Contact Center: How to Turn Complaints into Insights

Context

The bank’s contact center handled hundreds or thousands of customer requests every day. The team helped solve problems, defused tension in difficult moments, and effectively kept the service running even when the flow of requests seemed endless.

But behind this operational heroism, there was another problem. The bank had learned how to deal with the consequences, but was doing very little work with the causes. Complaints kept repeating, the contact center kept processing them, but there was essentially no systematic work aimed at reducing the number of such requests.

Challenge

The turning point came when the chair of the management board asked a simple question that the bank did not have a clear answer to: why exactly are customers complaining, what can be done about it, and is there information inside these requests that could help systematically improve the bank’s products?

The problem was that there was a lot of data, but very little meaning. Requests were recorded, assigned to categories, and passed on for processing, but they did not form a clear picture of the main customer problems. The bank could see the flow of complaints, but it could not see which of them pointed to systemic failures in products, service processes, or digital journeys.

ApproachTo answer the leadership’s question, it was not enough to simply look at the contact center statistics once again. The problem started at the level of how requests were recorded. The bank had a complex categorization system that had historically grown to more than a hundred categories. Some of them were too broad, such as “Internet banking”; others were too technical, such as “token activation missing.”

In such a system, requests were recorded, but they did not explain what was actually happening with the customer experience.

We considered three possible approaches to categorizing customer requests.

The first was by responsible department. In this logic, requests are divided into topics related to loans, deposits, financial monitoring, or the branch network. For a large organization, this is convenient because each department sees “its own” problems. But this approach works poorly for complex customer situations, because they are often cross-functional. The problem emerges precisely because it has no single owner: none of the functions involved in creating it considers itself the main one.

The second was by the first identified cause. For example, a customer says their card is not working, and the operator sees in the system that the reason is an “inactive option” and labels the request accordingly. This seems logical because it immediately suggests what should be done with the specific case. But this kind of category describes the bank’s internal diagnosis rather than the customer’s problem.

Behind the phrase “inactive option,” there may be very different situations: a technical failure, poor communication, unnecessary product complexity, or even a feature that should have been enabled by default.

The third approach was from the customer’s point of view. This is the one we chose. In this logic, what matters is not which specific cause was triggered inside the system, but what the person cannot do. Not “token activation missing,” but “I cannot make a payment.” Not “inactive option,” but “I cannot add my card to my smartphone.” This logic does not always immediately explain the root cause, but it makes it possible to see the problem through the customer’s eyes and group scattered requests into much clearer and larger themes.

Only then does it become visible that many small and technical categories actually describe the same customer problem.

After that, we worked not with individual tickets, but with recurring scenarios. Each request was treated as a small piece of research: what happened, what the customer had been doing before contacting the bank, how they had tried to solve the problem on their own, and what they wanted to do after the issue was resolved. This micro-journey helped not only describe the request better, but also see which topics created the most friction for the customer and the greatest workload for the bank.

Key Insight

After changing the logic of analysis, the main thing became visible: despite the enormous number of requests, most of the load on the contact center was created by a relatively small number of recurring customer problems. What had previously looked like a chaotic mass of complaints actually formed a limited set of themes that systematically damaged the customer experience.

Diagram of the reasons why customers contacted the bank

This meant that the bank could work not only with the consequences, but also with the causes. When requests are grouped properly, it becomes clear which problems should be addressed first because they create both the most friction for the customer and the greatest load on the contact center.

This is how the contact center began to open up from a different perspective. Not only as a unit that heroically processes complaints, but as a source of insight into exactly where the bank is losing quality in the customer experience. This was the main shift: requests stopped being just an operational flow and became material for systemic product decisions.

Solution

The new logic of analysis gave the bank not just better analytics, but a basis for action. Once requests were grouped around real customer problems, it became possible to define priority themes for systemic change. The conversation shifted from “how do we process complaints faster?” to “what exactly needs to change so that there are fewer of these requests?”

At the same time, the analysis showed something important: not all requests should be reduced. Some categories are desirable both for the customer and for the business. These are situations where asking for help creates value, reduces anxiety, or helps a person get through a critical moment in their interaction with the bank. For example, if a customer has lost their card or suspects fraud, quick contact with the bank is not a problem, but a necessary part of the service.

Similarly, requests related to complex or risky financial operations may be desirable because they give the customer confidence and give the bank an opportunity to maintain trust in a sensitive situation.

Another group of requests, on the contrary, should not exist at all. They create no value for either the customer or the bank and arise only because there is unnecessary friction in the product, process, or digital journey. For example, if a customer cannot make a payment because the flow is confusing or the interface message is unclear, this request is a pure loss for both sides.

The same applies to situations where a person cannot add a card to their smartphone because of a technical or communication error and is forced to call the contact center only to bypass a problem that should not have existed in the first place.

This distinction allowed the bank to define the right type of solution for each major theme more precisely. If a category is desirable, it should not be artificially reduced. Instead, the bank should invest in the quality of support, speed of response, and the customer’s sense of security. If a category creates no value, it should not be processed better – it should be removed through changes in the product, service process, or digital experience.

As a result, the contact center began to play a new role for the bank. It became not only a point of reaction to problems, but also a source of insight for product, service, and digital teams. Instead of endlessly fighting the flow of requests, the bank gained the ability to distinguish where it needed to strengthen service and where it needed to eliminate the very causes of requests.

Result

The bank received not just a new way of looking at requests, but a working tool for prioritizing change. Instead of hundreds of fragmented categories, it gained a clear map of problems that made it possible to see what was behind the flow of complaints and where action should be taken first.

This changed the quality of the conversation inside the organization. The contact center stopped being only a point for processing requests and became a provider of information for product, service, and digital teams. The bank gained the ability to separately strengthen the scenarios where customers genuinely need live support and separately work on removing the barriers that should not lead people to the contact center at all.

In effect, this created a basis for systematic work on reducing unwanted requests. Not only to improve the efficiency of complaint handling, but also to reduce the very reasons why those complaints arise.

Conclusion

If your contact center accumulates hundreds of requests every day, it does not necessarily mean that the problem is only the workload. Often, behind this flow lies a limited set of recurring customer problems that can only be seen when you stop looking at requests from inside the process and start looking at them through the customer’s eyes. This is the moment when complaints stop being just operational statistics and become a source of insight for changes in the product, service, and digital experience.