Qonto is the leading European business finance solution with over 500,000 customers. The company makes day-to-day banking easier for SMEs and freelancers by offering an online business account combined with finance tools, such as invoicing, bookkeeping, and spend management.
Recently, the company became one of the first Chattermill customers to test Chattermill’s latest product - Speech Analytics - which enables them to integrate customer and prospect calls into their Chattermill account for in-depth analysis.
By leveraging customer insights from their voice data, Qonto aims to become a more agile and customer-centric company:
- Product teams can analyze support and prospect calls to pinpoint critical product issues and identify areas for improvement
- Insights from call recordings alongside other data sources will provide a holistic view of their customer journey to inform the product strategy
- Customer calls will offer valuable insights into what prospects seek in a business finance solution, enabling Qonto to refine its product offerings and marketing strategies
We spoke with Sara Huier, Voice of the Customer Expert, to explore how Qonto plans to leverage customer insights from voice data alongside other feedback sources to gain a holistic view of their customer and user experience.
Q&A with Sara Huier, Voice of the Customer Expert at Qonto
Q: Can you tell us about your journey at Qonto and how customer feedback has influenced your role and the company’s growth strategy?
Sara: I'm Sara Huier, and I've been with Qonto for the past 2.5 years, working at the heart of our customer experience efforts. I started as a Customer Insight Expert for the Operations Team and have since transitioned to my current role in corporate strategy as a Voice of the Customer Expert. This journey has given me a unique perspective on how customer feedback can drive both operational excellence and strategic decision-making. In this position, I’ve been at the forefront of a significant transformation in our approach to customer feedback.
We don’t just listen to customers to support them - we actually use their insights to shape our growth strategy. And it’s been an exciting journey.
At Qonto, we are Europe’s leading business finance solution, committed to revolutionizing banking for SMEs and freelancers. When our founders, Alex and Steve, started Qonto back in 2016, they had a vision of making business finance simple, fast, and intuitive. Fast forward to today, and we’re serving over half a million customers across Europe in four countries: France, Italy, Spain, and Germany.
What sets us apart is, first, our comprehensive offering. We provide an online business account combined with invoicing, bookkeeping, and spend management tools - all in one place. But our true differentiator is our customer obsession, which has led to exceptional customer satisfaction. We’re proud to maintain over 70 NPS points, high CSAT scores in customer support, and industry-leading ratings on review platforms.
Q: Could you tell me a bit more about your recent experience with Chattermill's Speech Analytics at Qonto? We're curious to hear about your approach before Chattermill, the challenges you faced, and what objectives you were aiming to achieve.
Sara: We’re still in the early stages, but the initial findings and the process itself have been really interesting. Before working with Chattermill, our approach to voice data was fragmented. We were using a mix of systems - some in-house tools and some third-party software - to analyze customer interactions across different channels. We faced inconsistent data categorization, spent hours on manual analysis, and struggled to identify trends across the various feedback channels, let alone in real-time. We knew we had valuable data, but figuring out how to extract it efficiently was a real challenge.
Recognizing these limitations, we set clear objectives for our voice strategy. We wanted to consolidate all customer feedback into a single comprehensive view, automate the analysis of large volumes of speech, and do this in real-time. That’s where Chattermill came in. For the past three months, I’ve been overseeing the testing of Chattermill's Speech Analytics and it’s been an intensive process of testing, providing feedback, and assessing the potential impact on each department.
Q: Could you provide any examples of insights you have discovered so far?
Sara: We started by integrating speech data, experimenting with transcription, creating dashboards, and testing real-time analytics. While it’s still too early to draw firm conclusions, we’ve already uncovered some interesting insights. For example, we noticed a trend of customers expressing interest in more advanced billing features, which led to discussions and improvements in our product.
Chattermill's Speech Analytics also helped us identify areas for improving customer experience. For instance, we found an opportunity to streamline a specific step in our onboarding process, which allowed us to enhance the service and create a more positive journey from the beginning. These initial insights are just the starting point, but as we refine our approach and expand the dataset, we expect to uncover even more valuable insights.
Overall, the work with voice data has been very positive so far and aligns perfectly with our commitment to better serve our customers. For us, it’s about setting new standards in customer-centric banking, and I believe we’re making significant progress in that direction with this tool.
Q: Could you walk us through how you use the platform and give us a quick tour?
Disclaimer: For privacy and security reasons, we could not use real Qonto customer data. The environment shown here is a mock setup created with dummy data for demonstration purposes only.
Sara: Of course! Let’s say we want to gather some initial insights. I’d start by selecting a metric - often it's the sentiment distribution, which shows us the ratio of positive, negative, and neutral mentions for specific categories or topics we’re tracking.
