3 Ways AI is Helping Personalize Customer Experiences

Personalization is the process of delivering a customer experience that fits unique individual preferences. Personalization is typically enabled by Artificial Intelligence (AI) algorithms, which collect and analyze user data and then offer relevant suggestions.

Studies indicate that personalization can prevent cart abandonment, ensure customer loyalty, and increase ROI. A New Epsilon study, for example, discovered that 80% of customers are influenced into making a purchase when served with personalized offerings. Read on to learn 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 that will appeal to each customer, based on their history and preferences.

AI can help improve retail customer experience in several ways:

• Improve purchasing recommendations ‘use customer preferences and the behavior of similar customers to recommend more relevant products, up-sells and cross-sells.

• Increase conversion rate ‘modify product page and shopping cart behavior to emphasize items or actions most appropriate to the current user.

• Share targeted content in real-time ‘while the customer is visiting the website, and later when executing email campaigns, AI can help push content and offers that are most likely 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 in a user’s itinerary and suggest a next destination that the user is likely to enjoy.

• Selecting the most appropriate product ‘multi-class prediction models help show the right content to the right user. For example, 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 can build your own AI algorithm, or 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 each segment. The problem is that segments only roughly characterize each customer’s preferences and needs. The ideal would be to personalize each email specifically for each customer.

AI classification algorithms such as multi-layer perceptrons, as well as traditional machine learning classifiers like Naive Bayes and decision trees, can capture a large number of data points describing a list subscriber’s behavior, and dynamically select the best content for each individual.

Dynamic emails can be assembled based on data points such as:

• Previously read content
• Emails the user opened and links they clicked
• Previous sessions on the website
• 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 discovery and booking experience using machine learning. There are many approaches to personalizing travel content, below are two advanced and highly successful ones.

Content-based filtering

This approach uses preferences data users provided when registering for the website or when purchasing previous products, and then correlates the data with product features.

For example, solo travelers may be interested in a swimming pool in their hotel during the summer, while business travelers are more interested in having a strong Wi-Fi connection in their rooms. Accommodations and flights always have distinct attributes, usually available as metadata that is defined for each property, that can be matched with individual preferences.

The downside of the content-based approach is that it cannot match offers to customers based on implicit features of the product. For example, a group of users may prefer hotels with a more modern look or a bold design, or may be influenced by external reviews about a hotel’s service ‘data that is not captured in the property metadata. The same goes for restaurants and other attractions, which offer a holistic experience that cannot be described by structured metadata.

Collaborative filtering

This approach suggests products to users based on products selected by similar users. The idea is that a user is likely to respond well to a customer than another, 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 can be performed using many machine learning methods. A common method is matrix factorization. The matrix factorization algorithm creates a profile matrix for each user, filling in 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 is a shift to non-linear deep learning approaches, which are more expressive and can capture many more implicit attributes.

Conclusion

There are plenty of uses for AI-powered personalization. The customer of 2020 wants a user experience that delivers taste-matching content. As retail giants like Amazon continue to personalize offerings, customers become more accustomed to convenience. This level of hyper-personalization can only be achieved with AI algorithms, which are trained to learn user behavior and serve them with customized offerings.