AI-powered hyper-personalization can deliver value to customers by crunching data at enormous capacity and anticipating the customer’s journey with precision.

The integration of artificial intelligence (AI) into customer experience (CX) strategies is transforming the way businesses interact with consumers. Customer expectations have never been higher. Let’s see how Hyper-Personalization helps in meeting those expectations.

It Is Time to Take Things Personally!

Businesses are expected to anticipate the customer’s intention and excel at it. We live in an era where the customers want us to predict what their needs are.  Competitions are becoming more advanced. A tailored personalized experience can improve customer satisfaction, increase revenue, and boost brand loyalty.

80% of customers are more likely to purchase from a brand that offers a tailored customer experience. Hyper personalization story will vary by sector. For someone in the financial sector, it may mean personalization of financial decisions. For retail, it means personalizing the shopping experience, recommendations, and curation of experience based on order history. For the healthcare industry, it can mean improving personalized care plans.

Although the experience varies for each industry, here are some examples of hyper-personalization that can be used to increase brand loyalty, customer experience, and increase revenue.

Recommendation Engine

This is one of the most effective applications of hyper-personalization and offers the best ROI. Customers love recommendations! Having an AI-driven recommendation engine that is personalized based on the customer’s activity on the website, social media, order history, search history, and any external source can boost your ability to engage the customer and drive satisfaction.

Targeted Customer Ads

Analytics can process large amounts of data from different sources and predict outcomes, create individualized plans, and suggest targeted ads for a customer. This results in improving brand loyalty and the customer feels valued. It can ultimately result in a better Click Through Rate.

Personalized Landing Pages

Having a personalized page improves customer satisfaction and makes the customer loyal to the brand. Imagine the landing page displaying content related to your personal goals. To calculate your personal goals, a data analytics engine works through a million parameters determines your most probabilistic next goal, and breaks it down into next steps. Your digital marketing team can then personalize the landing pages by creating relevant content.

Individualized Pricing and Product Configurations

Imagine curating a personalized bundle with special pricing for a customer who is shopping for a specific goal. For example, back-to-school shoppers are looking for bag packs, water bottles, etc. based on age, location, and weather conditions.

Using hyper-personalization, the customer can be offered a curated list of back-to-school items, including upsell items and a promotional value bundle. This is a win-win situation for the brand and the customer.

Personalized Customer Service

Depending on the customer, different customer service options can be provided. Some like talking on the phone, some like to chat and some like to email. Using AI, a personalized customer service agent can be created for the preferred language, location, and type of contact. AI Assistants can quickly learn from your past conversations, order history, and the journey on the website.

It can then offer a unique and individualized customer experience. The goal is to solve the customer’s problem on the first contact and if required a human should intervene.

Delivery of Notification

Every customer is different. Depending on the individual, a tailored notification plan can be developed. In-app notifications work well for some, some like text messages. Depending on the time of the notification, some like email versus in-app notifications. Hyper personalized delivery plan for an individual customer ensures that the message is communicated effectively and on the preferred channel, tactically based on the time of the day, week, etc.

Challenges

One of the challenges faced by many companies is to not have a vision or not knowing where to start. Also, there are some unique challenges when it comes to using hyper-personalization to deliver a tailored experience. First, at the core of hyper-personalization is data.

There are challenges in collecting data from all the sources. You don’t want to miss data about a change of mind during the customer journey. The next challenge is the turnaround time in getting the data ingested for processing.

You would want things to be in real-time, but the reality is that it takes a lot of cost and engineering efforts to make the data available for processing in real-time. The ROI is not effective so many brands trade off with near real-time updates. But you must do it before your competitor does it!

Some other challenges are the quality of data, the ability to fine-tune the output, and training the models to embed the voice of your brand in it. Making sure that the hyper-personalized experience is right for the customer is another challenge. Informing customers about the use of data and consent from customers is equally important.

How to Overcome Common Challenges?

Creating a roadmap for hyper-personalization will likely be the first step. Overcoming the challenges is also an opportunity to get to know your customers. You must understand that there are customers who are going to produce data that will suddenly signal a change of mind, lose interest without any reason, or not open your notifications for days.

These events are input to your LLMs and these help to fine-tune individualized experiences.

That is why it is best to start small, learn, improve, test, and fine-tune the hyper-personalized experience for a small set of customers. Once you have the experience tailored to a certain group of customers, you should then plan to expand the range of customers who benefit from hyper-personalization.

Also, it is ideal to roll out the features in phases and get the customer acquainted with the brand’s new initiatives. You would want to gradually offer new experiences like recommendations, personalized chatbots, ads, etc. If you don’t have the data to train your model, you must wait until you have enough confidence. Things can go wrong if you don’t have the right testing process for new personalized features.

It is important to understand that at the core of these experiences is a digital experience platform (like Sitecore) that composes the experience for the customer.