Customer care leaders are facing significant challenges as they navigate the transition to AI-driven operations while trying to meet business targets and rising customer expectations. The shift from traditional, human-led customer service to AI technologies represents one of the biggest disruptions in customer care history, leaving many leaders feeling caught between outdated models and underperforming digital solutions.
Working with our clients implementing AI solutions in customer service, we’ve found that successfully adopting new technologies like AI requires a fresh look at how we set goals for customer service (CS) performance and how we measure and monitor performance. These are also the starting points for building a modern and robust CS function that is able to utilize the advantages of AI or any other technology. A well-thought-out and implemented performance audit and enhancement effort not only contributes to CS’s tech adoption readiness, but also to customer satisfaction, a key factor in overall business performance.
The State of Customer Service in 2024
According to Baydr Yadallee, a commercial leader driving growth and digital innovation, customer service is at a critical juncture in 2024. Inspired by an article in the Financial Times titled ‘How did customer service get so bad?’, Yadallee highlights that customer satisfaction levels in the UK are at a nine-year low. This decline, supported by data from the Institute of Customer Service, underscores the challenges businesses face as they navigate the increasing complexity of customer interactions in a digital age.
The data is compelling: companies with customer satisfaction scores at least one point higher than their sector average achieved average compound-revenue growth of 7.4% between 2017 and 2023. In contrast, firms with scores at least one point below the average saw flat revenue growth. Despite these clear benefits, Yadallee notes that many organizations still underinvest in customer service, choosing instead to focus on developing new products and services.
This underscores the importance of measuring and monitoring customer service effectiveness, as it has become more than just a support function; for the best-in-class business players, it is a major contributor to overall success. Without the right metrics in place, it’s impossible to know if your customer service efforts are truly meeting goals, and it becomes difficult to see whether the new AI solution is yielding the expected results. As companies try to balance the efficiency gains offered by AI with the need for personal interaction, using the right metrics is key. Before turning to the KPIs that need to be tracked for AI adoption, let’s take a look at what companies expect from an AI customer service project.
Expectations of Companies Adopting AI Solutions in Customer Service
Businesses see AI not just as a tool for automation but as an opportunity for redefining customer interactions and enhancing efficiency. Here are the most common expectations we see:
- Improved customer experience: AI technologies, such as chatbots and virtual assistants, are expected to provide immediate, 24/7 support, leading to higher levels of customer satisfaction. Additionally, businesses anticipate that AI’s ability to analyze vast amounts of data in real time will enable more personalized and relevant interactions.
- Enhanced efficiency and cost savings: by automating routine tasks, such as answering FAQs, routing inquiries, or handling simple transactions, companies expect to reduce the workload on human agents, leading to faster response times and reduced operational costs. This efficiency is also anticipated to free up human agents to focus on more complex and value-added interactions, improving service quality.
- Reduction in human error: by automating repetitive and data-driven tasks, companies anticipate fewer mistakes in handling customer inquiries, processing transactions, or managing customer data. This reduction in errors is seen as a way to improve accuracy, build trust with customers, and maintain high service standards.
- Data-driven insights: companies expect AI systems to analyze and interpret customer data, uncovering patterns, preferences, and emerging trends. These insights are valuable for improving service offerings, identifying potential issues before they escalate, and tailoring marketing efforts.
These expectations underscore the strategic importance of AI in customer service, as smart companies view it not just as a technological upgrade but as a critical component of their long-term success. However, to meet these expectations, businesses must approach AI adoption thoughtfully, ensuring that their existing customer service foundations are solid and that they have a clear understanding of how AI will integrate into and enhance their operations.
What Key Performance Indicators (KPIs) Should Be Tracked before AI Adoption
A wide variety of KPIs exist for measuring customer service effectiveness. From the viewpoint of AI adoption readiness, we recommend starting with three top KPIs listed below. Why? The reason for this comes from the expectations for AI implementation.
1. Customer Satisfaction Score (CSAT)
What it measures: the level of customer satisfaction with specific interactions or overall service.
Why it’s important: tracking CSAT helps us understand how well our current customer service is meeting expectations. It serves as a benchmark to measure improvements post-AI adoption.
Why it’s top: CSAT is a direct measure of how satisfied customers are with our current service levels. Since AI adoption aims to improve the customer experience, this KPI provides a crucial baseline to measure the impact of AI on customer satisfaction. Improvements in CSAT post-AI implementation would indicate successful enhancement of the customer experience.
2. First Contact Resolution (FCR)
What it measures: the percentage of customer inquiries resolved during the first interaction without the need for follow-up.
Why it’s important: high FCR rates are a sign of effective problem-solving. Tracking this KPI provides insight into how well your team currently performs and how AI might enhance first-contact resolutions.
Why it’s top: FCR is a critical indicator of the efficiency and effectiveness of your customer service. AI technologies are often implemented to improve FCR by providing more accurate and timely solutions, making it a key KPI to track before and after AI adoption.
3. Cost Per Contact
What it measures: the total cost associated with each customer service interaction, including staff time, technology, and overhead.
Why it’s important: understanding this metric helps in budgeting and ensuring that customer service operations are cost-effective. Tracking this before AI adoption allows you to measure cost efficiency post-implementation.
Why it’s top: understanding the current cost per customer interaction is essential for evaluating the financial impact of AI implementation. AI is often expected to reduce costs by automating routine tasks, so tracking this KPI before adoption helps you assess whether AI delivers the expected cost efficiencies.
These three KPIs—CSAT, FCR, and Cost Per Contact—offer a comprehensive view of customer satisfaction, service efficiency, and cost-effectiveness, making them the top metrics to monitor before adopting AI in customer service.
Addressing the Complexities of Customer Journeys
It is important to note that many organizations fail to fully understand the complex nature of their own customer journeys. This lack of understanding often leads to ineffective service design and a reliance on superficial metrics, such as average handling time, which do not capture the essence of issue resolution and customer satisfaction.
Mapping out the entire customer journey, identifying touchpoints of frustration, and aligning on desired outcomes for both the customer and the business are essential steps. This holistic approach should involve every department and function within the organization, emphasizing the interconnectedness of their roles in shaping the customer experience. By breaking down the problem into constituent parts, businesses can tackle these challenges systematically and make meaningful improvements to their customer service operations.
Conclusion
In summary, before diving into AI implementation, it is worth considering the optimization of customer service’s core elements. By paying close attention to essential KPIs like customer satisfaction, first contact resolution, and cost per contact, organizations can create a solid foundation that maximizes the benefits of AI. This preparation is crucial for ensuring that AI not only integrates smoothly but also delivers on its promise to transform customer service into a strategic asset that boosts satisfaction, efficiency, and overall business performance.