We can see here that under "Payments," there’s a spike in negative sentiment. To dig deeper, I’d select "Payments" and explore what’s driving that negativity. In this case, we can see that issues related to the local tax system are behind it.
Next, I’d want to know which specific location this concerns since we operate in different markets. Let’s break down "Local Taxes" by country. Not surprisingly, we find that Spain has a spike in negative reviews.
By clicking for analysis, our AI-powered Insights Assistant shows us the specific clusters of topics driving the negativity. In this case, it’s related to increasingly frustrated customers in Spain over tax payments.
Individual call details
Sara: On the left, we have all our filters as usual, and in the center, we have summaries of the analyzed calls, allowing us to easily navigate the findings in this new interface.
Let’s take the first example: customers seeking assistance with paying local taxes in Spain. Clicking on the first call, we can immediately open a summary on the right side, which I really love. It gives an overview of the call, highlighting key pain points and feedback. Even for a long 20-minute call, the summary condenses the main topic into a few lines.
The "Pain Points" section quickly identifies where the customer journey went wrong, while the "Feedback" section shows the outcome—whether the issue was resolved and if the customer was satisfied. In this example, the summary confirms the problem with paying taxes in Spain, and the pain point aligns with this issue. The feedback shows that while the customer appreciated our workaround, they also expressed a desire for better support for local taxes in Spain.
What I find particularly useful are the "Outlines," which break down long calls into specific topics discussed at different points. For lengthy calls, this is especially helpful. If I want to dive deeper, I can check the transcript or listen to the recording directly.
This example highlights a known issue, but Chattermill's Speech Analytics helps us confirm these insights more efficiently.
Sara: For a more positive view, we can look at the Positivity Index, a metric I often use to understand what’s working well and what we should continue focusing on because customers appreciate it. For example, here we see an "Amazing Service Experience" category with a score of 150 points, but it’s a macro category, so it doesn’t give much detail. Let’s break it down by theme to see what’s driving this positivity.
As expected, the theme that stands out is customer support responsiveness. I’ll click for analysis, and the AI-powered highlights show that customers are very satisfied with our 24/7 support, praising its critical role in their experience. We can further explore this by checking the feedback through new responses.
Taking the first call as an example, the customer reached out late at night regarding a failed payment with a supplier. The main pain point was the payment failure, but the feedback shows they appreciated the prompt and efficient resolution. The outlines confirm that the issue was resolved quickly, and the customer was satisfied shortly after the call began.
Again, I can dive into the transcript or play the recording to review the specifics. Although this is dummy data, it reflects what our customers love most - our 24/7 support team.
Q: Given these early successes, and it’s only been a few months, how do you see Chattermill's Speech Analytics influencing your strategy or product roadmap at Qonto over the next 6 to 12 months?
Sara: That’s a great question. In the next 6 to 12 months, I believe Voice Analytics will play a key role in a few specific areas. First, it will help us better understand the needs of potential customers by analyzing voice data from initial inquiries and consultations. This will provide valuable insights into what prospects are looking for in a business finance solution, allowing us to refine both our product offerings and marketing strategies to meet those needs from the outset.
Secondly, we’ll use it to improve the onboarding journey. By analyzing interactions in the early stages, we can identify pain points and areas of confusion for new users, helping to create a smoother transition for customers as they start using the platform.
And finally, most importantly, Speech Analytics will contribute significantly to enriching our product roadmap.
Q: How are you planning to integrate voice data with the other data sources you're currently analyzing? Are you considering it a separate project, or do you see it as part of a larger integration effort?
Sara: Speech Analytics will be a key component of a larger project, which aims to unify various customer insights sources within Chattermill. This is the first time we are implementing this unified approach, allowing us to accurately identify the most frequently mentioned missing features and desired improvements within a broader context.
By integrating insights from various touchpoints - such as customer support tickets, surveys, and review platforms - we'll be able to develop a comprehensive view. I’m confident that combining the voices of current customers with those of prospects will provide us with a 360-degree perspective, which is crucial for reinforcing our leadership in customer satisfaction within the fintech space.
Q: Thank you, Sara, for sharing Qonto's journey with Chattermill’s Speech Analytics so far. It's inspiring to see how this product is already shaping a customer-centric strategy and driving real impact through tangible use cases.
You can watch the full interview with Sara here.
Learn more
Voice data is just one part of how your customers interact with your business. They also provide feedback through product reviews, surveys, customer support emails, or social media. To get the full picture, you need to monitor customer experience across all channels. Chattermill's Speech Analytics, integrated into the Customer Experience Intelligence platform, combines voice data with feedback from every channel, giving you a complete view of your customer journey.
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