From data gaps to business impact: a customer service transformation

Marta Marszalek is currently the Manager of Customer Support. However, a few years ago, she stepped into a team leader role in customer service at a pension and life insurance company, which was undergoing a period of change in which the task at hand was clear. The role was designed to be both operational and strategic, with a focus on leading and developing the team in their day-to-day work. The company, with around a hundred employees, was in the midst of shifting from a more traditional way of working toward a modern, flexible mindset.

The desire to evolve was there, but the structures, systems, and processes hadn’t quite kept pace. It didn’t take long for Marta to realise that the real challenge had nothing to do with engagement or work ethic. It was about data.

“We had goals set for customer service, but we lacked the conditions to meet them. The numbers didn’t look good, but we didn’t actually know why,” she says.

That became the starting point for a change journey in which feedback data analysis would drive more efficient workflows, stronger cross-departmental collaboration, and a clearer connection between customer insights and business value.

When the numbers don’t tell the full story

Early on, Marta encountered figures that raised the alarm. The average call duration was around seven and a half minutes. Only about 70 percent of calls were answered. A large proportion of cases were being transferred to other departments. For an organisation with high ambitions, these were numbers that simply weren’t sustainable.

It became clear that the current situation needed to be challenged at a deeper level. Why were calls taking so long? Why weren’t targets being met? What were the calls actually about? And was there a pattern behind the numbers?

“I wanted to put words to both the soft and the hard. The team felt that something wasn’t working, while management was looking at numbers that didn’t meet expectations. I needed to bridge those two worlds and find the root cause.”

As it turned out, 60 percent of all calls were about the same thing: gaps in the company’s web functionality. The calls lasted 15 to 30 minutes because something in the funnel wasn’t working properly. The high volume of lengthy calls was raising the average handle time, which in turn pushed queue times higher.

“Suddenly, it became clear. It wasn’t customer service that was inefficient. It was a web issue that was generating traffic, extending calls, and disrupting the entire flow.”

When the findings were presented to the leadership team, it served as a wake-up call. What had previously felt like a vague, hard-to-pin-down problem now received objective proportions.

Making a manual analysis in a numbers-driven industry

The road to those insights was anything but automated. Marta counted emails by hand, sorted cases into Excel spreadsheets, and waited weeks for new call labels to be implemented in the systems. Phone statistics were lacking, and the ability to influence system settings was limited.

“We knew very little about our own data, and the systems weren’t built around our needs. To understand the reality of what was going on, I literally had to count things myself.”

In a finance and insurance context, where numbers are central to decision-making, it became clear that even the customer insights needed to be presented in numbers too.

“In this industry, you have to show change through graphs, tables, and percentages over time. Only then does it become clear what’s actually happening and why we need to take action.”

Translating softer values, like customer experience and frustration, into hard, measurable insights proved critical to building momentum for change.

Customer service as a goldmine of insights

One of the most significant realisations was that customer service occupies a unique position in any organisation.

“If something isn’t working on the website, customers call customer service, not IT. If a piece of communication is unclear, they reach out to customer service, not marketing. We’re a goldmine of customer insights.”

Despite this, there were no systems in place to structure, analyse, and route those insights further to the right departments. Marta describes how better integrated systems could have automated parts of the analysis and more quickly delegated tasks to the right teams, IT or marketing, for instance.

“With better analytical support, we could have worked in completely different. If calls and messages had been automatically categorised and analysed on an ongoing basis, ideally with the help of AI, we could have spotted the patterns straight away and spent our time on actionable solutions rather than compiling data,” she says, adding:

“If we’d had the right tools from the start, we could have worked more proactively, identified patterns in real time, and turned insights into concrete actions. When analysis happens continuously and in a structured way, it stops being a side project and becomes a natural part of business development. That’s where real impact is created.”

From analysis to action

Once the root causes had been identified, the work of change began to gain momentum. The IT department improved the web functionality. Marketing mapped the customer journey and refined communications. A new contact system was brought in to enable better measurement, follow-up, and transparency.

The organisation gained better tools to track transferred calls, measure response times more precisely, and give each team member a clearer picture of their own performance. The handling of calls became more efficient through improved workflows and fast identification of customers.

A new target was set: 85 percent of all calls should be handled directly by customer service. To get there, the reasons for transfers were documented, and knowledge gaps were identified and addressed.

The results spoke for themselves. The proportion of calls linked to the web issue dropped from 60 to 15 percent. Average call duration fell from 7.5 minutes to 3.5 minutes. Average queue time plummeted from 400 seconds to just 30.

Data that drives engagement, self-leadership, and business results

Transparency around the numbers also changed the team’s dynamic. Employees could track their own performance and became more autonomous. When new targets needed to be set, it was the team itself that wanted to increase the ambition even further.

“When people are given clear conditions, transparency, and a real sense of ownership, something shifts. They start driving the development forward themselves.”

The effects made a clear impact on the business results. Shorter call times and dramatically reduced queue times freed up resources, improved availability, and created a more sustainable cost structure. Clearer, more reliable data made it possible to prioritise the right development efforts, make decisions based on facts rather than assumptions, and steer the work efforts toward measurable business value. The customer insights became a concrete foundation for strategic decisions at crossroads and long-term planning. 

It wasn’t just business successes that became measurable. Medarbetarengagemanget ökade markant och teamets interna NPS nådde höga nivåer. Employee engagement rose sharply, and the team’s internal NPS reached impressive levels. For Marta, that was confirmation that data-driven change doesn’t just strengthen the bottom line, but also creates a genuine sense of participation and pride.

“When we could show progress over time, in clear graphs and figures, it became obvious what was making a difference. It didn’t just motivate the team; it also built trust with management. That’s when customer insights truly become business results,” Marta concludes.