Customer Dissatisfaction Score: Identify, Measure & Reduce Customer Frustration

The customer gives the interaction a score of 2 out of 5 in the survey that follows the call. When this response appears in the dashboard, the call will have long since ended, the agent will have moved on to the next queue, and the problem in question has probably already occurred three more times that day. A DSAT that is based completely on surveys only identifies those customers who were unhappy. It doesn’t provide fast enough feedback to allow any action to be taken, and it only includes a small number of customers who take the trouble to respond.

What DSAT Actually Measures

The Dissatisfaction Score, or DSAT, is the opposite of CSAT since it is the percentage of customers who give a poor rating to an interaction, generally a 1 or 2 out of 5, calculated by dividing the number of dissatisfied replies by the total number of replies. A DSAT of under 10% is usually regarded as healthy, while one above 20% indicates a problem that should be investigated right away. While CSAT shows you who is satisfied, DSAT makes it clear where things are going wrong, which is why it is important even if the overall satisfaction figure appears to be okay.

The issue isn’t the metric but the input; survey-based DSAT only ever receives feedback from customers who respond, and response rates for the surveys sent after a call are low. It is precisely the interactions which never get surveyed, such as those in which a customer hung up annoyed and never returned, that a method relying only on surveys fails to capture.

Detecting Dissatisfaction Without Waiting for a Survey

Convozen’s AI Analysts analyse 100 per cent of voice, WhatsApp, email, and chat conversations, not just the portion which happens to elicit a survey response. Rather than relying on a customer to report their frustration afterwards, the system looks for the signs of frustration within the conversation itself.

It works since Convozen’s speech processing system is designed for the kind of audio found in real contact centres, not for clean recordings. The system has been trained on more than 50,000 hours of telephonic speech and then fine-tuned using over 4,000 hours of hand-annotated data, which enables it to perform well in cases of noisy calls involving accents and code-switching in which a great deal of genuine frustration is expressed and where tone-blind tools usually fail to pick it up.

What that produces, across every conversation:

From Signal to Root Cause

Only has value the ability to detect dissatisfaction if it results in some action. Convozen’s Automated Quality Assurance system checks each conversation against the SOP coverage, the standards for opening and closing calls, and the business-specific parameters, enabling the cause of an increase in dissatisfaction to be identified specifically, for example, a gap in training, a problem with the script, or a policy that customers keep pushing back on, rather than leaving it as a mysterious figure on a dashboard.

Audit Copilot takes the same data and automatically fills in the audit forms in line with the team’s current manual procedures, recording the subjective judgments such as strength and weakness at the individual call level, so when a QA lead looks at a sudden increase in dissatisfaction, they aren’t beginning from scratch. Moreover, since Agent Assist displays mood insights to the agent during the ongoing call, some of that frustration is identified and dealt with before it has a chance to become a DSAT number.

Why Full Coverage Changes the Picture

Cars24 carries out its support operations using Convozen, employing automation and event triggers which automatically convert key moments into follow-ups, tags, escalations, and updates regarding disposition, all supported by live performance dashboards on a per-representative basis. That is what sets reacting to a dissatisfaction score afterwards apart from detecting the moment it is emerging.

Every conversation, not just a sample: dissatisfaction signals are picked up regardless of whether the customer completes a survey

• It is real-time, not retrospective: the mood reaches the agent during the call, before the customer becomes frustrated and hangs up.
• Not just a number, but the root cause. The SOP and QA scoring analysis trace the spike in dissatisfaction back to the actual failure.
• Designed for use with real contact centre audio. Frustration is accurately detected even in the presence of noise, different accents, and code-switching.

Catch Dissatisfaction with ConvoZen Before It Becomes Churn

See how Convozen surfaces frustration signals across 100% of your conversations, not just the ones customers rate. Book a Demo