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Almost universally, modern customers have come to expect the same thing: personalization. It’s no longer enough to take a one-size-fits-all approach. That’s why businesses of all sizes are trying to create tailored customer experiences, and, increasingly, you’ll struggle to find a major brand that doesn’t offer some form of personalization.    

With personalization becoming the norm, it’s becoming harder to deliver experiences that stand out from the rest. Enter Artificial intelligence (AI), the trick to delivering truly personalized customer experiences.

What is AI-driven personalization in CX?

AI-driven personalization

                             Image sourced from segment.com

 

AI enhances the traditional methods of personalization to create even more tailored experiences. At the heart of this approach is machine learning (ML) technology. Using ML, AI algorithms attempt to replicate human thought patterns.

This might sound terrifying, but in reality, it means AI can help users in more intelligent ways. It means that it can learn more from previous experiences to deliver better outcomes. In short, the more it learns about customers, the more it can deliver what they need.  

As a consumer, AI-driven personalization is hard to escape. Go to your favorite streaming platform, and you’ll find a list of recommended movies. When you visit an online store and view a product, you’ll probably find ads for that product everywhere you go. Some e-commerce stores even alter their layout based on your previous habits.   

It’s safe to say that the phenomenon of AI isn’t going away anytime soon. In fact, 9 in 10 companies use AI personalization to drive growth. 

Key AI tools for CX personalization

Let’s look at some ways brands use AI to power personalization in more detail.

 

AI-Enabled product recommendations

 

amazon products screen

                     Screenshot sourced from Amazon

 

AI draws data from two sources to power product recommendations. Data specific to the customer and data from other, general customers.

Using customer-specific data, AI will look at your previous interactions with a brand to help determine what you’ll buy next. This means looking at some of the following factors: 

 

  • The items that you have viewed previously. 
  • Your search queries. 
  • Products that you have previously purchased. 

When looking at general customer data, AI can use its powerful analytical ability to analyze customer groups. Sometimes, a purchased product will have obvious complimentary items (such as a tennis racket and tennis ball). AI identifies these as a suggested purchase. 

AI can also identify less obvious correlations. It will examine a customer’s current and past purchases and identify similar purchases made by other customers. For example, let’s say AI finds out that many people who buy vitamin supplements also invest in car magazines. It can use this insight to power a recommendation.  

AI can even attempt this when no purchase has been made, simply by looking at purchases by customers in a similar demographic. This level of ‘intelligent’ recommendation gives a much greater chance of a customer making an additional purchase.

 

Natural language processing (NLP) and sentiment analysis

NLP chatgpt

                    Free to use image sourced from Unsplash

 

Natural language processing (NLP) is a powerful technology often used by customer support.  

AI can analyze language through speech or text using NLP and respond intelligently. 

One of the best examples of the technology in action is modern chatbots. Not long ago, many customers would roll their eyes when being met by a chatbot. Far from a personalized experience, they’d struggle to get any useful response from a bot. That’s because chatbots respond to pre-defined queries and phrases. 

But, a modern NLP-powered chatbot solution can respond to customer queries in real time. AI can look at previous customer queries and how they were resolved. Then, it can use this information to help guide its response. 

Even if a chatbot can’t help on one occasion, it can forward a customer to a human agent. The bot can collect information from an agent who resolved an issue – if the same problem comes up again, it will know how to respond. 

Of course, NLP has many applications beyond chatbots. Another example of how NLP helps support personalization is through sentiment analysis. AI can analyze previous customer interactions and identify how people feel about your brand. 

Simply by analyzing text or speech, sentiment analysis can tell you whether customers have a positive, negative, neutral, or mixed view of your brand. 

Customer segmentation tools

 

Customer segmentation tools

                  Free to use image sourced from Unsplash

 

Customer segmentation is the process of breaking customers down into smaller groups. It’s a surefire way of creating more personalized experiences.

For example, let’s imagine you identify that a product is popular with customers with certain interests. You can create a segment of customers based on that interest. This can be applied to your marketing, helping to ensure your ads reach the right people.  

Normally, building segments takes time and lengthy research. It involves hours of analyzing and comparing datasets. And of course, there is scope for human error. A simple mistake can lead to targeting the wrong people.

