The Gist
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AI is here to stay. Generative AI’s growing intelligence can greatly improve all facets of customer experience, including NPS, CES and CSAT.
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LLMs help you understand your customers. A company’s NPS can be boosted by using large language models to understand customer data.
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Chatbots ease customer interactions. Omnichannel chatbots improve CES and CSAT by providing around the clock support.
It’s no secret that companies would be nothing without their consumers, but fostering trust through stellar customer service is equally important when trying to make a positive brand impression.
Using generative AI to boost customer experience metrics allows customer service teams to better understand how customers think and behave — which they can then utilize to provide an overall better customer experience.
Although there are a plethora of customer experience metrics to rely on, we’re going to examine the potential impact of generative AI on Net Promoter Score (NPS), Customer Effort Score (CES) and Customer Satisfaction Score (CSAT).
Generative AI certainly has an expected impact on customer support and service. According to the International Labour Organization, these customer service tasks are subject to automation and generative AI:
- Issuing tickets for attendance at sporting and cultural events
- Taking reservations, greeting guests and assisting in taking orders
- Determining the most appropriate route for service delivery
- Making and confirming reservations for travel, tours and accommodation
Generative AI undoubtedly serves as a cornerstone for CX. To further explore its advantages, let’s look at three primary customer experience metrics — NPS, CES and CSAT — and shed light on how AI enhances each one.
What Is Net Promoter Score?
But first, let’s level-set on what these customer experience metrics are and mean to overall customer experience efforts.
The Net Promoter Score customer experience metric is used to measure customer loyalty and satisfaction, providing insight into how likely customers are to recommend your product, service or company. Usually, customers are asked something like: “On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?”
“Promoters” are those within the nine to 10 range, “passives” answer between seven and eight and “detractors” answer with a six or lower. To calculate the final score, you subtract the percentage of detractors from the percentage of promoters. Final NPS score: percent of promoters minus percent of detractors.
The best score would be 100, and the worst score would be -100. Ideally, this metric is combined with others for a more comprehensive view of the customer’s experience.
What Is Customer Effort Score?
The Customer Effort Score customer experience metric measures how easy it is to navigate/interact with your company. It shows how much effort a customer has to exert to resolve a problem, purchase a product or service, get a question answered, and so on.
It’s typically measured in various ways, the most common including the Likert scale (“strongly agree” to “strongly disagree”), a numbered scale (usually from one to 10) or an emoticon scale (a sad face indicating dissatisfaction, a happy face indicating the opposite).
This customer experience metric is important because if customers find it challenging to interact with your brand, they might switch to a competitor. But if your company makes interactions seamless, consumers will be more likely to remain loyal and recommend you to others.
What Is Customer Satisfaction Score?
The Customer Satisfaction Score customer experience metric is straightforward. It measures a customer’s satisfaction with a particular product, service or experience. Similar to NPS, consumers are asked a feedback question like: “How satisfied were you with your [product/service/experience]?” with answers usually falling on a scale (1-3, 1-5 or 1-10).
CSAT = positive responses ÷ total responses, x 100
This customer experience metric is useful because it reveals how well a product or service meets or exceeds customer expectations. However, like any other metric, it should be supplemented by others like NPS and CES.
Now that we’ve covered the basics, we can explore how generative AI can enhance all three of these customer experience metrics.
Boosting NPS with Large Language Models
Stefan Osthaus, founder of The Customer Institute, believes AI is the way of the future when it comes to analyzing NPS feedback using machine learning metrics. Asking customers questions on a numerical scale — or providing a drop-down menu with pre-set options to choose from — is easy to interpret and compartmentalize, but the moment the “why?” question comes into play is when complications arise. This is where AI steps in.
“AI is a very important tool to ease the handling of open questions,” Osthaus noted. “In the past, we’ve had software that creates word clouds. It’s very impressive visually, but very useless content-wise because you now have a group of words that pretend to give you information.”
Word clouds pick out words or phrases that appear to be related, then organizationally cluster them together. This removes particularity from customer feedback, reducing their responses to the “good” or “bad” words they used to describe their experiences. If you truly wanted to analyze open-ended feedback, you’d have to read through every response. Undoubtedly, this can become unwieldy if there’s an overwhelming amount of feedback.
