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8 examples of AI personalization across industries

Through AI content personalization, organizations can build unique profiles of users and customers and tailor their products, advertisements and services to better fit them.

The goal of marketing teams at product and service enterprises is to deliver the right product to the right person at the right time.

Personalization tailors products and services to the specific needs of the individual users, and using the power of big data and machine learning algorithms, organizations can build detailed profiles of each individual and automatically customize offerings to those individuals.

It is through this combination of AI and content marketing that organizations have achieved an even more in-depth look at their potential customers and current users through hyperpersonalization. 


Through hyperpersonalization, organizations can build a unique profile of individual users and customers and have that profile learn and adapt over time based on behavior.

Hyperpersonalization uses customer information to provide personalized content, products and services to match customers' preferences. The data used includes profile data, user location, browsing history and purchasing decisions.

AI-based personalization aims to deliver optimal customer experiences in real time, targeted specifically to that individuals' needs instead of grouping people together into broader categories.

Personalization is dependent on the ability to make offers that adjust to changing consumer behavior and preferences. It also must be able to respond to organizational requirements and other external influences.

Common use cases for AI-enabled hyperpersonalization

AI personalization helps organizations increase engagement, improve customer loyalty, increase sales and better understand their customers. While some use cases are industry-specific, such as personalized healthcare and treatment, other use cases can be applied more broadly.

  1. Personalized content -- Brands now understand that not all users are the same and content isn't a one-size-fits-all approach. To resolve this issue, organizations need to shape their content to the individual. For example, if the customer is using mobile technology, brands can automatically push personalized content and offers based on the customer's location.

Additionally, some companies such as Starbucks and McDonalds are looking to personalize the user experience at the point of purchase. In 2019, McDonalds announced that it was working to personalize menu boards at drive-throughs for customers. With the power of machine learning, the digital menu boards at the drive-through can take into account factors that may influence diners' ordering decisions and dynamically change the menu accordingly. For example, in colder weather the menu may push hot coffee or tea.

This idea is being further applied by Thread, a UK-based fashion company. The company uses AI to provide personalized clothing recommendations for each customer. Customers take style quizzes to give the company data on their style and the company can come back with personalized recommendations based on that specific customer's likes and dislikes.

  1. Personalized messaging -- By building more personalized profiles, companies are now able to provide more personalized and targeted messages to their users and customers. For example, if my bank knows that I am moving, they can provide me with targeted messaging around mortgage rates and deals. Or, if a home improvement company knows that I recently purchased a home, they can use personalized customer data to tailor email content to the individual leading to a higher likelihood of conversion than sending me more general messaging.
  2. Personalized ad targeting -- Machine learning is helping companies place better, more targeted ads based on a variety of factors in near real time. These factors include demographics, buying history and behavior and can help appropriately match advertising requests to the right audience at the right time at scale. Without the help of AI and machine learning, this is borderline impossible.
  3. Product recommendation -- With the help of machine learning algorithms, companies are able to move away from rules-based recommendation systems to more intelligent systems. By looking at data points, such as which products a customer added to their cart or previous purchases, brands are better able to suggest related or relevant product recommendations in real time. Amazon recently made their ML-based recommendation system available for purchase to help companies of all sizes create real-time personalized user experiences at scale. Named Amazon Personalize, this service makes it easy for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product re-ranking and customized direct marketing.
  4. Personalized websites -- For companies that are driving traffic to their website, they understand that not every user benefits from the same content. Sites that are using big data and machine learning are able to change website content depending on which visitor is on the site. Taking into account a variety of data points, including purchase data and other live or past behavior on the site, they are able to dynamically change content displayed on the screen.

With the help of AI, companies are able to automatically customize the content on their website based on the user to provide a higher likelihood of conversion. For example, many websites now know where a user is located and may automatically translate the website into the common language of that location, correct time zone and local currency. Additionally, if they know I'm a first-time user on the site, they might provide more explanation of their product or service verses. If they know I'm a repeat visitor, they might provide images to products I've previously purchased.

  1. AI-enabled avatars, robots and greeters -- Some brands are understanding the value of incorporating robots into their company. Hilton Hotels, for example, uses a robot concierge named Connie to make guests' experiences as personal and enjoyable as possible. The two-foot-tall robot stands in the lobby to greet guests and answer questions. By knowing which guests are coming into the hotel, these robots can learn over time to provide more specific greetings and services based on that user's personal preferences.
  2. Personalized AI-powered chatbots -- The first step for successful personalization is to gather comprehensive and accurate data on your customer or user. Unfortunately, traditional website forms aren't always up to the task. Visitors might want to share data apart from the specified fields, may question why certain fields are required or may struggle to find all information necessary to fill out the forms correctly. Personalized AI chatbots have the ability to gain deeper insights from users.
  3. Better customer sentiment analysis -- Marketers must pay special attention to the smaller "micro" elements of content to garner customers' reactions. In all this content clutter, this is essential for marketers looking to stand out. AI can help better identify the true sentiment of users rather than generalizing between different customer interactions. By learning individual users' personality quirks, the systems can observe and know when individual users are satisfied rather than trying to guess based on generic learned behavioral traits.

Limitations and frustrations of personalization

One of the big challenges of using AI for hyperpersonalization is the cost and complexity of setting up AI-based personalization systems. The need for data, compute power and complicated systems can entail significant costs, given the complexity of engineering that goes into building these systems. Additionally, these smart systems require investment in data, tools and content. In order to streamline and scale personalization efforts, companies need to switch from a manual personalization strategy to one powered by AI.

Additionally, as companies begin to make messaging and offerings more personal, they need to watch out for user pushback against personalization features. This illustrates the "uncanny valley" of data. The uncanny valley is the concept that as robots appear more humanlike, they become more appealing, but only up to a certain point when the object becomes too human like that feeling of unease and uncanny feelings occur.

So, too, can this uncanny valley occur with data. When companies and marketers are applying hyperpersonalization to customers they need to constantly be monitoring customer sentiment to make sure they provide just enough information to be beneficial without providing too much information or personalization to start to become creepy or make the customer feel uneasy using their product or service.

The promises of hyperpersonalization are significant, and the lure of making products and services better and more targeted to their ideal customer base makes the allure of AI-based personalization an inevitability.

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