Personalized experiences are rapidly becoming expected by modern customers and companies that do not offer personalized CX risk losing customer retention and revenue. Hyper-personalization is one way organizations are avoiding such risks.
Customer engagement is vital, with audiences now comparing interactions with each brand to find the best experiences available. With these demands in mind, hyper-personalization may be the ideal solution to creating user-centric experiences.
Hyper-personalization has become a factor in differentiating successful businesses from the pack. But what exactly is it? What are the benefits? And what’s required, technology and infrastructure-wise, to make it work for your business?
Read on to reveal all of the above, plus 5 top successful examples of companies putting hyper-personalization into action!
What is hyper-personalization?
Hyper-personalization is the process of using AI and real-time data to create products and content for shoppers.
It’s a customized experience of the shopping journey consisting of offers and solutions highly adherent to customers’ needs, strengthening the relationship and ensuring customer loyalty through positive experiences.
The importance of prioritizing the customer was highlighted by Amazon CEO Jeff Bezos when stating:
With hyper-personalization, customers are treated as individuals, with their tastes and preferences analyzed by brands and retailers to provide a unique customer experience that differs for each shopper.
The magic is in making the customer have an individualized perception of a solution that efficiently solves their needs/desires, reducing the “friction” of the journey as much as possible.
Hyper-Personalization vs. Traditional Personalization:
The crucial factors separating hyper-personalization from traditional personalization are the product discovery journey and website interactions that are individualized for each customer.
With traditional personalization, consumers receive more general product recommendations, as it uses personal and transactional information to make assertions about the customer based on their characteristics.
With hyper, instead of just receiving general product recommendations, customers get a more context-sensitive experience based on how they interact with the brand.
Hyper-personalization evolves audience segmentation, which enables efficient, customizable, and subject to modifications in real-time to better respond to customer requests.
It is more complex and evolves behavioral and real-time data such as browsing behavior, in-application behavior, and engagement data to interpret consumer intent which results in more contextualized communication and, ultimately, superior conversion.
The benefits of hyper-personalization
When done right, hyper-personalization offers smooth, frictionless customer journeys that delight shoppers and increase conversions.
The impact that hyper-personalization can have was showcased by Evergage when sharing that:
“86% of companies report seeing a measurable uptick in business results from hyper-personalization.”
The benefits of hyper-personalization are vast and far-reaching, but here are some of the key advantages:
Provides Customized Content:
The most classic or obvious example but hyper-personalization has become a selling point for many brands in the digital age – where they have seemingly endless choices.
By offering customized content, companies can appeal to consumers on a more personal level, leading to greater loyalty and satisfaction.
The idea is to leave your audience never having to filter through masses of content they have no interest in, to receive only the most tailored and relevant information.
Offers Greater Customer Engagement:
Hyper-personalized content creates a level of engagement that has never been possible before.
Think of receiving customized messages from your favorite brand on your phone or walking into a store and having all your preferences already known to create a personalized in-store experience.
This is the level of engagement that creates loyalty and customer retention.
Hyper-personalization helps businesses acquire new customers and grow their businesses.
One way to personalize content is to use data to create targeted ads for products similar to those that the user has already shown interest in or ads tailored to the user’s specific demographics.
Hyper-personalization can also improve websites; imagine a user has visited a site several times but has never made a purchase. Sites can show targeted offers or personalized recommendations to encourage them to go through and make the purchase.
Enhances Customer Loyalty:
With hyper-personalization, businesses can create bonds with their customers, leading to long-term relationships.
By leveraging data and artificial intelligence, businesses can deliver personalized content and recommendations to individual users to strengthen customer relationships and drive conversions.
How hyper-personalization works in Customer Experience
Hyper-personalization creates unique and customized experiences for each individual user.
The more relevant your offerings and content, the more value you provide for the individual, which works wonders for word-of-mouth marketing and repeat business.
Hyper-personalization takes CX to the “next level” by leveraging technology by personalizing at scale to deeply tailor individual customer experiences based on each customer’s actions.
Think of it like a tracking beam honing in on specific behaviors to reach customers in new and more relevant ways to tailor individualized experiences for each customer.
What is required to deliver hyper-personalization?
When it comes to delivering hyper-personalization, data rules the roost. Customer data needs to be collected to understand individual needs.
Using data and AI, you can deliver a hyper-personalized user experience that will improve customer engagement and conversions. The hyper-personalization process will only work if your company invests in collecting and processing data.
Data intelligence: the engine of hyper-personalization
The magic happens when decision automation algorithms are powered by processes, such as machine learning and deep learning, to scan the database and find the fit between the information collected and the consumption profiles to create hyper-segmentations.
This automation of the decision process manages to cross data from different sources, considering many variables. This complexity will ensure that your company can adapt marketing and sales strategies to understand this evolution of consumer expectations.
But what exactly is the process?
The 5 Steps of Hyper-personalization
- Data Quality Assessment
Data quality criteria exist to understand whether the data used to try to perform an analysis or generate a solution are consistent with reality. Here we see if the information is up to date, if it has undergone any previous transformation that could influence results, and if the information is useful to deal with the problem or not, etc. The key here is having a clear vision of quality to avoid incorrect and biased answers.
