Your Biggest Innovation Opportunity is Hiding in Your Support Tickets

What's a Complaint Analysis Test?

It's not surveys. It's not interviews. It's systematically mining your customer support tickets, calls, and complaints to uncover patterns of unmet needs.

No recruitment. No scheduling. Just examining the data you already have to find the frustrations your customers are experiencing right now.

Most teams focus on asking customers what they want. But they're already telling you through their complaints.

How a bank discovered a £3.2M opportunity in their complaints

A retail banking client was planning a major digital transformation programme. They had a list of 23 potential features and improvements, all based on competitor analysis and internal workshops.

Before prioritising with them, we ran a Complaint Analysis test to see if their roadmap aligned with actual customer pain points. We looked at:

• 6 months of customer support tickets

• Call centre transcripts

• Social media complaints

• App store reviews

The results surprised everyone. While the transformation team was focused on adding features, customers were primarily frustrated with one thing: the time it took to verify transactions flagged as potentially fraudulent.

The data showed:

• 37% of complaints were related to payment delays

• Average resolution time was 26 hours

• Affected customers were 4x more likely to switch banks

The team pivoted to prioritise a real-time verification system. Customer satisfaction jumped 22%, complaints dropped by 41%, and they identified £3.2M in prevented customer churn.

All because they looked at the complaints they already had instead of asking customers what new features they wanted.

How to run a Complaint Analysis Test

To run this test, you'll need:

• Access to customer support data (tickets, calls, emails)

• A clear framework for categorisation

• Input from your support teams

1. Start with the right questions

Define what you want to learn: Are you solving the right problems? Missing key features? Creating unnecessary friction? Focus your analysis on questions that will directly inform your product decisions.

2. Gather the right data

Collect 3-6 months of support data, including:

• Common complaint categories

• Frequency and trends

• Resolution rates and times

• Customer sentiment

• Feature requests

3. Involve your support teams

The people who talk to your customers every day have invaluable insights. Schedule sessions with your support team to understand:

• What patterns they see that might not be obvious in the data

• Which customer types experience which problems

• The workarounds they've developed to help customers

4. Look for patterns, not anecdotes

The most valuable insights come from:

• Recurring issues that affect many customers

• Problems that cause strong negative emotions

• Issues customers contact you about multiple times

• Complaints that lead to cancellations or churn

Common mistakes to avoid:

• Only looking at the most recent complaints

• Focusing on the loudest customers rather than the most common issues

• Treating all complaints as equal in importance

• Jumping to solutions before fully understanding patterns

Try this in the next week

Pull the last 100 customer support tickets your company received. Categorise them by the underlying job, pain point, or unmet need. Identify the top three recurring patterns. Compare these with your current product roadmap priorities.

If there's a mismatch between what you're building and what customers are struggling with, you've just found your biggest innovation opportunity.