Modernizing customer experience KPIs with generative AI

In the race to redefine excellence in customer service, businesses are shifting gears, discarding outdated key performance indicators (KPIs), and embracing a dynamic approach that harnesses the potential of artificial intelligence (AI).

While KPIs are essential for businesses that want to elevate their customer experience, providing insights into customer satisfaction, loyalty, and engagement, the way to  measure them is changing. Generative AI is playing a major role in this change.

In this article, we’ll dive into how generative AI is impacting customer experience KPIs and how it can help organizations meet these KPIs.

Understanding customer experience KPIs.

Measuring and enhancing business performance is crucial to success. Customer experience metrics offer a comprehensive view of customer interactions, providing important data on satisfaction levels, loyalty, and overall engagement.

But what exactly are these KPIs? They can range from Net Promoter Score (NPS), which measures customer loyalty, to customer satisfaction  (CSAT), which measures customer satisfaction, or customer effort score (CES), which assesses the ease of interaction with a company. Tracking these metrics gives businesses an in-depth understanding of their customer experience and helps them identify areas for improvement.

Enter generative AI. This revolutionary technology is transforming the way businesses approach these KPIs. It streamlines support by automating responses, ensuring customers receive instant assistance whenever needed. It also provides valuable insights, analyzing vast amounts of data and helping businesses tailor their strategies to meet their customers’ needs better.

Top 10 customer experience KPIs.

1. Net Promoter Score (NPS). 

NPS is a critical metric that measures customer loyalty and a great KPI for evaluating customer satisfaction. It measures the likelihood of customers recommending a company’s products or services to others. Generative AI helps enhance NPS by analyzing customer feedback across various channels, including social media, emails, and chatbots. It extracts key sentiments and patterns, providing actionable insights that help businesses improve their operations and customer experience, boosting their NPS.

2. Customer satisfaction (CSAT). 

CSAT is a direct customer experience measurement for satisfaction with a company’s products or services. For example, generative AI-powered chatbots provide instant support around the clock, resolving issues and answering queries promptly significantly improving overall customer satisfaction, thus positively impacting CSAT scores.

3. Customer effort score (CES). 

CES measures the ease with which customers can interact with a company—from navigating its website to resolving issues. Generative AI can predict and address customer needs promptly, reducing customer effort. For example, chatbots powered by generative AI can help resolve issues faster as customers don’t have to wait on the phone for the next available agent or for an answer to their email. This creates a smoother and more enjoyable customer journey.

4. Average handle time (AHT). 

AHT is the average time taken to resolve a customer issue. It’s a crucial metric for customer service efficiency. Generative AI can automate routine tasks, such as automatic summary, and assist in problem-solving to enable faster issue resolution. Reducing AHT allows businesses to handle more customer interactions, improving customer satisfaction.

5. First contact resolution (FCR). 

First contact resolution measures the ability of a company to resolve customer issues during the first interaction. Generative AI provides customer service agents with insightful data to address queries faster and accurately, as well as suggesting next best actions according to the interaction context. High FCR rates improve customer satisfaction and reduce service costs.

6. Customer acquisition rate. 

This KPI measures the rate at which a company gains new customers. Generative AI enhances lead targeting and personalizes interactions, making marketing efforts more effective and increasing conversion rates. This leads to an improved customer acquisition rate and thus fuels business growth.

7. Customer churn rate. 

This metric measures the rate at which customers stop doing business with a company. Generative AI analyzes customer behavior data to identify at-risk customers. For example, analyzing customer feedback across various channels and providing insights into the areas where service needs to be improved. Operations teams can then take action to retain these customers, reducing churn and maintaining revenue.