From ticket to transformation: AI-powered analysis of support interactions as key to efficiency and a better CX

Every day, companies receive a flood of information hidden in support emails, chats and service calls. Feedback is omnipresent. And yet most of it remains unconsidered. There are now technologies that allow AI to summarize books and simulate human conversations – but why are we still unable to extract the sentiment from the conversations customers have with us?

For decades, surveys have been the tool of choice when it comes to understanding and improving the customer experience (CX). But traditional surveys only capture a fraction of customer voices. The reality is that the most detailed, unfiltered and most importantly actionable feedback is often found in conversations and interactions with customer service. This source of information is found in tickets and transcripts buried in CRM systems that relevant stakeholders such as product, marketing or leadership teams do not have access to.

Unlocking the value of support interactions

Customer service is no longer just a reactive function, but a proactive one. Every “How do I reset my password?” or “The login is not working” has the potential to create a better customer journey.

Thanks to AI, we can now analyze huge amounts of unstructured support data: Emails, chat and call logs as well as data from platforms such as HubSpot, Meta, Salesforce and Co. on a large scale. With tools based on natural language processing (NLP) and machine learning, companies can automatically determine the reasons for making contact, group feedback by topic, recognize moods and emerging problems before they become crises.

According to a recent Capterra study, more than 55% of companies in Germany already use AI in customer service. This puts Germany in third place worldwide. 60% of these companies expect AI to reduce costs and improve efficiency. Increased productivity (61%) and improved customer satisfaction (49%) are the most frequently cited benefits. However, there are still concerns: loss of trust (43%) and data protection (36%) are challenges that need to be addressed, as is transparency.

But even these concerns overlook an important point: AI still needs to be monitored by humans. It in no way replaces human expertise, but enhances our ability to understand and react. AI does not replace humans, but is a tool to improve human performance. While humans excel in emotional intelligence, relationship building and nuanced personal interaction, AI brings strengths in data analysis, multilingual communication and efficient handling of repetitive tasks. When combined, the result is not a substitution, but a synergy.

Breaking down silos – creating one voice

One of the biggest missed opportunities in customer experience is the fragmentation of customer data. Feedback from surveys, NPS scores, support tickets, social media and product reviews all provide part of the story, but are rarely told together.

Although customer service is at the forefront of customer interaction, it is often separated from marketing, sales and product teams. This separation not only restricts the flow of valuable insights, such as recurring complaints, unmet needs or new customer expectations, but also hinders cross-functional efficiency. Customer service is a treasure trove of qualitative data, but without the right integration, its potential for broader business strategy remains untapped.

By merging support interactions with traditional feedback sources, companies can close the feedback loop and create a 360-degree view of their customers. This unified approach is not only efficient, but strategic. It enables service, product, marketing and sales teams to act on real-time insights and make customer-centric decisions that reduce costs, drive customer loyalty and improve products/services.

Let’s take the use case of broadband provider Fibrus as an example. By analyzing support interactions with the help of AI, the company achieved remarkable results in just 8 months:

• NPS skyrocketed from -28 to +56
• Customer contacts with the service team fell by 30%
• Trustpilot rating more than doubled, from 1.7 to 3.9.

The takeaway from this? Analyzing not only what customers say in surveys, but also what they ask in all support channels, what they complain about and what they have challenges with, turns noise into knowledge. This is so important because companies today need much more than just a feedback tool. They are looking for a solution that provides deeper insights and valuable support in developing an actionable strategy to improve the overall customer experience.

Ultimately, AI-supported analysis offers the benefits of a combined solution for two important goals: improving customer service at multiple touchpoints along the customer journey and improving the customer experience as a whole, which in turn saves costs in the longer term.

Smart data analysis with the help of generative AI

The next level of understanding customer feedback is achieved through generative AI, i.e. tools that not only analyze data, but interact with it. A team can query historical support data, such as: “What are the top three issues new users report in the first week?” Instead of waiting for a business intelligence team to create dashboards, customer service and CX experts can gain immediate, actionable insights. This democratization of data access enables faster decisions and smarter solutions.

Generative AI also opens new doors in the customer interaction itself by helping customers solve problems faster, suggesting answers to service agents or summarizing long interactions for efficient follow-up.

From reactive to proactive: unstructured data as a gold mine

AI-supported analyses enable trends to be identified at an early stage. With a context-sensitive alert system, companies can identify recurring problems or defects early on before they spread or impact KPIs. It’s a shift from reactive service to proactive CX design.

Außerdem können Unternehmen, durch das Verständnis der Ursachen für Support-Kontakte, Self-Service-Tools verbessern, die Dokumentation aktualisieren oder Produktprobleme beheben, was letztlich zu einer Reduzierung des Supportvolumens und der Betriebskosten führt.

AI on the rise as a competitive advantage

Customer service is no longer a cost center, but a listening post. But it takes more than ticket systems and satisfaction statistics to really listen to customers. It takes intelligence.

AI helps to gain insights from existing data. It connects the dots between customer pain points and business outcomes. And when used responsibly, with a focus on transparency, privacy and human control, it can drive a level of customer understanding that was previously unattainable.

The results are tangible and measurable, such as increased operational efficiency and an overall increase in customer satisfaction, combined with time and cost savings through automation and the implementation of a centralized solution. There are also the benefits of access to informed, data-driven decisions that are immediately actionable, through summaries, and deep insights into critical support issues. Finally, AI-powered analytics supports the implementation of context-sensitive business KPIs for more control to make improvements that relate to the most important metrics in each specific case.

With all these potential benefits in mind, it’s clear that analyzing support interactions using AI is not just an opportunity, but a necessity for any company serious about its customer experience.

Would you like to learn more about how to turn support interactions into data-driven insights using AI? Read our brochure to find out how you can put customer service at the heart of your product and service strategy: Download brochure