Personalization is the process of delivering a customer experience that matches unique individual preferences. Personalization is usually enabled by AI (AI) algorithms, which collect and analyze user data then offer relevant suggestions.
Studies indicate that personalization can prevent cart abandonment, ensure customer loyalty, and increase ROI. a replacement Epsilon study, for instance, discovered that 80% of consumers are influenced into making a sale when served with personalized offerings. Read on to find out how AI is improving the personalization of customer experience.
1. Hyper-Personalized Retail Website Experiences
Retailers and eCommerce companies are using AI to personalize online experiences. The goal is to display relevant, context-sensitive products and offers which will appeal to every customer, supported their history and preferences.
AI can help improve retail customer experience in several ways:
Improve purchasing recommendations—use customer preferences and therefore the behavior of “similar” customers to recommend more relevant products, up-sells, and cross-sells.
- Increase conversion rate—modify product page and handcart behavior to stress items or actions most appropriate to the present user.
- Share targeted content in real-time—while the customer is visiting the web site, and later when executing email campaigns, AI can help push content and offers that are presumed to be acted on by each customer.
An instructive example is how Booking.com uses machine learning and deep learning to personalize their user experience:
- Predicting next destination—a recurrent neural network (RNN) analyzes a sequence of locations during a user’s itinerary and suggest a next destination that the user is probably going to enjoy.
- Selecting the foremost appropriate product—multi-class prediction models help show the proper content to the proper user. for instance, Booking.com infers the traveler type from the search query they typed in, and tries to predict the user’s response to different types of accommodations—private homes or hotels.
To add personalization, you’ll build your own AI algorithm, otherwise, you can leverage existing AI solutions dedicated to personalization marketing.
2. Automated Email Content Curation
Marketing teams spend major efforts building segments within their marketing lists, scheduling weekly emails to customers, and customizing those emails for every segment. the matter is that segments only roughly characterize each customer’s preferences and wishes. the perfect would be to personalize each email specifically for every customer.
AI classification algorithms like multi-layer perceptrons, also as traditional machine learning classifiers like Naive Bayes and decision trees, can capture an outsized number of knowledge points describing an inventory subscriber’s behavior, and dynamically select the simplest content for every individual.
Dynamic emails are often assembled supported data points such as:
- Previously read content
- Emails the user opened and links they clicked
- Previous sessions on the web site
- Previous purchases
- Purchases or similar customers
3. Personalizing Travel Website Searches and Bookings
Travel websites can dramatically increase conversion and revenues by personalizing elements of the invention and booking experience using machine learning. There are many approaches to personalizing travel content, below are two advanced and highly successful ones.
This approach uses preferences data users provided when registering for the web site or when purchasing previous products, then correlates the info with product features.
For example, solo travelers could also be curious about a swimming bath in their hotel during the summer, while business travelers are more curious about having a robust Wi-Fi connection in their rooms. Accommodations and flights always have distinct attributes, usually available as metadata that’s defined for every property, which will be matched with individual preferences.
The downside of the content-based approach is that it cannot match offers to customers supported implicit features of the merchandise . for instance, a gaggle of users may prefer hotels with a more modern look or a bold design, or could also be influenced by external reviews a few hotel’s service—data that’s not captured within the property metadata. an equivalent goes for restaurants and other attractions, which supply a holistic experience that can’t be described by structured metadata.
This approach suggests products to users supported products selected by “similar” users. the thought is that a user is probably going to reply well to a customer than another, the similar user already liked. This approach is extremely useful for offerings that don’t have explicit characteristics, or where metadata is missing for key characteristics.
Collaborative filtering is often performed using many machine learning methods. a standard method is matrix factorization. The matrix factorization algorithm creates a profile matrix for every user, filling within the parameters already known for one user (for example, demographic information), and taking the missing data from other, similar user profiles (for example, purchase information for users who haven’t purchased yet).
Matrix factorization was traditionally performed using linear algorithms like Funk MF and SVD, but there’s a shift to non-linear deep learning approaches, which are more expressive and may capture more implicit attributes.
There are many uses for AI-powered personalization. The customer of 2020 wants a user experience that delivers taste-matching content. As retail giants like Amazon still personalize offerings, customers become more familiar with convenience. This level of hyper-personalization can only be achieved with AI algorithms, which are trained to find out user behavior and serve them with customized offerings.