The introduction of AI has made the segmentation process a lot easier. Thanks to machine learning, AI can look at enormous datasets in seconds and identify commonalities. Using historical and real-time data, can create smarter, more accurate segments. This enables you to pinpoint customers in more targeted ways than ever. 

A modern customer segmentation tool can be easily integrated with many different tools. This means you can apply AI-generated segments across many advertising channels. 

 

How AI personalization enhances customer satisfaction

Simply put, customers like AI because it makes their lives easier. Each of the AI tools listed above helps customers in different ways. 

AI-powered product recommendations help customers save time. Shoppers don’t want to sift through pages and pages of products. They want to find what they’re looking for quickly so that they can move on to other things. 

NLP provides similar advantages. Have you ever been stuck in a customer support queue, waiting hours to see an agent? AI-powered chatbots can see you instantly. If they have the right data, they can resolve your issue without any human involvement.

Even segmentation can boost satisfaction. Customers don’t enjoy being bombarded with irrelevant marketing. This is especially true when an advert interrupts a piece of content that they are enjoying. Segmentation helps to make sure that ads are relevant. It helps shoppers find new and interesting products. 

Your offers to provide smoother experiences to customers won’t go unnoticed. They’ll come to view your brand as a reliable and helpful source. 

That said, as with any new strategy, you should also monitor the impact your AI personalization has had on your customer experience, feedback, and overall sales. 

To do so, include ERP implementation within your new AI CX project plan. This will ensure that you’re collecting the data you need from the start so you can analyze how AI personalization impacts customer satisfaction, employee workload and resources, and sales figures. 

 

Future Trends in AI and customer experience

The AI revolution isn’t stopping anytime soon. If anything, it’s only likely to accelerate in the coming years. Here are some of the top trends to look out for. 

Emerging technologies to look out for

 

emerging technologies

              Free to use image sourced from Unsplash

 

So, what technologies will shape the customer experiences of tomorrow? Below are some of the top examples. 

  • Generative AI – We’ve mentioned how NLP is transforming chatbots. Generative AI is the next stage. Tools such as ChatGPT can not only look at historical data but gather information from across the internet. This means that it is capable of generating detailed responses to queries on the fly. 
  • Real-time assistance – AI tools are now capable of providing customer support agents with real-time insights. This data can help agents solve customer problems. It can suggest solutions, and provide ongoing sentiment analysis, helping agents to choose the right tone. 
  • Cookieless personalization – Globally, emerging data laws are making personalization more difficult. Major browsers, including Google Chrome, have recently removed third-party cookies. In response, many brands have invested in cookieless personalization. This focuses on machine learning analyzing first-party data to project recommendations. 
  • More omnichannel experiences – Customers want seamless experiences when traveling between channels. The next stage of this is linking online and in-store experiences. One way this might be achieved is access to online reviews. A customer would scan a QR code in-store using your app and be linked to a review page. 

 

Leveraging ERP for an integrated data approach

Data is everything when it comes to AI personalization. We’ve already mentioned how ERP (Enterprise Resource Planning) implementation will help you analyze AI personalization’s impact on your business. Now, we’ll go into a little more detail about how ERP can give you an edge regarding the future of CX.

From customer support to HR, businesses collect a lot of information. As it’s difficult to stay on top of all this data, it’s likely that we’ll see more businesses invest in ERP software. This is because ERP connects all your business processes, guaranteeing that all key data is accessible from a single interface. 

The benefit of this centralized data point from an AI and customer experience perspective is that, firstly, it provides a single source of information, making it easier to track customer experiences and touchpoints. Second, it allows organizations to implement new technologies more easily, helping them stay up-to-date with the latest CX trends and, ultimately, provide a better level of service. 

 

Key takeaways

There’s no denying that the future is going to become more and more personalized. Throughout this article, we’ve explored many ways that AI can boost customer experiences. Let’s recap some of the key points. 

AI personalization uses machine learning algorithms to create more targeted experiences. Customers not only want but expect personalization, that’s why it should be a top priority. From improved product recommendations to smarter chatbots, AI improves many aspects of customer experiences. 

We’ll likely see the emergence of even more innovative AI in the coming years. So, why not be at the forefront by investing in the latest cutting-edge personalization techniques?  

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