Voice-of-market research is the practice of systematically collecting and synthesising what the market says about a company, a category, or a competitive set, across all the channels where that conversation happens. It goes beyond customer surveys to include competitor reviews, Glassdoor data, expert perspectives, analyst commentary, and direct customer interviews, and it treats these sources not as separate reports but as facets of a single question: how does the market actually perceive this business, and where does that perception diverge from how the business sees itself?
The answer to that question rarely lives in a spreadsheet.
How is voice-of-market different from voice-of-customer?
Voice-of-customer (VoC) research focuses on what your customers say about your product or service. It is inherently inside-looking: you are asking the people who chose you to explain their experience.
Voice-of-market is a wider lens. It captures:
- What people who have not chosen you say (competitor reviews, lost deals)
- What people who are no longer customers say (churn analysis, former customer interviews)
- What people who observe the market say (industry analysts, journalists, expert network calls)
- What employees say (Glassdoor, Indeed, LinkedIn sentiment)
- What the competitive set says about themselves (earnings calls, marketing materials)
Each of these perspectives reveals something different. Your retained customers may love you; your churned customers may tell a completely different story. Your customers may see you as best-in-class in one dimension; competitor review sites may show that the competition is closing the gap. Employees may describe a culture that contradicts your employer brand.
The pattern that emerges when you hold all of these sources together is frequently more strategically important than any single source on its own.
The 6 main sources for voice-of-market research
1. Customer review platforms and app stores
Review platforms (G2, Capterra, Trustpilot, Google Reviews) and app stores are repositories of unsolicited market perception. Unlike survey data, these reviews were written without your involvement: the customer chose to write, chose what to say, and wrote without response options constraining them. That makes them unusually revealing.
For competitive intelligence, they are particularly valuable. You can analyse competitor reviews with the same methods you use for your own, identifying where competitor customers are delighted (product dimensions you need to close the gap on) and where they are frustrated (positioning opportunities). See analysing app store reviews at scale for the analytical approach.
2. Glassdoor and employer review platforms
Glassdoor data is not primarily useful for HR. For market intelligence, it reveals the internal reality of competitor organisations: culture, management effectiveness, strategic direction as experienced by employees, and attrition signals. If a key competitor is seeing a sustained decline in ratings alongside commentary about leadership uncertainty, that is strategic intelligence.
Employer reviews also reflect customer-facing capabilities. Comments about sales team pressure, customer service culture, or product development priorities often predict competitive behaviour before it appears in financial results.
3. Expert network interviews
Expert interviews are the highest signal-density source in voice-of-market research. A 45-minute conversation with a former Chief Revenue Officer of a key competitor, or a current VP of Product at a customer company, can surface strategic intelligence that would take months to assemble from public sources.
The key analytical principle for expert interviews is that the goal is not to get a single expert's opinion; it is to identify where multiple independent experts, from different positions in the value chain, converge or diverge. Convergence across independent sources is evidence. A single expert opinion, however informed, is a perspective. For more on structuring expert interviews for strategic research, see commercial due diligence qualitative analysis.
4. Customer interviews (including former customers and non-customers)
Direct customer interviews remain the richest qualitative source for understanding market perception in depth. For voice-of-market purposes, the most strategically valuable interview groups are often those that standard VoC programmes miss:
Churned customers: Why did they leave? What was the moment the decision crystallised? What did they find elsewhere that they could not find with you?
Competitor customers: What drove their choice? How do they describe your company compared to the one they chose? What would need to change for them to switch?
Non-adopters: Who considered your category but chose neither you nor a competitor? What unmet need is keeping them out of the market entirely?
For a practical guide to running customer discovery interviews across these segments, see customer discovery interviews.
5. Earnings call transcripts and competitive intelligence
What competitors say in earnings calls (and how they say it) is a consistently underused source of market intelligence. Executives describe their growth thesis, their competitive positioning, their product priorities, and their market definition in these calls. Reading them systematically over time shows how the competitive narrative is shifting, which product areas competitors are betting on, and what they are worried about.
Earnings call transcript analysis with AI covers the methodology for processing these at scale.
6. Industry and media coverage
Trade press, analyst reports, conference presentations, and regulatory submissions contain the ambient discourse of a market. Reading this corpus systematically, rather than opportunistically, reveals the framing through which the market is being interpreted: what problems are considered central, what solutions are considered credible, which companies are being cited as leaders or laggards.
The synthesis challenge: holding multiple sources together
Each source in a voice-of-market study has a different format, a different type of content, and a different reliability profile. Customer reviews are abundant but variable in quality. Expert interviews are high-signal but dependent on the specific vantage point of the expert. Glassdoor data is subject to selection bias (who chooses to write a review?). Earnings calls are carefully managed communications, not candid assessments.
Standard synthesis (reading everything and writing a narrative summary) loses this nuance. It averages across sources with very different evidential status. It cannot show, transparently, where the confidence in a conclusion comes from. And it makes it impossible for a reader to challenge the evidence base, because the connection between the sources and the conclusions has been dissolved in the act of summarising.
Structured qualitative analysis preserves these distinctions. By coding each source for the same themes and tagging each excerpt with its source type, you can analyse patterns across sources while keeping the provenance of each piece of evidence visible.
How to build the market picture
Map the sources available
Before collecting, inventory what types of evidence are available for the market in question. Which competitor review platforms have meaningful coverage? Are there accessible expert network opportunities? Does the client have customer interview data or verbatim feedback that has not been systematically analysed? Are competitor earnings calls or regulatory submissions available?
The sources you can access shape the picture you can build. Be explicit about which voices are present in your analysis and which are absent.
