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3 Uses of Artificial Intelligence (AI) In Customer Experience

December 9, 2019

AI is taking over the world of client experience.

By 2025, it is predicted that AI will power 95% of customer interactions. But what does this mean for the client experience industry? 

AI in customer experience

AI technology will likely influence the client journey. While we have already seen iterations of AI in client experiences at work, like automated chatbots and content personalization, but there is still more to come. Read on to learn about three ways that AI will change the future of client experience. 

1. Provide virtual reality as a service

Virtual reality service could be practical for a number of B2C industries, including e-commerce

One of the most frustrating user experience issues that customers face during online transactions is describing their issue over the phone or through a live chat with support. In most cases, customer service representatives must rely on the customer’s description of the issue to resolve it, which can lead to confusion on both ends of the phone or chat room.

Additionally, customers who are not technically savvy may have a hard time navigating a company’s website or contact hub at all. On the support side, it can be difficult for representatives to know if a customer has followed their instructions or carried out a task correctly.

One way to navigate this issue is to involve video in customer support issues, meaning customers submit recorded evidence in place of descriptions of an issue. For example, an Amazon customer trying to operate her newly-purchased Alexa speaker might record a video of her attempt to turn the device on. A representative could then watch the video and troubleshoot the user error or confirm that the device arrived inoperable. 

A more advanced resolution would be to implement virtual reality technology as a service. This would allow a representative to watch what a customer is trying to do in real-time and recommend solutions as if they were their in-person. This would also give customers a sense of human experience, which is commonly cited as one reason consumers love to shop at brick-and-mortar stores. 

Virtual reality would allow for all of the benefits and experiences customers seek in-store without a company eating the cost of a physical location for products. Facebook’s Oculus Rift and Microsoft’s HoloLens are just two such virtual reality technologies currently emerging, but there will no doubt be more to come.  

TIP: See just what more is coming in the VR and AR space with the latest trends for 2020! 

Learn more: AR/VR Trends →

2. Minimize customer abandonment rate and complaints

Cart abandonment is one of the biggest issues facing e-commerce businesses, with over 69% of customers on average leaving items in their carts without purchasing them. By applying artificial intelligence, some elements of the in-store purchase (which involve a trained sales rep) can be imitated online. 

Body language, for example, is something that can’t inform the consumer decision-making process online like it can in-person, when a sales rep can influence shopper behavior based on observation. However, AI can be trained to pick up on similar signals that point to virtual body language. As a result, recommendations can be made to push a consumer to the next stage of the purchasing process.

Similarly, a tool like this can be used for omnichannel promotional messaging and to retarget customers at different stages of the customer journey—all while increasing your company’s conversion rate and decreasing cart abandonment. 

Some other ways to decrease cart abandonment with AI include: 

  • Re-engaging past customers based on micro-moment behavior
  • Lead segmentation 
  • Service suggestions that pair with products

Where there’s AI, there’s usually a way. The limit of implementation in your company’s conversion and retention strategy is only limited by brainstorm bandwidth. To complement the data visualization and segmentation that comes with AI, also consider qualitative research, which can often inform the “why” behind the consumer decision-making process.

3. Fine-tune price models to reflect customer interaction levels

As many as 73% of retailers are planning to use intelligent automation to enhance their pricing and promotion by 2021, which means this optimization method is quickly becoming the norm.

Let’s face it: price optimization is not realistic at scale without the help of automation. A single inventory manager, for example, cannot keep up with the best pricing for thousands of items on a weekly or daily basis. Luckily, AI can comb through data and consider hundreds of pricing scenarios, which could make your company even more profitable at speed.

Additionally, price optimization can reveal untapped connections between purchased items that a human might not have the bandwidth to pick up on. For example. perhaps customers who purchase bed linens are also interested in laundry detergent, which makes a lot of practical sense. However, perhaps that same customer is also likely to purchase a bulk order of chicken noodle soup—now that’s something a human is less likely to pick up on. 

Eventually, you might find that that customer is interested in eating in bed, which makes sense once your company connects that customer’s geographic area (likely urban) to the purchase. And voila! An urban-millennial segment is born that you can sell more personalized recommendations to.

An AI for customer experience

In the very near future, industries using AI will change customer experience as we know it, but that is just the beginning. Both B2B and B2C businesses will implement it in ways we can only imagine— even beyond the AI platform advancements in this article.

Discover the Easiest-to-Use AI Platforms...

3 Uses of Artificial Intelligence (AI) In Customer Experience Artificial intelligence (AI) is set to revolutionize client experience over the course of the next few years. Learn about three specific ways it will in this article.
Emily Smith Emily is the Content Strategist at Remesh, where she spends most of her time spinning data in stories

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