and more.
What does hyper-personalization require?
What do you need in order to create a hyper-personalized experience? Where do you start?
It’s probably no surprise that the requirements are no different from designing, developing, and delivering any other kind of experience. It begins (must begin!) with customer understanding: Who are your customers? What are their pain points? What problems are they trying to solve? What are their needs and expectations? What are their preferences?
Taking the time to get to know your customers is really at the root of hyper-personalization. You cannot tailor the experience or your offerings in real-time without knowing your customers and putting your customers at the heart of hyper-personalization execution. (Well, you could, but it wouldn’t be wise or successful.) But you must also have a keen understanding of your products in order to deliver on this level of personalization.
What else is required? Data, namely behavioral data, both historical and real-time. Never before has the phrase “right data to the right people at the right time” been more relevant than with hyper-personalization.
And data is just data until you do something with it, so you’ve got to have the tools and the technology to deliver insights, content, offerings, and the experience in real-time. The more of the right data that you have about your customers, combined with the ability to understand, learn, and adapt, and then the tools (such as customer data platforms to create a single view of the customer, journey analytics, and predictive and prescriptive analytics tools and capabilities), technology (such as geofencing, facial recognition, and biometric sensors), and the people to apply all of that in a relevant and contextualized way, the better the experience will be for your customers.
So what does hyper-personalization require? It requires that you understand your customers, have the right data at the right time (preferable a single source of data, somewhere where omnichannel data is captured, stored, and accessed), and use AI-powered tools and analytics to deliver the right experience at the right time.
By the way, customers are open to it: Salesforce found that 61% of millennials will share personal data if it leads to a more-personalized shopping experience, while 58% will share it to power product recommendations that match their needs. But will they share it for hyper-personalization?!
What are the benefits of hyper-personalization?
Hyper-personalization is no easy lift, and it wouldn’t be worth the effort if there weren’t benefits for both customers and the business. Let’s take a look at those benefits.
On the surface, customer benefits seem to be a no-brainer, but with some of the challenges around the creepiness factor, data privacy, and security concerns, the benefits might not be as cut-n-dried. But according to research by Ascend2, “improving the customer experience” is the top priority for brands with hyper-personalization strategies.
How will hyper-personalization improve the customer experience? Done right, i.e., if brands take the time to understand their customers and then use the data and insights in a way that truly benefits the customer, customers will receive communications, content, and messaging in a more cohesive and contextualized way–at every touchpoint. They can then use this information to make more-informed decisions.
In addition to–or in conjunction with–that, hyper-personalization all but eliminates choice overload, i.e., when there are too many choices or irrelevant choices, saves customers time and money, reduces customer effort, and eliminates information overload. All of this simplifies the experience for the customer, through faster and easier transactions and interactions.
Hyper-personalization isn’t just about marketing and content. It can also be used to design products and services to meet customers’ needs.
A few other benefits include anticipating customer needs, providing a seamless experience across channels, and meeting expectations of the connected customer, i.e., know me and remember me.
Given the increasing rise and popularity of hyper-personalization, there must be benefits for the business. Yes, of course there are.
Again, done right, one of the benefits is that the brand comes across as one brand and one brand voice. “Dear customer: We know you, and we know how you’ve interacted and transacted with us in the past. We are using that information, for your benefit, across all of our channels–seamlessly and simultaneously–to provide you with a seamless experience.” One brand.
Brands who get hyper-personalization right will also be celebrating increased revenue (including from cross-sell and upsell opportunities), increased advocacy and recommendations, increased engagement, and increased brand loyalty.
What are the challenges of implementing hyper-personalization?
Notice that I’ve added the “when done right” caveat a few times. It’s tough to do this right. Hyper-personalization is a heavy lift and requires the right data, tools, and technology.
Data is the driving force, but there are hindrances and obstacles to this driving force, including: silos and siloed data, data quality, lack of tools and technology, and lack of customer understanding. And even if these obstacles are in place or close to being in place, the real kicker is speed and being able to glean insights fast enough to be able to deliver the right experience in real time.
On top of that, the balance of personalization and the ultimate experience rests on how well businesses can respect data privacy, security concerns, and the creepiness factor. Customers are willing to provide data and information to enjoy a better experience, but brands must be prepared to protect and secure personally identifiable information (PII)–and to tell their customers how they are doing that. In addition, given the hyper-connected nature of IoT (Internet of Things), a clear source of customer behavioral data, data security is always a concern that must be top of mind for the entire C-suite.
No matter the reason, businesses must always do good, do what’s right, and respect customers as humans–and treat them the way they want to be treated.
Are any brands doing hyper-personalization well?
There are probably a lot more examples than these, but the ones I came up with based on personal experience are:
Amazon: I probably don’t need to explain why Amazon makes this list. I’ve been a Prime member for as long as I can remember. My homepage on the app, on my desktop, and on my TV reflect my purchase and viewing behaviors. They make product recommendations based on my search history and my purchases. They notify me when it might be time to repurchase certain items, e.g., vitamins, cat food, etc. They understand me. They know me. They show me.
StitchFix: if you subscribe to StitchFix, you know that they take them time to get to know you and your preferences, both through a questionnaire and through their iterative subscription process, i.e., if they send you an outfit you don’t like, you can return the items that don’t fit your style. They learn and iterate from there. This particular example may not be built on speed, but it will build customer loyalty based on a learning relationship.
Starbucks: you know that the Starbucks app is their learning tool, providing tons of data about what you buy, when you buy, what you like and don’t like, etc. Given that, they are able to present you with deals, offers, and product suggestions that are relevant to your tastes and purchase patterns, a powerful tool to build loyalty, for sure.
Netflix: stop and think about your experience with this platform, and you know that they make recommendations based on your viewing habits. You can rate content, which they then take into account when they recommend other content to you.
Hyper-personalization isn’t just for retail brands. It’s for any vertical. For example, event apps have taken to getting to know users because many brands are starting to use the same top event apps. As such, these apps are able to learn and understand so that they can then recommend sessions, attendees to connect with, and more.
There are certainly brands in other industries (e.g., financial services, healthcare, utilities, etc.) that could be taking greater advantage of this, but there are limitations that must first be overcome. Is this your brand? How can you use hyper-personalization? What are the obstacles for your brand?
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About the guest author
Annette Franz is the founder and chief experience officer of CX Journey Inc.
She’s got 25 years of experience in both helping companies understand their employees and customers and identifying what drives retention, satisfaction, engagement, and the overall experience—so that, together, you can design a better experience for all constituents. She has worked with both B2B and B2C brands in a multitude of industries. Connect with her: www.cx-journey.com | @annettefranz | @cxjourney | LinkedIn | Facebook