Consolidate in a single analytical environment
Bring all the data into one place. This sounds simple, but in practice it means taking expert interview transcripts out of individual folders, customer feedback out of the CRM, competitor reviews out of a browser tab, and Glassdoor extracts out of a spreadsheet, and putting them into a single project where they can be coded and compared.
Skimle accepts all of these formats (including structured CSV exports from review platforms, PDF documents, audio and video transcripts) and allows each document to be tagged with metadata recording its source, date, and type. This metadata is what makes cross-source comparison tractable.
Code for consistent themes across sources
Define the themes you are coding for before you start. For a voice-of-market study, common themes include:
- Product capability perceptions (strengths and weaknesses relative to competition)
- Value and pricing perceptions
- Customer support and service quality
- Cultural and organisational health signals (from Glassdoor)
- Strategic direction signals (from earnings calls, media)
- Switching cost and loyalty drivers
Once the coding scheme is defined, apply it consistently across all source types. A customer's comment about pricing in a review gets the same code as an expert's comment about pricing in an interview. This is what makes cross-source comparison possible.
Use the Data view to spot differences and emerging patterns
The most analytically valuable output of a multi-source voice-of-market study is not the consensus view. It is the pattern of differences: where do sources converge, where do they diverge, and what does that divergence reveal?
Skimle's Data view lets you explore your corpus across source types, time periods, and metadata dimensions interactively. A theme that appears strongly in churned customer interviews but not in current customer interviews is a leading indicator of market dynamics that aggregate metrics would not yet show. A pattern of employee attrition commentary on Glassdoor that correlates with competitor pricing changes in earnings calls may surface a competitive intent before it is visible in market share data.
For academic and business qualitative analysis, metadata analysis across categories is one of the highest-value analytical outputs because it reveals the structure beneath the data, not just its content.
Report with source transparency
The conclusions of a voice-of-market study should be traceable. When you say "competitor X is losing its pricing premium position as customer perception of product quality is declining," that conclusion should link to specific excerpts from competitor reviews, churned customer interviews, and expert commentary that support it.
This traceability is important not just for credibility, but for the ongoing utility of the research. As market conditions change, the ability to go back to the underlying evidence and ask "does this conclusion still hold?" is what separates strategic intelligence from a document that becomes stale the moment it is delivered.
What purely quantitative methods miss
The argument for investing in qualitative voice-of-market research is precisely the argument about what numbers cannot tell you.
Net Promoter Score tells you the direction of customer feeling, not its content. Review star ratings tell you the overall valence, not the specific dimensions being judged. Glassdoor ratings tell you employee satisfaction is declining; they do not tell you whether the driver is compensation, leadership, culture, or strategy.
Qualitative market intelligence fills in what is behind the numbers. Often the most strategically significant findings are patterns that quantitative data would either miss entirely or surface only after they have already become consequential:
- A competitor's product is developing a reputation for a specific capability that your product does not have, visible in early reviews but not yet in market share
- A segment of customers is developing a workaround to your product's limitations that is migrating them toward a competitor's adjacent offering
- A change in the competitive context (regulatory shift, new entrant, technology change) is reshaping what customers consider important before it is visible in retention data
These are the findings that make voice-of-market research worth doing. They require reading the words, not just the numbers. And they require a systematic approach to make sure you are seeing the patterns across a large enough sample to be confident, not just cherry-picking the quotes that fit a hypothesis.
Frequently asked questions
How is voice-of-market different from traditional competitive intelligence?
Traditional competitive intelligence often focuses on structured data: market share, pricing, product features, financial metrics. Voice-of-market research focuses on perception: how customers, employees, experts, and observers talk about and understand the competitive landscape. The two are complementary. Quantitative competitive data tells you what happened; voice-of-market tells you why, and what is likely to happen next.
How many sources do you need for a credible voice-of-market picture?
There is no fixed number, but as a practical starting point: at least two or three independently collected source types (to enable cross-source validation), and enough volume within each source to identify patterns rather than individual opinions. For a market with strong review platform coverage, 200-300 reviews per competitor may be adequate for quantitative pattern recognition; qualitative depth requires 20-50 interviews or a smaller corpus of unusually rich documents.
Can you run voice-of-market research on a market where you are not yet competing?
Yes, and this is one of the most valuable applications. Pre-entry voice-of-market research maps what customers want, what they do not get from existing competitors, and what switching costs and loyalty dynamics look like: precisely the intelligence needed to assess whether a market entry is viable and how to position it.
How do you handle sources that contradict each other?
Contradictory sources are information, not a problem. If customers rate a competitor positively while employees describe a company in crisis, both perspectives are relevant. Customers may not yet be experiencing the internal dysfunction that employees are describing. That gap, between current customer perception and the internal signals that typically precede external change, is often where the most valuable strategic intelligence is found.
Ready to build a complete picture of your market from all available evidence? Try Skimle for free and see how a single analytical workspace brings together customer interviews, competitive reviews, expert perspectives, and market documents into a unified, interactive view.
Related reading: Voice of customer research: a practical guide | Earnings call transcript analysis with AI | Customer sentiment analysis: how to read the mind of customers
About the authors
Henri Schildt is a Professor of Strategy at Aalto University School of Business and co-founder of Skimle. He has published over a dozen peer-reviewed articles using qualitative methods, including work in Academy of Management Journal, Organisation Science, and Strategic Management Journal. His research focuses on organisational strategy, innovation, and qualitative methodology. Google Scholar profile
Olli Salo is a former Partner at McKinsey & Company where he spent 18 years helping clients understand the markets and themselves, develop winning strategies and improve their operating models. He has done over 1000 client interviews and published over 10 articles on McKinsey.com and beyond. LinkedIn profile
