What Is Hyper-Personalization? How it Works & Best Practices

Hyper-personalization relies on data—and lots of it. By collecting and analyzing data on individual customers from multiple sources, you can build rich profiles that reveal their unique interests, behaviors, likes, and needs.

This data can come from more obvious sources like previous purchases, browsing history, and subscribed content. However, it also uses contextual data like your customer’s location, time of day, device used, weather conditions, and much more. Whatever information you can access helps shape your personalized messaging.

All of this data feeds into algorithms and business rules that dynamically build each individual's user experience. You’ll instantly roll out the personalized marketing strategy across several channels, including your website, mobile app, emails, ads, etc.

For example, an ecommerce brand might show different product recommendations to its site visitors based on the season in their region, day of the week, purchase histories, and recent searches. A media company may mix up the articles, videos, and ads shown in each user’s feed based on what content they consume, when they read or watch it, and their most up-to-date interests.

The key is using all the available first and third-party data to create a multidimensional profile for each user. This detailed profile then informs all the adaptive content, messaging, offers, and experiences you deliver through your digital channels.

More unique, relevant marketing helps you connect with your customers and leads to better engagement and results.

Marketers have been personalizing their campaigns for years—that’s no secret. However, while there are similarities between traditional and hyper-personalization, the differences help businesses take their efforts to the next level.

Traditional personalization typically relies on broad segments or basic customer data, such as a customer’s name, location, or previous purchases. For instance, an online retailer might show shoppers from different countries differing product categories on their homepage.

Hyper-personalization goes much further by tailoring every piece of content and the entire user experience at an individual level across all your touchpoints. It uses more diverse data sources and advanced algorithms and adapts in real-time.

Let’s use the same online retailer example. Rather than showing the same products to all customers on the west coast, a hyper-personalized engine could factor in their search history, browsing behavior, social data, local weather, time of day, and many other variables to display a completely unique experience for each individual.

This becomes incredibly valuable when we consider the nuances of your customers. Basic personalization might identify two 55-year-old men living in California, consider them similar, and show them identical products. Hyper-personalization considers their individualities (maybe their exact location, job role, marital status, if they have kids, etc.) and presents them with items they’re likelier to want. It doesn’t just create a profile that’s like them, but one that is actually them.

Hyper-personalization is also continuous and omnichannel. As customers interact with your website, mobile app, email, ads, and other touchpoints and share more information, their profiles are constantly updated. This then adjusts their experiences in each channel based on their latest interests and the most up-to-date context.

Hyper-personalization offers substantial benefits for businesses, but it’s not without its challenges. Like any advanced marketing strategy, there are trade-offs to consider.

You must evaluate both sides of the coin against your organization’s resources and your team’s ability to overcome hurdles. Here are some of the main pros and cons.

Pros

• Increased engagement and conversions: Hyper-personalization helps drive higher engagement, purchases, and other desired actions by providing more relevant and tailored experiences.

• Improved customer loyalty: Customers feel seen and listened to when brands adapt their experiences to their needs. This strengthens loyalty and retention.

• Competitive differentiation: Hyper-personalization enables you to stand out from competitors who may still use basic segmentation. You can offer something more helpful and familiar.

• Efficient use of resources: Customizing per individual focuses your marketing efforts on spending on prospects most likely to convert. Find repeat buyers and use personalization to upsell.

• Richer data and insights: The user data you collect can reveal powerful insights about your customers, which can inform multiple strategies.

Cons

• Data collection challenges: Gathering enough varied user data for profiles can be difficult. Implement tracking across all your customer touchpoints, incentivize customers to share their data, and responsibly collect data from third-party sources.

• Implementation complexity: Integrating multiple data sources, rules, and technologies for omnichannel personalization is complex. Invest in a consolidated customer data platform (CDP) and personalization engine technologies to centralize your data and automate the experiences.

• Needs advanced analytics: Deriving insights and optimizing from large user data sets requires advanced analytics capabilities. Build data science teams or use external analytics services. Use machine learning (ML) to continually optimize personalization rules and models.

• Potential privacy concerns: Tracking detailed individual data can raise privacy issues if mismanaged. Be fully transparent about how customer data is collected and used. Allow customers to opt-out and access or edit their data. Follow data privacy regulations.

• Organizational resistance: Breaking down silos and shifting processes or mindsets for this customer-centric approach can be tricky. Get buy-in from leadership and share your vision. Break down between marketing, product, and analytics teams, and adopt agile working methods.

Hyper-personalization is already in action in many of our favorite brands and products.

Here’s how some of the biggest names in the business use hyper-personalization to deliver even better customer experiences.

Netflix

Netflix is a prime example of effectively using hyper-personalization with its recommendation engine.

By analyzing individual viewing histories, ratings, search data, devices used, and other behavior signals, Netflix customizes each user’s homepage and recommends content in a highly personalized way. Their algorithms can personalize the artwork, metadata, and video thumbnails to increase relevance.

