How do you know if your customers will stick around? Hard truth: you don’t. Humans are incredibly emotional and intuition-led. However, as a group, our actions can be predicted through our social influences and feedback loops.

That’s great news for customer service teams.

Tell-tale signs of action rear their heads across customer service metrics like CSAT scores and Automated Resolution (AR). Naturally, these are the metrics customer service teams should be monitoring. But, very often teams get weighed down by indicative (not holistic) metrics like call handling times and ticket closure rates, which prioritize speed over solutions.

Thankfully, AI can now dig deep into your very best (and very worst) customer databases to surface gaps between what customers need and get. With machine learning, customer support teams will soon be able to predict what customers need, even before they know it themselves.

Balancing age-old strategies with new-age trends can be daunting. We’re here to help.

In this guide, we’ll discuss traditional versus AI-led customer service metrics and why AR is the customer service metric of the future. As an added bonus, we’ll share a customer metrics toolkit on how to integrate AI metrics with traditional customer service KPIs. You’re welcome.

Traditional customer service metrics

The traditional customer service performance metrics are CSAT, NPS, and CES. Let’s wade through the alphabet soup, and explore these metrics’ pros, cons, and implementation methods in detail.

The pitfalls of traditional customer service metrics

While CSAT, NPS, and CES are good measures of tracking customer service, they can’t capture the depth of human-human interactions. They suffer from:

1. Narrow perspectives: These scores often lack a comprehensive customer experience view, and instead focus on specific interactions or aspects.
   
2. Reactivity: They’re predominantly reactive, analyzing customer experiences after they’ve happened and delaying potential improvements. 
   
3. Limited forecasting: Traditional measures lack predictive capabilities for forecasting future customer behaviors or trends.
   
4. Bias and misinterpretation risks: Most traditional customer support metrics stem from survey responses. But unless you craft and order questions thoughtfully, you may collect biased or inaccurate data.
   
5. Emotional engagement gap: Emotional engagement, a critical component of customer loyalty and satisfaction, is challenging to quantify through standard surveys.
   
6. Real-Time Feedback Challenges: Real-time analysis — crucial for quickly addressing customer issues — is impossible to attain through conventional metrics. 
   
7. Adaptability Issues: Adapting traditional metrics to new channels, platforms, and customer expectations requires continuous refinement, which is resource-intensive.

The emergence of AI-first metrics

AI-first metrics do more than counteract the pitfalls of traditional customer service metrics; they open up a world of possibilities for companies to innovate and optimize their customer service. They aren’t just a resource-saving technique, but a reflection of customer expectations in 2024 and beyond.

AI-first metrics offer previously unattainable use cases such as:

1. Comprehensive customer profiling: AI-first metrics allow a deeper understanding of the customer journey. By collecting and analyzing data from touchpoints across high-intent calls, onboarding, usage, and offboarding, AI holistically evaluates customer experience, uncovering insights into customer behaviors, preferences, and pain points.
   
2. Enhanced personalization: AI technologies can sift through vast amounts of data to identify individual customer preferences and trends.
   
3. Predictive analytics: Beyond analyzing current trends, AI-first metrics employ predictive analytics to forecast future customer behaviors. 

Commonly used AI-first metrics in customer service

Some commonly used generative AI metrics across the customer service industry include:

1. Automated Resolution Rate (ARR%): Measures the percentage of customer service requests resolved without the involvement of your customer support team, indicating the effectiveness of AI agents or virtual assistants.
   
2. Predictive Customer Satisfaction (PCS): Uses AI to predict customer satisfaction based on interaction data, potentially identifying issues before they escalate.
   
3. Sentiment Analysis Scores: Employs natural language processing (NLP) to gauge the sentiment behind customer feedback, providing insights into their emotions and attitudes.
   
4. Containment Rate: Assesses the number of interactions handled by AI agents compared to those requiring live customer service agents.
   
5. Behavioral Prediction Scores: AI analyzes customer behavior patterns to predict future actions, such as how likely a customer is to of re-purchase or churn.

While indicative of AI integration, these customer service metrics don’t offer insights for optimizing customer experience. In the end, all a CX leader needs to know is “How effective is our team in understanding and resolving customer needs?"

Using CSAT with NPS and CES to improve customer satisfaction

How can businesses analyze their customer satisfaction score alongside other data to win customers over repeatedly and reliably?

Start by looking at CSAT as just one piece of the customer service puzzle.