However, Large Language Models can support customer experience metrics like NPS.
“With Large Language Models (LLMs), you can download a spreadsheet with thousands of open-ended responses, upload them into your LLM, and have a discussion with the AI,” Osthaus said. “You can ask: What does the data tell me? Where are the most prominent pain points? What does a customer say about product ABC or service 123? LLMS provide a whole new dimension of data insight.”
Related Article: Combine NPS and CLV For Truth Growth
Chatbots Can Yield a Higher CES
Brad Cleveland, CX consultant and CMSWire Contributor, believes AI makes things easier for the customer. Conversational AI and omnichannel chatbots are used by many brands to help improve the Customer Effort Score customer experience metric because they allow customers to access support when they need it. And there are many uses cases for these AI chatbots.
Amongst other things, these chatbots can readily access a customer’s purchase history or previous support tickets, thus streamlining the interaction further. A customer’s “ease” interacting with a brand would be significantly hindered by a contact center agent manually pulling up this information and analyzing it over the phone.
“AI directly improves CES by helping you better anticipate customer needs and deliver more timely and tailored solutions.” Cleveland said. “You’re essentially improving both ends of the customer experience equation (what they need and what you are providing). So, harness AI well, and you’ll move customer experience in the right direction and see CES (as well as CSAT, NPS, et al.) trends that make you smile.”
Additionally, chatbots have the ability to provide around-the-clock support for customers who need help after business hours. Being able to resolve an issue from home, at any time, is far more appealing than waiting until the next morning to speak to an agent or other support staff. Improving the Mean To Time Resolution (MTTR) will consequently increase CES.
Boost CSAT Through Customer Feedback Analysis
Similarly, generative AI can help boost the Customer Satisfaction Score customer experience metrics by analyzing customer support tickets in a way human operators never could. Customer feedback analysis is greatly improved because AI has the ability to compartmentalize vast amounts of customer responses from surveys, reviews and other sources to identify common issues or pain points.
According to Sprinklr, these insights allow customer experience leaders to uncover specific details about customer feedback that may have otherwise gone unnoticed. Before AI implementation, it was difficult to see beyond a broad overview of how your customer support team was performing. Now, CX leaders can identify any common issues that may be lowering your satisfaction scores that weren’t initially apparent.
Support tickets can be tagged and sorted based on a customer’s reason for calling or contacting your company with an in-depth record of their feedback.
Micah Solomon, CX consultant, author, and CMSWire contributor, believes AI can help mitigate calls between agent and customer. For example, customers will be more satisfied with their experience if omnichannel chatbots are efficient, accurate and available at any time. This way, human agents can manage more complex issues over the phone instead of taking every single call coming through.
“You should be investing in technology to have you be prepared for bottlenecks. An AI bot and/or AI-informed search engine to handle an inquiry overload (or prevent it in the first place),” Solomon said. “In addition, sophisticated AI real-time-training tools are now available that can turn a generalist agent into a ‘temporary specialist’ on the fly when all your actual specialists are otherwise engaged.”
He also recommends using AI to get an advance (or at least faster) notice of pending issues that may affect your team if you’re using technology that searches social media mentions round the clock. This, as a result, will boost your Customer Satisfaction Score because it will prevent future issues.
“In the physical world, an innovative use of technology to remove a bottleneck is the recent deployment of Amazon Go technology to pre-check fans’ IDs (for age verification) at Denver’s Coors Stadium and thus minimize the lines once those fans start lining up for a drink,” Solomon said. This is an example of using generative AI as a preventive measure to keep customers satisfied.
Conclusion: Generative AI Improves Customer Experience Metrics Overall
Using AI to make minute improvements within the customer experience will impact how consumers view your brand, even if the customer doesn’t point to them directly. A customer will be more likely to recommend your product or service if their experience is seamless and streamlined in subtle yet constant ways. Slashing the time they spend on hold, if only by a minute, is likely to impact the customer if other small aspects of their experience are also marginally improved.
The lament of, “if I had more time, I would’ve done this differently,” can be quickly phased out thanks to AI technology. Customer service leaders can use machine learning metrics and the benefits of tools like ChatGPT and others to not only increase NPS, CES and CSAT but improve their CX as a whole.
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