We are living in the era of the ‘Evolved Consumer’, where consumers actively seek information to make informed decisions and don’t place their trust implicitly in brands. Consumer Behavior today is heavily reliant on Reviews, User Generated Content & Influencers. So how do you make your brand stand out and get noticed?
By providing highly targeted, customized, and hyper-personalized experiences. That is what Hyper-Personalization is all about.
What is Hyper-Personalization?
Hyper personalization is a more advanced next step to personalized marketing where it leverages artificial intelligence (AI) and real-time data to supply more relevant content, product, and service information to every user.
In this blog, we will learn how brands like Amazon, Starbucks, Netflix & Spotify are fully embracing hyper-personalization going into 2020. Also, find industry-specific data points that can be used to create effective hyper-personalized campaigns for your business.
What is the difference between Personalization & Hyper-Personalization?
Personalization is the incorporation of personal and transactional information like name, title, organization, purchase history etc. to your communication. Hyper-personalization goes one step further and utilizes behavioural and real-time data to create highly contextual communication that is relevant to the user.
For example: Sending an email to a user with their first name in the subject line is a typical example of personalization. A good practice, but not engaging enough to capture a user’s fancy.
Hyper-personalization is more advanced. For example, a user browses for green shoes on your app, spends around 15 mins and leaves without purchasing. A quick analysis of the user reveals:
- An affinity for buying discounted goods
- Prior search and purchase history for ‘X’ footwear brand
- Maximum purchases happening on a Sunday from 6-9 PM in the night
- Push notifications having the highest user engagement in the past
Now, a hyper-personalized campaign would send a push notification to the user’s mobile device advertising a flash sale on X brand’s green shoes on a Sunday, preferably between 6-9 PM.
The need for Hyper-Personalization
- Your message only has 8 seconds to capture and hold the attention of your user. To get noticed, your communication needs to stand out and be clutter-breaking.
- According to Google, ‘best’ search phrases have increased by 80% in the past 2 years on mobile devices. People are searching online heavily to make more informed decisions.
- User engagement with content has gone down by 60%, and information overload is making consumers tune out.
- According to Accenture, 75% of consumers will be more likely to purchase from someone whose offerings are personalized according to individual preferences.
How brands are using Hyper-Personalization
Top brands like Amazon, Spotify & Starbucks have moved on to a stage of predictive personalization, where AI & machine learning analyze a whole host of factors to power their recommendation engine. By and large, most businesses that are dabbling with personalization generally don’t venture beyond segmentation.
Let us take a look at how some of the biggest names in business have achieved incredible growth trajectories with hyper-personalization.
Amazon is the king of ‘Me-Commerce’, with its recommendation engine powering 35% of conversions. Let us see how Amazon creates a unique, hyper-personalized experience for consumers:
I was scouring Amazon for Olive green running shoes but happened to leave my search mid-way. Soon after, Amazon sent this email:
The email goes one step beyond the regular personalization tactic of including my name.
Amazon has access to data points like Full name, Search Query, Average time spent on search, Past purchase history, Brand affinity, Category browsing habits, Time of past purchases, average spend amount, etc. Using this, they could create a profile and use that to craft a highly contextual email highlighting Olive green Puma shoes (Olive green was part of my search query & I have purchased Puma footwear in the past).
Amazon’s recommendation engine algorithm is called ‘item-to-item collaborative filtering’. It suggests products based on 4 data points:
- Your previous purchase history
- The items that you have in your shopping cart
- Items that you have rated and liked
- Items that have been liked and purchased by other customers
Compared to other E-Commerce brands, conversions from Amazon’s on-site recommendations are 60% higher.
Channel: Push & In-app Notifications
Starbucks has seriously upgraded its e-commerce personalization game with the use of AI. Using real-time data, the system can send over 400,000 variants of hyper-personalized messages. Offers doled out are unique to each user’s preferences, based on their activity and past purchases.
The Starbucks app interface is personalized for each individual user
- Starbucks Loyalty program is incredibly successful, boasting of over 13 million users.
- The app pushes Food and beverage suggestions personalized for each customer with an AI-based algorithm. It analyses past purchase history, tastes, and preferences to come up with recommendations for each individual customer.
- Starbucks engages loyalty program members with personalized games on email and mobile.Starbucks app sends in-app transaction message with reward details:
Starbucks informs users about closest stores that accept Mobile Order & Pay option
→ Increase in marketing campaign effectiveness by 3X
→ 2X email redemptions
→ 3X increase in incremental spends via offer redemptions
→ 24% of total company transactions happening via mobile app
With over 140 million active users, Spotify is the clear leader in the music streaming app industry. With over 5 billion streams, their Discover Weekly feature has been a massive hit. It 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.
With their Live Concert feature, Spotify sends information via email about live events of their favorite artists along with an option to buy tickets. The content is personalized based on the music preferences of each individual user.
To identify a song’s popularity, Spotify looks out for the number of times a particular song has been streamed, and how many people are adding it to their playlists. Say if X & Y both like the same song (based on stream count/playlist addition), then Spotify will recommend different songs from both X & Y’s playlists to each other. Further, Spotify’s AI-enabled recommendation engine analyses songs and the listening habits of its users and suggests similar sounding music to matching user profiles.
Channels: Email, Push Notifications
Streaming behemoth Netflix boasts of over 103 million users on its platform. This incredible success is largely because of providing its users with a highly personalized experience. More than 75 percent of site activity is driven by their personalization engine. With Netflix, the personalization begins right from the homepage.
Netflix users can assign a star rating to the content denoting their like/dislike. This, coupled with the stream count of the content and the individual user profile helps the algorithm predict content that would be favored by users. By successfully combining behavioral attributes with predictive learning, Netflix sends recommendations to its users about content.
How can you start off with Hyper-Personalization?
Every customer-facing business invariably collects data about users at multiple points. We list down some common data points for different industries that you can use to get started.