While other measures of customer satisfaction, like NPS and CES scores tend to follow the same structure, there’s no single benchmark for a CSAT. A good rule of thumb is to measure your customer support KPIs (key performance indicators) regularly and compare your results over time. All three scores should be improving. If the scores are already strong, then they should be staying there. This indicates top-quality customer service operations.

If your KPIs drop, quickly identify the problem. Compare success metrics across channels, products, and services to understand what’s working and what’s not, and adjust your strategy accordingly.

Your goal here is continuous improvement and a focus on high customer service quality.

Together, these three metrics help you take the temperature of your overall customer service experience and continually improve your customer relationships.

An easy way to boost all your customer service KPIs is to implement personalized, self-service on your customer service channels. AI agents, for example, make it easy for customers to find information or resolve issues quickly, with minimal effort, in a single, seamless interaction. AI agents also personalize interactions at scale, driving customer satisfaction and loyalty while helping you lower customer service costs. Plus, with an AI chatbot, you can easily add the customer surveys you need to collect feedback, measure these customer service KPIs, and continually improve your customer experience.

Automated Resolutions: The North Star metric

Integrating AI-first metrics with traditional customer service KPIs like a CSAT score is the bridge between reactive and proactive customer service. Together, these metrics analyze data in real-time to help businesses predict customer needs.

For instance, a high CSAT score might indicate satisfaction, but when combined with AI-driven insights, a company understands the “why” (like efficient issue-resolution).

Automated Resolution (AR) is a generative AI-driven metric that shows how effectively a chatbot resolves customer issues without needing a human. AR measures successful conversations that are accurate, relevant, and safe. 

AR directly answers this critical question for CX leaders by quantifying the success rate of chatbot interactions.

If your AI system doesn’t do this automatically, you can manually assess AR by following these steps::

1. Take a statistically significant sample of your chatbot’s conversations.
   
2. Review the transcripts and mark each conversation as resolved or unresolved, and contained or uncontained.
   
3. Divide the number of resolved and contained conversations by the total sample number to get the ARR%.
   
4. Use this formula to calculate AR across your entire conversation volume: AR = conversation_volume x ARR%

Using AI to improve customer service metrics: A step-by-step guide

If you want to supercharge your entire business , you need to take a deliberate and structured approach. Here’s how you can go about it:

1. Identify customer service goals: Clearly define your objectives for an AI-first customer service strategy, such as improving customer retention or enhancing upselling opportunities.
   
2. Audit existing customer service processes: Conduct a thorough review of your current customer service workflows and metrics, and pinpoint where AI can help.
   
3. Choose the right AI solutions: Opt for AI tools and technologies like predictive analytics and personalized recommendation engines that best align with your customer service goals.
   
4. Ensure data readiness: Prepare your customer data for AI processing to ensure it's clean, comprehensive, and well-organized.
   
5. Integrate AI with customer service platforms: Incorporate AI tools smoothly into your existing customer service systems, and ensure compatibility.
   
6. Train customer service teams: Equip your service and technical teams with the necessary training to effectively use AI tools to enhance their customer engagement strategies.
   
7. Develop AI-driven customer insights: Utilize AI to uncover deeper insights into customer behaviors, preferences, and potential churn risks.
   
8. Pilot and refine: Implement AI solutions in a controlled setting, and then gather feedback, analyze performance, and fine-tune your approach.
   
9. Address privacy and compliance: Make sure your AI implementations adhere to data privacy laws and ethical standards.
   
10. Monitor AI impact on customer service: Continuously evaluate how AI influences top customer service metrics, using these insights for strategic decision-making.
    
11. Iterate and scale up: Based on initial results, broaden the use of AI across various aspects of customer service, remaining adaptable to new AI advancements.

The future of customer service metrics

Predictive analytics, machine learning, and natural language processing are setting the stage for a more proactive and personalized approach to customer service.

In the near future, we can expect AI to respond to and anticipate customer needs — offering solutions before the customer even identifies an issue. AI solutions will achieve this by analyzing large datasets, including past interactions, purchasing history, and even social media behavior.

Voice and emotional analytics are also on the horizon, with AI being able to understand not just what’s being said, but how it's being said, recognizing customer emotions and adjusting responses accordingly.

As AI technology evolves, businesses will meet customer expectations in real-time, creating a more dynamic and satisfying customer experience. Those that follow these trends and incorporate AI into their customer service will see loyal, satisfied, and happy customers.