December 19, 2018
Hyper personalized marketing
Marketing teams in companies like Netflix, Spotify, and Amazon are working tirelessly to drive hyper-personalized experiences to their customers. Driven by detailed data and clever AI, they are pushing the boundaries of what it means to be “personalized.”
They’re analyzing the behaviors, interests, and the needs of their customers. Why? Because it makes sound business sense. According to Evergage, 88% of US marketers see measurable improvements due to personalization — with more than half seeing a lift greater than 10%.
Hyper-personalization and marketing: a winning combo
The fact that consumers increasingly want hyper-personalization is not hyperbole. Recent surveys have revealed that:
- 40% of consumers are comfortable with having a retailer monitor their shopping patterns and purchases (2018, PwC)
- Personalized eCommerce and retail experiences result in higher revenue, fewer product returns, and greater customer loyalty (2017, Segment)
- Personalization leads to increased revenue: 40% of U.S. consumers say they have purchased something more expensive than they planned to because of a personalized service (2017, Segment)
All in all, it’s clear that hyper-personalization and marketing belong together.
What is hyper-personalization?
Long story short, there isn’t any real agreement on what hyper-personalization is. But as a general rule of thumb, hyper-personalization takes traditional personalization and turns it up to 11.
Traditional personalization uses data to “profile” your audience into groups with similar traits. It can segment them together, then take customers’ first names and pops them into the [First Name] merge tag field in an email.
Hyper-personalization goes beyond simple demographic segmentation and merge tags. It allows you to set a user’s browsing, purchasing, demographic, and real-time behavioral data as a foundation for tailored communication. This ensures that your marketing messages and experiences contain highly tailored content, products, or services.
Marketers who head down the path to hyper-personalization have more opportunities to tailor content that fits customer wants and needs on an individual level.
Currently, to execute hyper-personalization with any kind of accuracy or scale, you’ll need a lot of data. The end result is extremely worthwhile, but currently out of reach for many. Though with the advent of autonomous marketing just around the corner, this high barrier to entry may lower significantly in 2019.
Personalization vs. hyper-personalization in more detail
To get a real grasp on hyper-personalization, it helps to first look at what traditional personalization looks like today.
Facebook uses traditional personalization to segment its users into “audiences” based on age, gender, location, and declared interests.
As a marketer, you can use this segmentation functionality to target audiences who will be most interested in your product. Since you’ve selected an audience that matches your product, the advertising feels personalized. This helps to improve click-throughs since the audience is seeing content that is likely to interest them.
Customers who have declared their personal information can receive personalized messages, too. For example, if you’ve created lead generation advertisements on Facebook, you can message leads directly with follow-up communications that are personalized with first names, interests, or any other declared information.
If you were to use the same examples in the context of hyper-personalization, then you would add historical and real-time analysis of each customer’s specific browsing, purchasing, and behavioral patterns. So those same follow-up communications could include “products you might like” (based on past purchases) or “content for you” (based on previous content engagement).
With tracking pixels, data analysis and smart segmentation, you can track specific users on your website (or other channels), then retarget them across multiple touchpoints. Autopilot provides much of this functionality through page tracking triggers, integrated analytics software, and the smart segments integration.
Hyper-personalization is made possible using a mix of technologies such as artificial intelligence, machine learning algorithms, advanced audience segmentation, and scalable automation products to name a few.
It allows marketers to identify the subtle details that traditional personalization just doesn’t look for. It’s made possible through, third-party tracking pixels, integrated apps like Segment, sophisticated CRMs like Pipedrive, and (if your budget allows) the collection of third-party data.
Spotify’s AI-powered personalization
In practice, hyper-personalization helps customize shopping experiences, qualify leads, and streamline the customer experience. As one of the leaders in hyper-personalized content, Spotify has some great lessons to share in creating marketing campaigns that are perfectly tailored.
Breaking down listening habits
Spotify has long examined it’s user’s listening habits to deliver accurate recommendations. It’s Discover Weekly feature is a huge success, relying on an AI algorithm to provide highly personalized in-app playlists.
The algorithm studies individual music choices and cross-analyzes this data with the preferences of other users who listened to the same songs to create a highly-personalized playlist for each user. If two users like the same song (based on stream count/playlist addition), Spotify recommends each user a different song from each other’s playlist. This is also the basis for how “recommended playlists” and “radio” recommendations are generated.
Discover Weekly generates billions of new streams for Spotify through AI-powered hyper-personalization.
On average, personalized email marketing improves click-through rates by 14% and increases conversions by 10%. Spotify’s email marketing journeys take advantage of this fact, and then some.
They go beyond just writing “Hi –First Name–!” and instead deliver messages that are based on preferences and behavior. Just look at the email below: it’s offering a limited edition vinyl for an artist that I listen to all the time.
In this particular email, Spotify is driving sales for charity, since the artist recently passed away. I’m much more likely to convert since I know the artist, I know the story behind the email, and it’s something I care about. It’s an elegant example of how traditional and modern personalization strategies can work together to engage audiences.
Driving engagement with Spotify Wrapped
Spotify’s 2018 Wrapped was recently released to users who have been engaging with the platform all year. Clicking through on the CTA for Wrapped, customers are led through their listening habits, favorite songs, and statistics based on other users.
Users are given sharable playlists like “Your Top 100” and prompted to “Share Your 2018” at the end of the “wrapped” experience. The brand gives customers the opportunity on social channels, WhatsApp, and even billboards in major cities.
There’s no finite definition of hyper-personalization — it’s up to you where you want to draw the line. Evaluate your level of personalization and think: “Can I do more to drive growth here?” — it’s likely that you can.
And it’s exactly what your customers want.
Companies across the world are implementing similar personalization methods in their customer journeys and marketing strategies. Learn more about brands that have dominated the market using artificial intelligence to drive hyper-personalization.