What We Know About AI-Powered Personalization So Far (Examples Inside)

The article was originally published on the CHI Software blog.

How well do you know your customers? The answer to this question has become increasingly important since the 1990s.

The first steps toward personalization were made when CRM systems appeared and marketers accessed basic customer data (names, location, age, gender, etc.). Now, in 2023, businesses can analyze the client’s behavior and make the most personal offerings ever using AI-powered personalization. How can you as an entrepreneur use innovations to your maximum benefit?

There is no single clear-cut answer to this question, so we will not waste a single minute. Our article uncovers the difference between traditional and AI-driven personalization with use cases, changing customer preferences, and implementation tips.

How to Be Hyperpersonal? AI-Based Personalization at a Glance

AI personalization (or hyper-personalization) means creating unique offerings tailored to individual user preferences and behavior. It is not only about their name or age anymore but about a wide range of actions made.

There are three types of data processed by algorithms:

• Demographic. This data is used in both AI-infused and traditional personalization, including names, titles, genders, email addresses, etc.;
• Behavioral. It is a unique combination of actions made by a specific user, such as pages visited, time spent on a website (app), call center interactions, an acquisition channel (for instance, Google search or emails), and many more;
• Contextual. It is any type of data that adds context to behavioral patterns, which may include a browser used, device, operating system, and so on.

Detailed information allows algorithms to create a real-time offering to an individual customer and not a group of similar users. Needless to say, achieving such a high level of personalization is labor-intensive for humans, but artificial intelligence copes with it easily.

Apart from efficiency, AI offers marketers detailed target audience insights and provides more space for experiments in marketing campaigns. Specialists also mention increased engagement, a better understanding of clients, and higher conversion rates among the benefits brought by AI innovations.

In fact, not only marketers notice the benefits of personalization. Here are some insights from the customer’s perspective.

5 Examples of How to Use AI for Personalization

Personalization is a broad sphere with industry-specific use cases. But our task today is to review the most common practices used by businesses of various sizes and domains.

1. Personalized content

AI-powered marketing enables extraordinary detailing. Now businesses can pay special attention to every bit of content that greatly impacts customer reactions. Starting from a subject line up to greetings and recommendations based on previous interactions, brands can create unique messages for each of their clients.

As an example, take Thread. Now owned by M&S, it was a marketplace aimed to provide recommendations based on the customer’s style, budget, previous feedback, and individual body parameters. The brand’s stylists used a machine learning algorithm to deliver unique messaging to every buyer. The secret was to ask a new user several questions to form their unique portrait.

This practice is now common for many other companies, including too-well-known Netflix and Spotify. On the Thread website, a sign-up test was more detailed and asked, among other things, several lifestyle questions. Collected data allowed a team of less than ten stylists to tailor unique recommendations to more than half a million buyers.

2. Chatbots

Embedded conversational robots (chatbots) are another way to collect comprehensive user data. It is a program using AI and NLP (Natural Language Processing) to provide personalized interactions and collect user queries and feedback.

Not only can these smart robots easily understand requests and provide relevant responses, but they also collect valuable user data. It is a proven way to build up an NLP-based recommendation system.

Banking is one of the industries where chatbots have already started a revolution. The DNB bank has used AI powers to automate nearly half of in-chat communication. To improve customer service, the company hired 15 employees who train the algorithm. A single chatbot cannot yet replace a human agent completely, but it can surely take up all FAQs and even provide recommendations based on the available client’s profile.

3. Retargeting

Every visitor leaves a trace on your web platform by clicking on product pages, browsing through catalog categories, adding items to the cart, etc. You can use all these actions to your benefit with intelligent retargeting. While you are busy creating attractive ads, machine learning algorithms can give you a hint about the content appealing to your target audience.

4. Notifications based on certain events

This use case applies to mobile app users in particular. You know for sure that those who downloaded your application are already interested in your brand. Now you can improve their experience by reacting to certain events, such as a changed location, a product from the wish list appearing in stock, an unfinished purchase, etc.

In 2019, McDonald’s announced personalized menus adapted to user preferences and surroundings (time of the day or weather conditions). If, for example, the weather is rainy, users will get notifications offering hot tea or coffee.

5. Sentiment analysis

Sentiment analysis relies on NLP, biometrics, computational linguistics, and other spheres to extract valuable information from the vast volumes of raw data. Let us say, an algorithm has to analyze customer opinions about a product. It will scan text reviews, distinguish words with positive and negative connotations, and make a conclusion. At the moment, algorithms are learning to analyze less explicit sentiments.

In 2019, Gillette’s “We Believe” advertisement caused an ambiguous reaction both in the media and among consumers. The video got more than two million views in 48 hours, and as of April 2023, it reached 38 million views. Media headlines focused on negative comments about the ads, so the overall public reaction also seemed negative. But sentiment analysis showed that reviews carried mostly positive connotations.

Final Thoughts

Can a business survive without AI personalized recommendations? Yes, probably. But no one knows for sure if a business will remain successful with no AI tools implemented in the future. Algorithms in the right hands can do miracles: they save tons of your time, provide a competitive advantage, and increase customer loyalty.

Continue reading this article in full on our blog.