Bespoke qualitative data analytics
The power of qualitative insights.
At scale.
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Qualitative data is messy by nature. It arrives in the form of open-ended survey responses, interview transcripts, focus group notes, or free-text fields that have been sitting in a spreadsheet for months. Most organisations collect it and then aren't sure what to do with it. We are.
Our bespoke qualitative analytics service is built to handle data in whatever form it comes. Whether you have thousands of open-ended employee responses, a set of interview recordings, or a collection of customer feedback that has never been properly interrogated, we apply rigorous, scientifically grounded methodology to surface the patterns, themes, and tensions that matter most to your organisation.
What makes our approach distinctive is the combination of analytical depth and practical output. We don't just produce a list of themes, we examine how themes co-occur, how frequently they appear, how intensely they are expressed, and what the underlying language tells you about how people are really experiencing their work or your brand. Where relevant, we also integrate your qualitative findings with existing quantitative data, so that the two sources of evidence speak to each other rather than sitting in separate reports.
Our work is analytically transparent and methodologically defensible, producing findings you can stand behind in a boardroom. And because we work at scale, processing tens of thousands of responses where needed — volume is not a constraint.
If you already have data collected, we can work with it. If you need help designing and generating the data in the first place, we can do that too.
Schedule a free consultation to discuss your qualitative data analytics needs ➡️
Example insights
Theme occurrence mapping
Available across all service tiers
Like quantitative data, qualitative data contains patterns. Mapping theme co-occurrence can show three things at once: how prominently each theme featured in the data (larger circles mean the theme appeared more frequently), how often certain themes emerge within specific theme groups (e.g., enablers vs constraints), and critically, how often different theme groups emerge together.
Identifying corroborations and tensions within the qualitative data is where the most actionable insights live, and no survey score or structured rating scale alone can surface them. Whether the underlying data comes from employee feedback, client commentary, forum discussions, or open-ended responses of any kind, identifying and mapping dominant themes into meaningful structures can reveal the relational architecture within a dataset in a way that transforms raw text into a navigable map of what matters most, where, and why.
In this example the theme co-occurrence map shows dominant themes related to what employees identify as enabling and constraining organisational effectiveness. This mapping reveals how frequently themes appear, how strongly themes cluster within each group, and where the two groups intersect. Those intersections are often the most revealing finding of all, the places where a strength and a barrier are so consistently raised together that they are clearly two sides of the same organisational challenge.
Response length analyses
Available across all service tiers
The volume of language used to describe a topic is a signal in its own right. Dumbbell charts like this example can show, across any meaningful grouping within a dataset, how much more or less people say about one theme category compared to another, and whether that imbalance is consistent across groups or concentrated in specific ones.
Response length imbalances are rarely random. When one group produces significantly more or less language around a particular theme, that asymmetry reflects salience. Something is weighing on that group, or energising it, in a way that cuts through even when people are not explicitly asked to elaborate. This type of visualisation surfaces those asymmetries clearly and consistently, regardless of whether the underlying data comes from structured surveys, open-ended feedback, interview transcripts, or internal forum comments.
In this example the chart compares how many words employees used when describing organisational effectiveness, broken down by job level. The gap between the effectiveness enablers and constraints on each row shows where people had proportionally more to say about what helps versus what hinders effectiveness. These patterns across levels invite questions that the broader analysis can help answer.
Quantitative-qualitative data integration
Available on Insights and Strategic Intelligence tiers
Integrating qualitative and quantitative data can offer a more complete picture than either can produce alone. A heatmap like this, for example, can use theme categories and rating scales as a framework for organising response length data, revealing patterns that neither quantitative nor qualitative analysis could surface on its own.
In this example the heatmap follows on from the theme co-occurrence map and the response length analysis above, adding a third layer by crossing quantitative survey scores against the effectiveness enabler themes that emerged from the qualitative data. Darker cells indicate higher average word counts. The result is a single integrated view of which organisational effectiveness themes people not only rated most strongly but also felt most compelled to elaborate on, and at which points on the satisfaction scale that elaboration peaks.
