How AI is Improving Customer Experience

Artificial Intelligence is having a significant impact on public services, healthcare, environmental protection and even space exploration. It’s a technology that is changing the world, but it’ also changing how businesses communicate and serve people in every day interactions.

And Customer experience is at the heart of this change.

With the adoption of new technologies comes a natural growth in competition – better quality products and services, online self-service options, and ever-improving production and delivery lead times (imagine ordering a product online 10 years ago and expecting it to arrive the same day, people would have thought you were crazy.)  

What happens when everyone has great products, great lead times and a competitive price? A hunt for new ways to set yourselves apart from competitors – an exceptional customer experience. 

The Importance of Customer Experience 

A great customer experience is absolutely essential to driving business growth. With it comes heightened loyalty, customer retention, and brand advocacy. 

Providing the highest level of experience for your customers allows them to get maximum value from engaging with your company, with minimum effort or friction during their experience.  

Pulling this off has tremendous benefits – just take a look at Apple. The cult following they’ve cultivated didn’t just derive from great products and great branding, it’s driven by the whole experience of purchasing and owning an Apple product. 

But how are companies actually creating that experience in the right way? 

So, let’s dive into exactly how companies are improving their customer experience with AI. 

How AI Is Being Used to Improve the Customer Experience 

To find out exactly where companies are investing their efforts in AI,  Gartner also investigated the most common uses of AI to improve CX. 

The top 12 areas for investment during 2021 were as follows: 

  1. Personalization 
  2. Customer Journey Analysis 
  3. Intelligent Contact Routing 
  4. Virtual Customer Assistants 
  5. Speech or Text Analytics of Sentiments 
  6. Predicting Customer Churn and Retention 
  7. Content Image Categorization 
  8. Chatbot Optimizations 
  9. User Interface (UI) And User Experience (UX) Optimization 
  10. Predicting Customer Lifetime Value 
  11. Customer Service Agent Coaching 
  12. Agent Workforce Scheduling 

Top AI investments to Improve CX

Source: Gartner®, Quick Answer: What Are the Most Common Uses of AI to Improve CX?, 23 June 2022.

Interestingly, in our opinion half of those top investment areas focused specifically on developing the customer service department – either by directly helping the customer or helping the agent to help the customer. 

1. Personalization  

A personalized customer experience involves gaining an understanding of the customer’s tastes and preferences on an individual basis, rather than assigning customers to a “type” or “segment”. This is achieved by sending customers personalized communications, product recommendations, and offers. 

It’s not surprising that personalization was the top answer considering that 80% of customers expect personalized offers from retailers.  

So, how exactly is AI improving personalization?  

Machine learning (ML) models can be used to analyze browser history, past purchases, page clicks, duration on a page, and many more factors to gauge customer interests and preferences on an individual basis – in real time. 

This means that websites or apps can tailor their website content, offers, and recommendations based on each specific customer, creating an individualized customer experience.  

Netflix’s recommendation engine is a great example of this. It not only predicts what users will want to watch next, it also predicts which images will best engage the user as they scroll through the website or app. 

But that’s not the half of it…

Netflix actually uses machine learning and an incredibly complex algorithm to suggest what content to make from scratchIt’s now machines that are acting as creative forces…The algorithm has been known to suggest which actors should star in a certain TV show as well as the genre and pacing of the action.

2. Customer Journey Analysis 

To analyze a customer journey, the journey must first be outlined. This is achieved by tracking and mapping all user interactions with a business across multiple channels. That includes human interactions like call centers and automated interactions like website visits and chatbot conversations. 

All of these interactions are then mapped and analyzed giving insight into areas that are performing well (generating leads, sales, and conversions) and areas that require improvement (where users are exiting the journey). 

With the help of AI and ML models, masses of data can be analyzed in real-time. The models can identify key moments in which customers choose to make a purchase, engage with a service, or leave a provider. This provides valuable insight that allows organizations to steer away from negative paths – ultimately improving CX. 

3. Intelligent Contact Routing 

Intelligent contact routing is the process of distributing users to the correct point of contact based on their needs and requirements. Traditionally, a user would have to route themselves over the phone by selecting the correct department from a list recited to them. 

“For sales select 1, for customer service select 2…” 

With the help of AI and ML models, systems are able to automatically understand a user’s intent by analyzing voice or text channels to route the user through to the best-fit service agent. 

Not only does this have a direct impact on customer outcome metrics such as higher customer satisfaction or Net Promoter Scores. But, the systems are collecting valuable intelligence that can be used to improve the user experience and produce valuable insights for key stakeholders. 

For example, an excessive amount of complaints about a certain topic, product, or service can be flagged in real time by the system. 

4. Virtual Customer Assistants 

Virtual customer assistants, whether online or over the phone are essentially virtual avatars of a company. They act as customer service agents to help customers self-solve common queries faster, relieving pressure on contact centers.  

AI solutions allow for faster and more efficient development, particularly across multiple languages for global enterprises. This is augmenting the customer experience in exceptional ways, meeting the demands for a completely virtual experience that provides the highest level of service. 

5. Speech or Text Analytics of Sentiments 

Sentiment analytics is the process of analyzing voice or text data to determine the opinion or feelings of the user in regard to the specific topic of conversation.  

A combination of Natural Language Processing (NLP) and ML techniques makes it possible to analyze masses of text and speech conversations to ascertain the general consensus on a topic.  

