What if your favorite streaming platform could scan your face to determine your sex, age, mood, and energy level, and then use this information to recommend content? This could be the future of dynamic hyper-personalization. Machine learning and AI-assisted experiences like this already exist in entertainment and media (E&M), and more will come. 

Research shows that 76% of consumers want to see personalized communications when they’re considering a brand. And 78% said personalized content made them more likely to repurchase from a brand.

The entertainment industry has a rare opportunity, with its media-hungry masses and direct access to user data. Read on to discover new ways to connect with and learn from your customers.

What is hyper-personalization?

First off, what is hyper-personalization? The latest buzzword in marketing, hyper-personalization goes way beyond email merge tags like “Dear [FIRST NAME]” and using search history to target ads. Instead, you gather data from behavioral and real-time customer journey analytics to build a rich, detailed user profile. 

An elevated personalized experience means identifying and understanding data like: 

• Customer browsing and search behavior, based on all-time and seasonal trends
• In-app behavior
• Content viewed or listened to 
• Content not viewed or listened to 
• Favorite genres, shows, artists, or directors, purchases
• Time of day and device certain shows or music are selected

Imagine customized welcome videos, dynamically generated content, personalized recommendations, and custom watch calendars. 

Spotify’s annual Wrapped campaign is a perfect example of using customer behavioral data to drive hyper-personalization. A unique-to-you video shows your “stats” for the year, like top songs, minutes listened, and most-listened-to genres. You can then share the video with friends and on social to compare tastes and habits. The campaign is successful for a few reasons–it’s a conversation starter, it promotes sharing, and it makes each user the center of attention.

Disney Plus is also using predictive customer journey analysis to improve user experience. For example, the Post Play feature (which auto-plays something new after your program ends) learns from past patterns of when content is played in relation to each other. In other words, “After X happened, Y was the most common next thing to happen.” With this information, they can choose the show or movie they will most likely watch next.

Benefits of hyper-personalization in media & entertainment

If the success of media giants like Amazon, Spotify, Netflix, and Disney aren’t convincing enough, McKinsey found:

• Personalization in marketing can reduce customer acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and raise marketing ROI by 10 to 30 percent. 

Here’s what hyper-personalization can bring to the table for media brands:

• Boost customer engagement, especially by combining physical and digital outlets
• Increase customer satisfaction and loyalty by creating a consistent and highly personalized experience 
• Keep customers longer by spotting and preventing customer churn 
• Improve NPS by providing better customer experience and support 
• Make informed decisions with insights based on real-time, cross-channel data

10 examples of how marketers are using hyper-personalization

To get your creative gears turning, let’s explore some of the ways CMOs, ad buyers, product marketers, and other E&M leaders can use hyper-personalization. 

1. 1. Use customer behavior data, such as preferred channels and content affinities, to build a predictive customer journey analysis. Then, time personalized messages or offer upgrades based on the highest engagement times.

1. 2. Use predictive retention modeling to find customers who are at risk of churning. Then, offer them special discounts or upgrades as a retention strategy, or satisfaction surveys to try and improve their customer experience. 

1. 3. Use hyper-personalization to make emails, SMS, and in-app messaging more customer-centric. For example, you can choose memes or gifs based on their favorite shows, movies, or actors.

1. 4. A streaming site could remember commonly used settings per profile or user on a multi-user plan–like subtitle and language preferences—and then suggest or set those automatically. 

1. 5. An entertainment brand could use machine learning algorithms to continually test different marketing messages and creatives, and optimize campaigns on the fly based on performance metrics.

1. 6. Use geolocation and language preferences to offer content and messaging in regional languages and dialects. 

1. 7. Optimize audience segmentation to drill down messaging and content across channels. For example, one user sees an ad for The Mandalorian, while another sees an ad for Frozen. Or, two people see the same ad, but with different lead-in copy. 

1. 8. Utilize first-party data (like website behavior, purchase history, etc.) to build hyper-relevant recommendations. For example, a video streaming platform could build personalized Friday night watch lists for customers based on their usage history.

1. 9. Do a predictive analysis to understand customer behavior and optimize your ad campaigns for a new show or streaming plan promo accordingly, such as only serving ads to customers who are most likely to convert.

1. 10. Analyze sales data to dynamically adjust ad prices in real-time, based on factors like demand and inventory levels. For example, consider how the target audience for Taylor Swift fans may quickly engage with ads now that she’s announced a film version of her Eras Tour (and any other upcoming video releases). Platforms like YouTube could leverage real-time data and predictive analysis to adjust ad prices for upcoming music video premieres for Swift’s Taylor’s Versions.

Who knows, brands might even begin using virtual reality and AI to create entirely computer-generated celebrities. Like a 1990s-era Star Trek holodeck, the future of entertainment is poised to become a deeply personal and immersive experience, crafted around the individual’s requests and preferences. 

Total global E&M revenue hit $2.32 trillion (with a “T”) in 2022. As more E&M products go digital, what will your brand do to embrace hyper-personalization and build a segment of one? 

To learn more about ML-driven decision intelligence for better business outcomes, talk to an expert today.