Sentiment analyses
Available on Insights and Strategic Intelligence tiers
Sentiment analysis applies validated psycholinguistic frameworks to qualitative text to go beyond simply identifying what themes are present, quantifying the emotional signature of the language used to describe them. This transforms unstructured qualitative data into comparable, statistically testable emotional profiles across any grouping that exists within the data.
In this example the chart applies the six-dimension LIWC framework to employee qualitative feedback, comparing the emotional profiles of enabler-related and constraint-related responses across overall affect, positive emotion, negative emotion, anxiety, frustration, and disengagement. Understanding the emotional texture of a dataset matters because emotional states drive behaviour. How invested, anxious, frustrated, or disengaged a group of people feels shapes how they make decisions, how they collaborate, how they respond to change, and ultimately how they perform. Sentiment analyses make those states visible and measurable, turning what would otherwise remain a subjective impression into evidence that decisions can be based on.
Linguistic analyses
Available on Strategic Intelligence tier only
The specific linguistic choices people make, which pronouns they use, which tenses they write in, whether they use language that signals agency or passivity, are largely unconscious and therefore unusually revealing. Linguistic analysis applies computational methods to detect and measure these features systematically across a body of text, and to track how they shift as a response progresses from opening to close.
In a business context these patterns carry real interpretive weight. Data that contains consistent use of distancing language such as "they" and "them" rather than "we" and "us" can signal something about felt ownership and belonging. Similarly, segments of qualitative data that open in the present tense and drift into the past tense could indicate a form of psychological closure. None of these signals can be captured by asking people to rate something on a scale, and most would not surface even in a careful reading of the data at volume.
In this example the chart maps twelve linguistic features across the full progression of employee responses to a set of open-ended prompts. The colour intensity shows how strongly each feature deviates from its average at each point in the response, revealing not just what linguistic patterns are present in the data but where within a response they tend to emerge or intensify.
Organisational network analyses
Available on Strategic Intelligence tier only
Relationships and connections within any system are rarely visible in raw data. organisational network analysis makes those dynamics visible by mapping the relational structures that exist within a dataset, revealing how different entities connect, influence one another, and cluster in ways that conventional analysis cannot detect.
The resulting map can reveal patterns that other analytical approaches miss entirely. Nodes that are functionally isolated from the rest of the network may have limited impact not because of what they represent but because of where they sit relationally. Nodes that carry a disproportionate share of connections represent a concentration risk. And nodes whose influence extends well beyond what their formal position would suggest often hold more strategic leverage than any structured data source would indicate.
In this example the chart maps sub-departments across five functional areas of an organisation, using qualitative data to identify which clusters are associated with enabling organisational effectiveness and which with constraining it. Two sub-departments appear in blue shading, indicating functional isolation from the broader network. A third, shown in red shading, emerges as a strong enabler but one that is carrying significantly more collaborative weight than is sustainable.
Predictive analytics
Available on Strategic Intelligence tier only
Relationships and connections within any system are rarely visible in raw data. organisational network analysis makes those dynamics visible by mapping the relational structures that exist within a dataset, revealing how different entities connect, influence one another, and cluster in ways that conventional analysis cannot detect.
The resulting map can reveal patterns that other analytical approaches miss entirely. Nodes that are functionally isolated from the rest of the network may have limited impact not because of what they represent but because of where they sit relationally. Nodes that carry a disproportionate share of connections represent a concentration risk. And nodes whose influence extends well beyond what their formal position would suggest often hold more strategic leverage than any structured data source would indicate.
In this example the chart maps sub-departments across five functional areas of an organisation, using qualitative data to identify which clusters are associated with enabling organisational effectiveness and which with constraining it. Two sub-departments appear in blue shading, indicating functional isolation from the broader network. A third, shown in red shading, emerges as a strong enabler but one that is carrying significantly more collaborative weight than is sustainable.