Making Sense of Your Customer and Market Insights

Gathering customer and market insights is easy; turning those insights into confident decisions that drive growth is the real challenge.

Every company I speak with tells me a similar story. Some have too much data, others

have too little, but nearly all struggle to turn what they have into clear direction. Data comes from loads of different sources including customer surveys, CRM notes, support tickets, analytics dashboards, call transcripts, win-loss analysis, and competitor updates: each tell part of the story, but rarely the same one.

The problem is not collecting insight. It is connecting it.

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The widening gap between information and understanding

In my conversations with twenty-seven product and business leaders across Europe and North America, almost four out of five said they are collecting more customer or market data than they were a year ago. Yet fewer than half felt confident using it to guide decisions.

Larger organisations described fragmentation, with data scattered across multiple systems and owned by different departments. Smaller and mid-sized teams explained that they lacked a clear structure for capturing and revisiting customer input. This was not simply a question of company size, but of how insight work is managed. Incomplete understanding came up again and again as the central challenge.

Almost three-quarters of the people I spoke with said they rely on second-hand summaries of customer feedback instead of speaking to customers themselves. The result is a widening disconnect between what customers are saying and what companies believe they need.

Artificial intelligence is making collection easier, but interpretation is still not trusted. Automation can gather data faster, yet turning that data into trusted insight still depends on people. The more data companies collect, the more they realise that the real challenge lies in having the human time and empathy to make sense of it. Across every conversation, I’ve heard similar frustrations about why this gap persists and what gets in the way of understanding.

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Why are insights and understanding disconnected?

Across all these conversations, three patterns kept surfacing.

1) Fragmentation.

Customer and market information lives in different tools because it enters the business through different channels. Sales uses the CRM, support uses ticketing systems, and product uses research repositories. In my research, nearly three-quarters of participants said their data spans multiple systems or documents, often owned by different teams. When everyone is working from a different dataset, opportunities are missed or rediscovered months later. The most effective teams close this gap by combining what customers say with what the market shows.

2) Capacity.

Sixty percent of leaders said their teams do not have time to analyse the information they already collect. This is not because teams are inefficient. It is because interpretation requires a human with sustained focus, and that focus is constantly interrupted by the operational demands of shipping product. Automation captures data faster than ever, but making sense of it still requires human judgement, and there are only so many hours in the day.

3) Confidence.

Even when insights exist, decisions feel uncertain. As one VP of Product in North America put it, “It takes hours to make sense of a single customer interview, and by the time we do, the next one is waiting.” Without a clear view of patterns across all feedback, teams cannot distinguish signal from noise. Every data point feels equally important, so none of them do.

These patterns reveal something deeper than process problems. Collecting the data is not the issue. In startups, sales or founders often collect the insights and hand them off. In larger organisations, product or research teams conduct customer interviews while other departments gather operational data, sometime product doesn't talk to the customer, that's the job of sales and customer support. The real question is who connects all these dots. Who has the time, the focus, and the confidence to look across every data point and make sense of what it all means?

When no one owns this, insight work becomes something people fit in between everything else. The result is familiar in companies of every size: feedback keeps coming in faster than teams can make sense of it, and opportunities slip through the cracks.

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What effective teams do differently

The most effective teams I have seen treat customer and market insight as a strategic discipline, not an operational task. They create simple systems that make feedback patterns visible, identify the few signals that truly matter, and connect them directly to growth decisions. Most importantly, they give someone clear ownership of this work.

This is where my work as a Product Growth Advisor typically begins. I help teams build the structure and shared understanding needed to turn customer and market feedback into confident action. If you have not read it yet, my post Understanding the Role of a Product Growth Advisor explores this approach in more detail.

But even with the right structure, one constraint always remains: time. Analysing qualitative data requires sustained human focus, the kind that is constantly interrupted by the demands of shipping product. You can have strong systems and clear ownership, but if analysis still takes days, decisions slow down.

I have been exploring whether AI can speed up this process while keeping the human in the centre of it. The goal is not to replace human understanding but to remove the repetition that slows it down.

I've been working on a hypothesis that AI can actually speed up the process, without replacing the human in the flow. To tackle this challenge, I have been building an AI workflow that makes sense of scattered customer and market data. The goal is to help teams see patterns and opportunities faster by letting AI handle the repetitive parts of the analysis.

To tackle this challenge, I have been building an AI workflow that makes sense of scattered customer and market data. At the moment it is just a prototype, but it works like a small team of specialists. Each agent focuses on one task such as gathering, cleaning, analysing, or summarising information, then passes its work to the next. The final agent brings everything together in a simple interface that highlights emerging themes, contradictions, and opportunities.

Each part of the system uses the language model best suited to its task, whether that is OpenAI, Anthropic, or Google. This mix allows the workflow to mirror how human teams collaborate across different disciplines. It also keeps the analysis transparent and easier to adjust when something feels off.

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Making sense of fragmented data faster

I am currently looking for companies to pilot this system over the next eight weeks. Whether you have a dedicated product team or are handling product decisions alongside other roles, if you are collecting customer feedback in tools, documents, or spreadsheets and struggling to extract clear direction from it, this could be a good fit.

The goal of this pilot is to test whether AI can surface meaningful customer and market trends from your existing data. The agent is not yet a self-service product so you will share your data with me, I will run it through the system. But together we will refine the outputs until they reveal the insights you need.

Pilot participants will receive:

• AI-generated analysis of your customer and market data, identifying themes and patterns across fragmented sources

• Collaborative sessions where we review the outputs together and refine the approach for your specific context

• Recommendations showing how to apply these insights to your roadmap

To make sure the pilot of successful, I ask for honest feedback on what works, what does not, and whether this approach surfaces insights you might not have found manually.

If this sounds useful, reach out at info@crwburgess.com with a short description of where your customer data lives today and what decisions you are trying to make with it. I will respond within 48 hours to let you know whether it is a good match for the pilot.

The vision