User experience is a crucial part of any website, platform, and mobile application. The goal is to make the experience positive, thus encouraging repeat usage until the desired call to action is achieved. Hyper personalization applies automation and smart insights for the purpose of serving each unique user customized content.
In this article, you will learn what is hyper personalization and how to create a personalized experience, including real-world examples.
What Is Hyper Personalization?
Hyper personalization is the use of customer data to create and present customized contacts, information, or recommendations to customers. These customizations are created based on individual customer profiles. Profiles are created using data from browsing patterns, purchase histories, geographic location, demographic data, and behavioral data.
Why Is Hyper Personalization Important?
Modern customers expect to be treated as individuals. According to a study by Accenture 91% of consumers prefer brands that “recognize, remember, and provide relevant offers and recommendations”.
Previously, companies had to rely solely on demographics and behavioral data to segment their customers. This allowed for some personalization but still required grouping customers and personalization remained a bit vague.
These vague personalizations cannot compete with the massive amount of marketing communications that customers receive. Each time a customer receives a communication like this, their positive brand experience is lessened. This puts company-customer relationships at risk.
Ways AI Creates a Personalized Experience
Using AI, you can create significantly more personalized experiences for your customers across a variety of channels. Some of the most beneficial uses are covered below.
Personalized website experiences
With AI, you can create a better experience for your online customers. For example, you can target collections to match customer interests or develop customized landing pages or microsites.
The main site interactions you can improve, include:
Customer-based product recommendations
Real-time content sharing
Visitor conversion rates
Channel selection and timing for content delivery
Improved understanding of context
Optimizing personalization requires understanding contextual relevance. The effectiveness of your personalization is directly related to the accuracy of the data you collect and the analytics you perform.
AI can help you use data more discriminately and can faster, more in-depth analyses. It enables you to use tools such as decision tree-based models or neural networks instead of less accurate linear models. With AI, you can also access natural language processing and sentiment analysis.
These tools enable you to more easily integrate unstructured data and to produce more “human” communications. For example, data from customer support calls or social media posts can be processed and integrated with customer profiles.
Mobile devices are becoming the primary Internet device for many consumers. However, many retailers are still creating sites and content designed for larger format devices. This makes content less effective and can create a poor experience for your users. AI can help you adapt your existing content to better fit the needs of mobile browsers.
Some specific ways AI can help, include:
Chatbots for easier and faster communication
Unification of data across devices to provide an experience customized to access point
Access to device details for effective personalization
Automated A/B testing for mobile apps
Leverage Internet of things (IoT) devices
IoT adoption has grown rapidly in the past few years, changing the way you and your customers can connect. These devices can provide a wealth of information that was previously inaccessible. With AI analysis, you can leverage this information to create a fuller picture of your customer’s lives and interests.
Examples Of Artificial Intelligence To Improve Personalization
A growing number of retailers are embracing hyper personalization to improve their customer service, increase their competitiveness, and improve customer loyalty. Below are just a few examples of how retailers are providing hyper personalized services.
Personal stylist from Thread
Thread, a UK-based fashion retailer, offers customers AI-based product recommendations in the format of a personal stylist. These “stylists” combine information collected from style quizzes and ongoing reactions to product recommendations and refine recommendations accordingly. This enables Thread to provide customized service for hundreds of thousands of customers with minimal additional effort or staffing.
Robot concierge from Hilton
Connie is a robot concierge that Hilton Hotels has begun placing in its lobbies. Connie can greet guests, answer questions, and provide standard concierge recommendations. The robot uses natural language processing capabilities to interact with guests and develop meaningful profiles to assist them.
Personal trainer from Under Armour (UA)
Although UA is known for clothing, they also want to reach customers in lifestyle interactions, such as health and fitness routines. To accomplish this, UA created the Record app, which collects user information on sleep, diet, and physical activity.
Using AI, this data is then analyzed to create personalized health goals and workout plans. When workouts are completed, the AI can also provide feedback on workout effectiveness to help users maximize their efforts.
Automated jeweler from Rare Carat
Rocky is an AI-powered jeweler that guides customers looking to buy a diamond. It can help customers compare prices and provide insights into how various factors affect the price and quality of any diamonds that customers are considering.
With this tool, Rare Carat can provide an immediate, customized experience to customers regardless of location or budget. It enables them to simulate the benefits of a physical store from the comfort of the web.
Virtual Stylist from Levis
Levis has also employed a personalized stylist, designed to help customers find the perfect pair of jeans. They’ve implemented this stylist via a chatbot with natural language processing abilities. Customers can chat with this bot to define what they are looking for in terms of fit and appearance.
The stylist then provides recommendations for styles based on a combination of customer data and training from live stylists. Included is an easy sharing feature that enables customers to share their experience and get secondary feedback.
Hyper personalization helps admins, marketers, and web owners serve each digital user with the content that fits their needs and desires. This is made possible by advancements in AI and big data analytics. Hyper personalization can help you personalize web experience, get faster data analysis results, improve mobile optimization, and leverage data from IoT devices.
Companies throughout the world are now using hyper personalization to provide a wide range of services, including virtual stylists, automated jewelers, AI-based personal trainers, and even a robot concierge services. As the world becomes more digital, hyper personalization can soon turn into an enabler of the unique experiences that keep users happy and engaged.
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.