From Interviews to Impact: Turning Qualitative Research into Business Strategy
WRITTEN By Fluvio Consultant, Lyle Burns
Customer interviews are one of the most powerful tools in a product marketer’s arsenal, but only if your product marketers know how to extract usable insights. Too often, interviews become a file of notes instead of a source of strategic direction. At Fluvio, conducting market and voice of customer research is often at the core of our engagements. These interviews inform:
Messaging and positioning frameworks
Buyer personas and Ideal Customer Profiles
Market entry strategy
Go-to-market infrastructure
Product strategy
Sales process and enablement
To gather the data needed for these projects, you must set yourself up for success with proper pre-work for each interview. However, the true value comes from what happens after the interview: making sense of what we heard.
What is qualitative analysis?
According to the LP Wong, MSc and PhD in the Faculty of Medicine at University of Malaya, qualitative data analysis “involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence.”
Insights are more than observations
Many product marketers struggle after completing a successful set of interviews. Often, notes or even review of the interview transcripts leave product marketers simply pulling quotes and making summaries of what’s said or observed. What they should be aiming for is transforming those quotes and observations into insights that unlock business opportunities.
For example, for a health insurance software company, an observation might be: “Insurers are exiting service in this segment due to rules and costs.”
The insight? To effectively serve this segment, insurers need a solution that reduces servicing costs at scale and is flexible enough to accommodate special rules and regulatory requirements.
From there, it's important to tell a compelling, cohesive story that connects with the audience and makes the information easy to consume.
Deriving insights requires a process
To find insights, you can’t be biased. So, don’t immediately prioritize proving or disproving the hypotheses set in your prospectus. Instead, as you review interviews, notes, and transcripts look for themes and patterns across the data. You likely began noticing themes during the interviews, but reviewing transcripts can surface broader categories and unexpected connections.
As categories and connections form, consider what business problems or opportunities relate to the patterns and themes you’re finding. Go beyond identifying patterns and themes. Look for the relationships between them. Assess who you’re interviewing in terms of role, company size, state of their business, immediate goals, and their top challenges. You will begin to see if there are patterns within the segments. Identify consistency across segments and where differences lie across segments. Insights are not one-size-fits-all, the context matters.
With context, you can start to infer the “why” behind what is happening or what customers are doing and then understand the trends, rather than just what customers are telling you they’re doing. And with that, observations and anecdotes transform into actionable insights. These enable your team to make more relevant and informed business decisions and even find potential areas of differentiation.
Can AI help?
Can AI be used to help process qualitative data and uncover insights? The answer: yes, but with caution. Based on our experience, here’s what to keep in mind:
Start with human context. Before using AI, get familiar with the data yourself. Take an initial pass to identify themes and patterns manually. This grounds your perspective.
Use AI to jumpstart pattern recognition. Seeding your AI tool with context from pre-work (like research goals or hypotheses) helps it tag relevant themes more quickly.
Don’t outsource insight-making. AI lacks the nuanced judgment to infer why something matters or assess stakeholder goals. You still need to draw strategic connections.
Be mindful of errors, including:
Misattributed quotes
Hallucinated external references
Misunderstood context or tone. These can easily go unnoticed if you’re not deeply familiar with the source material.
Use AI as a co-pilot, not a replacement. Let it assist with categorization and early analysis, but keep human expertise at the center of insight development.
Storytelling makes insights actionable
Finally, putting it all together in a strong narrative that accurately tells the story and speaks to the priorities set in your pre-work with stakeholders is the final step in processing these learnings and insights. When crafting your narrative the information is curated and edited down to speak to the relevant goals and needs of the stakeholders you’re presenting to.
This editing and storytelling process allows you to take another look at the data with a new perspective to determine which quotes, learnings, and insight matter and which hypotheses have been proven or disproven. The goal here is to make sure these insights and takeaways are presented in a way that resonates, sticks, and influences action to help the organization and your customers.
Final Thoughts
Qualitative data can have a major impact for product marketers in informing projects and creating influence with the organization. Customer and market research should be conducted frequently. However, this requires product marketers to be comfortable and effective when diving into qualitative data and deriving insights.
Product marketers who master this process don’t just gather research; they shape strategy. By bringing the voice of the customer into every decision, they become indispensable partners across sales, product, and leadership.