Explori Blog

Pretty much every post-event survey produces them. Hundreds, sometimes thousands, of verbatim responses to questions like “What could we have done better?” or “What was the most valuable part of your experience?” We often say at Explori that open-text feedback gives you the colour that your quantitative data outlines. For most event teams, this rich data exists, but open-text responses have represented the same operational problem for years: too much volume to read manually and too qualitative to include in the executive summary without someone spending hours doing it by hand into a consistent taxonomy. Often, the result is that open-text data gets collected, glanced at, but largely ignored and fails to enrich the feedback narrative truthfuly. That is not a data problem. It is an analysis infrastructure problem. And it is one that AI is perfectly suited to dealing with. What the old model looked like Until recently, organisations tried to solve this with two mechanics. The first was manual coding: a human reads through responses and assigns each to a best fitting categorisation. Time-consuming, inconsistent at scale, and heavily dependent on whoever is doing the coding that week and their research skillset. The second was fixed thematic frameworks: a set of predetermined categories, adopted to create consistency and scale, and applied across all responses regardless of what respondents actually said. This often produces counts. It does not produce insight. If your respondents spent 40% of their verbatim responses talking about something your fixed framework did not anticipate, that signal disappears entirely. Both approaches shared the same failiure point: the analysis was not shaped by what respondents actually said. To be clear, this was not just a failiure of event teams, this was a widespread limitation of open-text analysis. And even experienced researchers (like myself) wrestled with this for years. What AI thematic and sentiment analysis does differently Explori’s AI open-text analysis generates themes dynamically from the actual content of each event’s responses, rather than applying a fixed category list. That distinction matters more than it might sound. A technology conference, hobbyist show, trade exhibition and internal event will surface completely different themes, and that difference is still present between events of a similar type, not because the questions are fundamentally different, but because the audiences, the format, and the commercial context are different. A fixed taxonomy forces all audiences and contexts into the same box. Dynamic theme generation reflects what each audience actually said. The analysis also layers sentiment within each theme. It is not sufficient to know that “exhibitor onboarding” is a recurring topic. You need to know whether respondents are raising it as a frustration or a strength. Sentiment at the theme level (positive, negative, neutral) gives decision-makers directional intelligence to act on, not just a list of things people mentioned. Analysis can be triggered manually during fieldwork for early signal, and regenerates automatically when the survey closes, so the final insights reflect the complete response set rather than a midpoint snapshot. What this means for the decision being made The point of asking open-text questions is to understand the rich and varied experiences and feelings of your audience. But only selecting a handful of comments to digest is like ripping random pages from a book and hoping you understand the story. However, reading every comment is time-consuming. Skipping most of them means missing the story. The use case for AI open-text analysis is not “read your open-text responses faster.” That undersells it. It's that you don't have to choose between tradeoffs, you don’t need to read every comment but you can understand the full story. When a stakeholder asks what exhibitors thought of the onboarding process for your flagship event. Previously, the honest answer was “I've seen some of the comments but I have not had time to get the full picture.” Now the answer is a dynamic thematic and sentiment summary of every response on that topic, available inside the platform, filterable by theme, with the underlying comments accessible for any that need scrutiny. That is the difference between qualitative data that decorates a report and qualitative data that supports a decision. A note on the transition from fixed coding Explori’s legacy fixed thematic code frames are being phased out as part of this release. For clients who have used fixed coding historically, here is what to expect. From launch, a four-week transition window during which legacy AI coding can still be added to new surveys. After four weeks, legacy AI coding will no longer be available to add to new surveys. By end of August 2026, full removal of legacy fixed coding, including for surveys copied from previous years. Existing surveys, historical data, and results already collected are unaffected during the transition period. If you have specific benchmarking or year-on-year comparison needs that relied on fixed coding categories, speak to your account manager before the transition window closes. Where to access it AI open-text analysis is available now within the Reports area of any eligible survey. Navigate to an open-text question and select Generate analysis to trigger it. Analysis regenerates automatically when your survey closes. It can be disabled at account, survey, or individual question level. You retain full control over where it runs. If you have questions about what this means for your existing surveys, your account manager is the right starting point. The bottom line The volume of qualitative data that events generate has always outpaced the capacity to analyse it properly. AI thematic and sentiment analysis does not replace the human judgment needed to act on insight, but it removes the bottleneck that has kept most of that data from being analysed at all. For organisations that take event intelligence seriously, that is a meaningful shift. For a concrete example of how consistent event measurement feeds strategic planning decisions, read how IMEX turned data into a strategic planning engine. Frequently asked questions What is AI open-text analysis in Explori? AI open-text analysis is a capability within the Explori platform that automatically identifies themes and sentiment from open-text survey responses. Rather than applying a fixed category list, it generates themes dynamically from the actual content of each event’s responses, so the output reflects what respondents said rather than what the analyst anticipated. How is dynamic theme generation different from fixed coding? Fixed coding applies a predetermined set of categories to all responses, regardless of what respondents actually wrote. If a theme emerges that is not in the fixed framework, it does not appear in the output. Dynamic theme generation reads the actual content of responses and identifies the themes that are present in that specific dataset, which means unusual or unexpected topics surface rather than being absorbed into a catch-all category. What sentiment analysis does the platform provide? Each question analysed by AI open-text analysis includes a sentiment breakdown showing the proportion of responses that are positive, negative, or neutral. This gives decision-makers directional intelligence on whether a topic is a strength or a concern, not just confirmation that it was mentioned. When does analysis run and how current is it? Analysis can be triggered manually at any point during fieldwork to get an early read on themes as responses come in. It regenerates automatically when the survey closes, ensuring the final output reflects the complete response set rather than a snapshot from midway through data collection. Can I disable AI open-text analysis if I do not want it? Yes. AI open-text analysis can be disabled at account level, survey level, or individual question level. You retain full control over where the analysis runs. What is happening to Explori’s legacy fixed AI coding? Explori’s legacy fixed thematic code frames are being phased out in three stages from June 2026. From launch, there is a four-week window during which legacy coding can still be added to new surveys. After that window, legacy coding will no longer be available for new surveys. By end of August 2026, legacy coding will be fully removed, including support for surveys copied from previous years. Existing surveys and historical data are unaffected during the transition. Does AI open-text analysis work across different languages? Speak to your account manager about language support for your specific survey programme. Language capability is an important consideration for organisations running multi-territory events where respondents write in different languages. How does AI open-text analysis fit into Explori’s broader intelligence platform? AI open-text analysis is one part of a broader platform investment in reducing the distance between event data and the moment someone needs to act on it. It works alongside quantitative measurement, benchmarking, and dashboard tools to give event teams a complete intelligence picture, not just a set of scores.