Measuring the service quality of a company involves comparing what the customer expects with what they actually perceive. This comparison relies on specific indicators, but also on gaps that are often poorly identified between internal processes and the experience lived by the customer. Understanding where these gaps lie allows for targeting the most cost-effective corrective actions.
Perceived service quality and delivered service quality: measurable gaps
Most companies confuse two things: the compliance of their internal processes and the customer’s perception of them. An average response time may seem acceptable in a dashboard, while being judged insufficient by customers contacting support for an urgent matter.
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Three types of gaps consistently appear in service quality evaluation processes.
| Type of gap | What the company measures | What the customer perceives |
|---|---|---|
| Reactivity | Average first response time | Perceived time before effective problem handling |
| Reliability | First contact resolution rate | Number of follow-ups needed before complete resolution |
| Empathy | Adherence to welcome script | Feeling of being listened to and adapted to personal situation |
The reactivity gap is the most common. A company may show a quick response time by counting the first automatic response, while the customer expects a human and contextualized reply. To delve deeper into these distinctions, a file dedicated to service quality on Décideur details the criteria to monitor as a priority.
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Service quality indicators: choosing those that reveal the real problems
The overall satisfaction rate (CSAT), the Net Promoter Score (NPS), and the first contact resolution rate are the three most commonly used indicators. They do not measure the same thing and are not interchangeable.
The CSAT captures immediate satisfaction after an interaction. The NPS assesses the customer’s likelihood to recommend the company, reflecting a longer-term relationship. The first contact resolution rate measures the operational efficiency of support.
Why a single indicator is not enough
A high NPS can coexist with a low resolution rate if customers appreciate the product but experience poor after-sales service. Crossing at least two indicators avoids blind spots.
The choice of indicators depends on the dominant contact channel. For a call center, the pick-up rate and average handling time are priorities. For a digital customer service, the first response time on chat or email and the customer effort score (CES) provide a more relevant reading.
- CSAT: to be collected immediately after the interaction, with a single question on a scale of 1 to 5 to maximize response rate
- NPS: to be sent periodically (quarterly) rather than after each contact, to capture the overall relationship
- First contact resolution rate: to be measured from the customer side and not the agent side, as a ticket closed by the agent can be reopened by the customer
- CES (Customer Effort Score): useful for identifying overly complex journeys, particularly in complaint processes
Analysis of customer verbatims with generative AI: what scores don’t reveal
Quantitative indicators reveal trends. They do not explain why a customer is dissatisfied. Since 2023, companies in banking, e-commerce, and SaaS have been deploying generative AI systems to continuously analyze verbatims from calls, emails, and chats.
Generative AI identifies recurring irritants in unstructured feedback, then prioritizes them by frequency and estimated impact on satisfaction. This processing allows for a shift from a static dashboard to a dynamic problem detection system.
Limits of the automated approach
Automated analysis of verbatims works well with high volumes. Below a certain threshold of monthly contacts, statistical noise makes the results unreliable. Human review of a sample remains necessary to calibrate the algorithms.
The other limitation concerns collection biases. Customers who leave a comment after an interaction are often those whose experience was either very positive or very negative. Average experiences, which are the majority, remain underrepresented in verbatims.
Employee experience and service quality: the correlation that dashboards ignore
The customer experience barometers published by major analysis firms in 2023 and 2024 confirm a strong correlation between employee engagement and the service quality perceived by customers. A trained and listened-to employee resolves issues faster and generates fewer complaints.
This correlation has concrete implications for quality management. Investing in ongoing training for teams in contact with customers produces measurable effects on the first contact resolution rate and on CSAT. Conversely, multiplying rigid scripts and top-down quality controls can degrade the empathy perceived by the customer.
Training and team autonomy
Two levers emerge from documented feedback. The first is training for complex situations, which reduces the need for escalation to a supervisor. The second is the decision-making autonomy granted to agents to resolve an issue without hierarchical validation.
These two levers directly affect resolution time and the customer’s feeling of being listened to, which are precisely the two dimensions where gaps between perception and internal measurement are most frequent.
Service quality cannot be managed solely from a dashboard. Quantitative indicators diagnose the situation, verbatim analysis identifies the causes, and investing in teams addresses the problem at its source. The most underestimated lever remains the gap between what the company believes it measures and what the customer actually experiences.