The brand is also well-known for using A/B tests to help drive its decisions. On average, Netflix selects roughly 100,000 customers to test a hypothesis. This is partly why every person has a different experience on its platform.

Amazon

We can’t talk about hyper-personalization without mentioning Amazon. The ecommerce giant relies heavily on hyper-personalization across its entire customer experience.

From the recommended product listings adapted to each user’s interests to the customized Amazon homepage and marketing emails and ads—everything is driven by personalization algorithms that analyze heaps of customer data.

It uses deep learning, an extension of AI and machine learning. Deep learning helps Amazon determine which product a customer will likely need or buy next—it can then recommend the item to the user when they next log in to the site.

Spotify

Like Netflix, Spotify uses hyper-personalization to adapt its content recommendations—Discover Weekly and other bespoke playsuits are perfect examples.

The unique algorithms note what people are listening to and highlight similar-sounding tracks and artists. By understanding its listener’s tastes, Spotifty ensures it delivers a great experience every time a user opens the app and (more important) keeps returning to explore more.

It also personalizes email content, homepage design, and advertising campaigns for different audience segments based on their listening habits.

One of the more recent and useful additional features is the “Events” category—users are shown details of nearby gigs and festivals where their favorite artists are performing. Spotify also recommends popular concerts near the listener’s location that might interest them.

Starbucks

The Starbucks mobile app is an excellent example of using hyper-personalization to enhance the customer experience.

Based on the customer’s purchase history, location data, and personal preferences, the app can intelligently surface customized order recommendations, highlight nearby stores, and suggest items the person will most likely want.

The loyalty app has been around since 2011, providing Starbucks with a wealth of valuable data to work with. It analyzes the information to understand each individual's likes and habits.

With these insights, Starbucks can send personalized offers and deals, encouraging loyal customers. This is also a large reason its customers are happy to share their information with the brand—they get a relevant (and tasty) treat.

Getting started with hyper-personalization is an exciting, optimistic process. However, before you start redoing your marketing or product roadmaps, remember a few best practices.

Build a unified customer view

Creating a unified view of each customer across all your systems and channels is key. You’ll likely need a CDP to help you integrate and stitch together whatever you have—profile data, behaviors, recent transactions, etc.

This provides a single data source for everything about that customer—a gold mine for creating truly individualized targeting and product experiences.

Focus on high-value cases

Rather than trying to personalize everything all at once, identify and prioritize the high-value and most impactful use cases—the areas that will drive maximum returns. These might include recommendations, website experiences, mobile app features, email marketing campaigns, and other major customer touch points.

Beginning with these priority cases enables you to prove the value of hyper-personalization before scaling your efforts.

Use AI and machine learning

The immense data volumes needed and the heavy task of figuring out the most effective personalizations can be overwhelming. Luckily, AI and machine learning are designed to handle these massive, complex challenges.

AI can derive insights from large datasets, automate segmentation, predict customer behavior, and continually refine your personalization strategies through reinforcement learning.

Just as you learn by spending more time with your customers, AI heightens its knowledge and recommended actions by getting to know the data.

Have an omnichannel approach

You must consistently personalize customer experiences across all your channels and journey touchpoints.

Your personalization engine and processes should enable a seamless omnichannel rollout, whether you’re personalizing a website, app, email, ad, or customer service interaction.

Balance automation and human input

Although automation and AI decisions are helpful, you should balance this with human expertise.

Ensure your marketing and product teams can manually curate the content and override experiences if needed. With an actual understanding of your customers and product, they might spot things the machines have overlooked.

Be transparent and customer-centric

Build trust by being fully transparent about how your customers’ data is collected and used for personalization.

Make your privacy practices clear. Listen to your customers' wants and prioritize these over business objectives where possible. Give your customers the control to change their experience settings and what you do with their information.

Experiment, measure, and optimize

Personalization is an iterative process that requires methodically testing new strategies, measuring the results, and refining your methods based on the performance data.

Establish processes for testing your personalization models, trying new approaches, and optimizing over time. This mindset will help you improve your user experiences and get a better ROI.

Whether individualizing your digital product experiences or optimizing your marketing campaigns, Amplitude enables genuine, scalable, hyper-personalization.

Use the analytics and A/B testing platform to help you deliver truly differentiated value and set your business apart.

• Unify user data from across all your digital properties and sources. Get seamless access to multidimensional customer profiles to analyze and take action.

• Create ideal user experiences based on their unique behaviors and real-time context. Through predictive targeting and content tailoring, Amplitude ensures each touchpoint is adapted to maximize engagement and conversions.

• Tweak and refine your efforts with easy-to-use, no-code tools. Carry out controlled A/B tests and experiments to see what’s working across any digital channel.

• Get detailed, immediate insights into the entire customer journey. Measure what’s driving value, identify friction points, and make constant improvements.

Ready to see what Amplitude’s solutions can do for your hyper-personalization efforts? Request a demo today.