- Big Data Strategy
It is common to think of Big Data more only for those vast volumes of data, but when we talk about Big Data Strategy, it goes beyond that; it is to create a way of dealing with data that is expandable, robust, that allows the necessary information to arrive correctly from who it needs to, and at the right time necessary.
When we talk about strategy, having robustness and a well-orchestrated flow of data is especially necessary, or the company will only be able to act with delayed data and lose competitiveness.
- Cluster Analysis & Market Segmentation
Once you have access to the data, it’s time to start segmenting populations to see what kind of pattern begins to form and what types of subpopulations are emerging for the segmentation of target audiences for greater assertiveness of which are the most exciting target audiences for a given action. From here on, as long as it is done correctly, each step should start to show gains in the “journey” of hyper-personalization.
- AI, Algorithms & Recommendation Systems
Once the data is of the required quality and there is a well-defined flow that can feed and consume machine learning models, only then can you effectively introduce AI models into an enterprise. Every AI model and recommendation goes through a degradation – trained with a set of data. And as time passes and customers’ buying habits and life moments change, the model will have performance losses. Once you have a robust structure for feeding, consuming, and analyzing the response of machine learning models, you will be able to detect the most suitable time to re-evaluate a machine learning model and thus keep the tool at its maximum.
- Real-Time Analytics
Every company will be influenced in some way or another, and changes can happen very quickly. We are talking here about seasonality, the period of the year, socioeconomic factors, and environmental accidents in the production chain, for example.
Being alert to how external factors impact metrics and forms of behavior in the company is essential for any company that wants to have a proactive view of its market niche.
Without real-time analysis and monitoring tools for capturing sudden changes in customer behavior, it is only possible to take reactive postures that can weaken the market position.
5 Examples of hyper-personalization in different sectors
Ok, enough theory; let’s look at the brands leading the way in captivating their customers and getting ahead of the competition with five examples of hyper-personalization from five different industries.
1. Retail: Amazon
What they do: Whenever a customer lands on Amazon’s homepage, it feels like it’s been designed just for them. This is thanks to an algorithm powered by predictive analytics and item-based collaborative filtering. Collaborative filtering looks through a user’s purchased items and the purchase histories of other people who bought the same product to provide “frequently bought together” information on their product listings.
How they go the extra mile: Amazon looks at purchase history with browsing data to make connections to what is being searched for to link them to similar products; for example, you might be thinking of buying a fish and then be sent a recommendation to watch Finding Nemo.
The impact: Amazon’s recommendation engine is responsible for 35% of total revenue.
2. Entertainment: Netflix
What they do: Netflix’s recommendation system is one of the planet’s most sophisticated AI-based recommender systems.
How they go the extra mile: It tracks many things to ensure they understand what you want to watch. Among other things, they track:
- interactions with the service;
- the time of day you watch;
- how long you watch for;
- And the devices you are watching Netflix on
The impact: 75% of Netflix users watch content from the recommendations offered to them. Plus, Netflix believes it could lose over $1 billion a year from subscribers quitting its service if it wasn’t for its Recommendation Engine.
3. Financial Services: Bank of Ireland
What they do: The Bank of Ireland is leading the way in changing the face of banking. Bank of Ireland implements data science, artificial intelligence, machine learning, and analytics.
How they go the extra mile: Known as the “Netflix of Banking,” Bank of Ireland uses technology to accurately recommend the right products and offerings, depending on what’s happening in the lives of their customers.
The impact: Leading the way in innovation and taking banking in a new direction seems to be paying off, as Bank of Ireland reported average six-month profits before tax of €419m.
4. Health & Beauty: Sephora
What they do: The cosmetic industry brand offers personalized cosmetic experiences to engage offline and online users and provide solutions. They provide a user-friendly website that recommends products based on user profiles and customized beauty products that meet the user’s needs.
How they go the extra mile: Sephora Beauty Advisor (Sephora’s digital platform) lets users browse and purchase products based on skin type, age, and gender and then sends them new ones to try out. In addition, it does the classic of recommending similar products based on the user’s previous purchases and specific searches.
The impact: Sephora is considered one of the leading beauty retail brands globally, and it’s all down to its personalized approach. With over 2700 stores in 35 countries, their success is apparent.
5. Insurance: Allstate
What they do: The auto insurance company offers personalized car insurance using the telematics program Amelia and a mobile app.
The app monitors customers’ driving behavior and provides feedback after each drive. Customers also receive incentives for safe driving.
The app interface lets clients check their rewards and driving behavior for the last 100 trips. The customer’s premium is then calculated based on factors like speeding, abrupt braking, and the time of the journey.
How they go the extra mile: The program lets customers pay insurance based on the miles covered. So, the app monitors the distance covered by the car, and low-mileage drivers can save on insurance. Plus, even if you don’t have an Allstate insurance policy, you can still participate in this program.
The impact: Allstate is now America’s second-largest publicly traded insurance company. Allstate has a customer satisfaction rating of 832/1000 – above the national average score.
For more on how to improve CX take a look at our: Complete Customer Experience Guide Ebook