This allows enterprises to gauge public opinion on specific topics such as products, services, brand reputation, or customer experiences – making it possible to adapt in an effective and timely manner to improve customer satisfaction. 

6. Predicting Customer Churn and Retention 

Being able to predict which customers are likely to leave a service or subscription is the business equivalent of having a second life. 

AI and ML models use data on customer behavior to evaluate the likelihood that each user will leave or remain with the business. This information can then be used to target users at risk of leaving with personalized marketing to reengage them. 

This proactive technique can help recapture customers before they are even actively considering leaving – reducing churn, increasing customer loyalty, and improving retention. Of course, this also enhances the experience of those customers who are receiving more personalized and targeted marketing that matches their needs.  

7. Content Image Categorization 

Content image categorization is the process of categorizing images based on their characteristics. Characteristics such as color, type of animal, shapes, items of clothing, type of car, etc. are identified and labels are assigned to the applicable group of pixels. 

AI and ML models are able to analyze masses of images in almost real-time and reverse engineer an image input into the system to find images with similar categorizations.   

This has made image-based search possible which gives a user more freedom with their search. For example, a user may like a piece of clothing they see in a picture but not quite be able to describe all of the detail.  

Big brands such as Google, Amazon, Forever21, and eBay are already adopting image-based search. 

Content image categorization also significantly improves the results of customer search queries. Instead of relying on manually inputting ‘tags’ by a human operator to match up with a user’s query, image analytics will identify all the pictures that meet the query.  

This both improves results through fewer human errors and lowers processing time when adding new products to a website or app. 

8. Chatbot Optimizations 

Optimizing existing chatbot solutions is another key investment companies are making. This is no surprise considering the rate of progression AI and ML technologies are experiencing coupled with the increasing importance of automated customer service.  

Chatbots have a varying degree of effectiveness, from answering very basic questions to more dynamic, topic-switching solutions that provide a more natural conversational experience. And AI is playing a central role in that shift to more dynamic conversations. 

AI solutions provide proactive optimization by constantly monitoring the performance of chatbots. This means they’re able to flag issues to developers in real time, highlight common queries that are not being resolved, and generate previously inaccessible customer insights.   

Teneo offers exactly that – with automated optimization suggestions built directly into the Teneo Studio interface and integrations to your reporting tools.  

Get in touch to learn more about building and optimizing your conversational AI solutions with Teneo.   

9. User Interface (UI) And User Experience (UX) Optimization 

UI and UX optimization are all about improving elements of a website, app, or software that users use to navigate or ascertain information. 

Improving UI and UX may lead to users spending more time on the interface, resulting in more sales, leads, or conversions. 

Right now, there aren’t AI systems that outright design web pages (yet). However, AI is being used to assist designers and developers in almost every area of their work.  

For example, AI can be used to analyze masses of customer data that designers can use to further understand their needs – creating a more fulfilling customer experience. This is particularly relevant for improving digital accessibility.  

10. Predicting Customer Lifetime Value 

Customer lifetime value (CLV) is the total income a business generates from a single customer over the course of the customer’s lifetime with the business. Traditionally, this would be calculated by taking the average revenue per customer based on past transactions. Resulting in one singular value for CLV. 

With AI/ML models, it’s possible to analyze previous data to predict the CLV in a more accurate way – allowing companies to use early data from new customers to predict who will have a higher CLV and therefore, be more valuable to the business.  

This allows strategic focus of marketing and sales efforts to retain those higher-value customers, providing better customer service and improving the customer experience in a targeted way. 

11. Customer Service Agent Coaching 

The training of customer service agents is essential for providing great customer service and experience. For a customer, there is nothing more frustrating than being met with an operator who can’t actually help you after being on hold. 

AI solutions driven by NLP and Natural Language Understanding (NLU) have been developed to improve customer service agents’ confidence, reduce stress, and aid them with complex customer problems. 

This is possible by simulating conversations with training operators before they get on calls with live users.  

Having the ability to run through simulations of difficult and frequent call types based on real-life past data ensures the operator is ready for whatever real callers throw at them.  

Agents being well-equipped to solve users’ queries and issues is vital for a smooth customer experience. 

12. Agent Workforce Scheduling 

Agent workforce scheduling is the process of planning when and for how long each agent will work based on the needs and requirements of a call center.  

By harnessing the power of AI and ML models to analyze historical data, it’s possible to improve forecasting of high and low-volume periods and in turn, schedule more efficiently.  

This can have a vastly positive impact on operating costs by reducing over-staffing during low times and improving customer service with enough agents during busier periods.  

AI systems also have the ability to schedule based on labor requirements and personal needs of agents such as holidays, fixed hours, shift rules, and additional breaks. This more efficient scheduling based on agents’ requirements leads to a better and happier workforce, which directly translates into better customer service and experience. 

AI is at the Forefront of Exceptional Customer Experience 

Gartner’s survey made one thing abundantly clear. AI is absolutely central to improving the experience of customers – whether that involves investments that support customers directly or that help agents help their customers.  

Customer service is at the forefront of that investment, but AI is making its way into other departments such as sales and marketing – supporting every aspect of the customer journey. But for now, it appears that organizations are primarily focusing on AI in customer service. 

The trend of customer interactions becoming largely more virtual – driven by the pandemic – has clearly accelerated those investments into the customer experience and no doubt this is set to continue in the coming years.  

Gartner®, Quick Answer: What Are the Most Common Uses of AI to Improve CX?, 23 June 2022.

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