Customer service feedback / complaints
Customer satisfaction
Understand the reasons for complaints and improve your after-sales service.
Description of the need
Our client is a company with a centralized after-sales service that handles a large volume of customer requests every day: complaints, requests for assistance, product returns or disputes.
While the teams had tools to manage tickets and process requests, they lacked visibility into actual customer satisfaction after their issues were resolved. Customer feedback was poorly structured, and the causes of complaints were difficult to analyze comprehensively.
The client was looking for a solution that would:
- To measure satisfaction after an interaction with customer service
- To understand the reasons for customer complaints
- To identify recurring problems in products or services
- To assess the quality of the processing of requests
- To quickly detect situations of persistent dissatisfaction
The objective: to improve the quality of after-sales service and reduce sources of dissatisfaction.
Proposed solution
We have implemented a system of satisfaction surveys that are automatically triggered after a customer service request has been processed.
The questionnaires are sent to customers via:
- Email after a ticket is closed
- SMS after an exchange with support
- Link integrated into case follow-up communications
The surveys allow us to measure several indicators:
- Overall satisfaction with demand management
- The speed of response
- The quality of the response provided
- The effective resolution of the problem
The responses are centralized in an analysis platform that allows:
- To identify the most frequent reasons for complaints
- Analyzing customer feedback to understand the pain points
- To monitor satisfaction after requests have been processed
- To compare performance between teams or support channels
Customer service managers thus have reliable indicators to manage the quality of their service.
Special Features
The questionnaires were designed to be short and easy to complete in order to maximize the participation rate after a customer service interaction.
The system makes it possible to link each response to the corresponding interaction (call, support ticket, chat conversation…), thus facilitating analysis and operational monitoring.
The system makes it possible to link each response to the corresponding interaction (call, support ticket, chat conversation…), thus facilitating analysis and operational monitoring.
Dashboards allow you to:
- To monitor the evolution of satisfaction related to after-sales service
- To identify the recurring causes of complaints
- To detect cases of persistent dissatisfaction
- To evaluate the performance of the support teams
Automatic alerts also make it possible to quickly identify dissatisfied customers in order to implement corrective actions or personalized follow-up.
This type of system allows companies to transform complaints into opportunities for improvement and strengthen the quality of their customer relationship.
Such solutions can also be deployed in technical services, support centers, or organizations with a large volume of customer requests to process.
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recurring complaints thanks to reason analysis
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speed in detecting dissatisfaction after after-sales service
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improvement in satisfaction after requests have been resolved
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FAQ — Customer satisfaction, NPS and customer experience
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Who is the Soft Concept Customer Satisfaction solution aimed at?
It is aimed at companies in all sectors wishing to structure a CX approach and industrialize the collection and analysis of the Voice of the Customer.
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Does the platform integrate into an existing CX system?
Yes. It can complement or strengthen an already structured system without disrupting your processes.
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Are technical skills required?
No. The creation, distribution and management of surveys is designed to be used directly by CX teams.
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How does AI add value?
It helps to formulate the right questions, automatically analyze the answers, detect trends, and trigger alerts on weak signals.
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Is the solution suitable for large volumes?
Yes. It is designed to handle large volumes of responses and verbatim comments, while maintaining fast analysis.
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