How One Insights Professional Analyzes Feedback Across 60-70 Music Events

Insights is not handled by a large central team. It sits within one role that helps create the research function, supports event teams, and makes sense of results across a broad portfolio of shows.

Before the process was harmonized, every show could be doing its own thing. That was manageable at the scale of five or ten events, but became difficult across 60-70 events. Add in international surveys, CSV and SPSS files, different languages, and inconsistent headers, and even understanding what each file contained could become a project in itself.

The real problem was not whether the data existed. It was how quickly it could be made usable, comparable, and specific enough to help each event team decide what to improve next.

The search for a new tool did not begin as a broad transformation project. It began with a very immediate need: finding an easy way to convert an SPSS file to CSV.

That small file-format problem exposed a much larger opportunity. Once Luiza saw that AddMaple could also analyze open-ended feedback, the platform became useful for the harder part of the workflow: understanding thousands of free-text responses without manually translating, keyword searching, and copy-pasting chunks into separate tools.

The value was immediate: file flexibility, faster exploration, easier text coding, and a more guided path through messy multilingual data.

AddMaple gave Luiza a single place to explore both the structured and unstructured parts of the survey.

Instead of manually hunting for patterns, Luiza could use dashboards and related statistics to explore which parts of the event experience were most closely associated with happier or unhappier customers. That is especially useful in festival feedback workflows, where overall satisfaction needs to be connected back to operational details like queues, security, food, pricing, toilets, crowd flow, and the wider experience around the music.

The same workflow also made it easier to move from scores into open-ended explanations. Scores alone can be dry. AddMaple helped quantify the topics appearing in written feedback, surface the strongest themes, and keep the consumer voice close through verbatim quotes.

For Luiza, analysis is not just about knowing whether food, prices, or toilets scored well. It is about benchmarking those results against similar events, then using regression and related statistics to decide which three or four priorities will matter most for the next show.

AddMaple supports that process by combining automated heavy lifting with researcher control. The AI copilot can suggest the most important themes, while Luiza can merge similar topics, add specific issues raised by event teams, and check the tagging answer by answer.

The result is a faster route from broad survey results to focused recommendations, with enough transparency for Luiza to feel confident in the analysis she shares with stakeholders.

Multilingual analysis is central to this workflow. The portfolio spans roughly 15 countries, and international visitors often respond in the language where they feel most comfortable. That means English, Dutch, German, Hungarian, French, Nordic languages, and more can all appear across the same research workflow.

Before AddMaple, that meant translating headers, manually translating keywords, and searching for words like "queues" or "toilets" in each language. That approach could miss slang, nuance, or responses written in an unexpected language.

With AddMaple, Luiza can analyze the original written feedback more directly. The platform can detect themes by meaning rather than relying on exact word matches, which is especially useful when respondents use slang or describe an issue without using the expected keyword. That helps more feedback make it into the analysis, even when it arrives in different languages or formats.

Festival surveys often include a deceptively difficult question: which artists do fans want to see next time? People love answering it, but analysis can be painful. Luiza remembered one festival where Billie Eilish appeared in 12 or 13 different spellings.

Previously, cleaning that kind of feedback meant manually coding names, interpreting misspellings, and extracting artist mentions from longer comments. With AddMaple, Luiza can treat the question like open text or convert it into a multiple-choice-style variable, quickly surfacing the most requested artists without months of cleanup.

That changes the role of the question in the research program. Instead of treating artist requests as a slow end-of-season cleanup task, Luiza can use them as a repeatable input into event planning throughout the year. For her team, that means more speed, more certainty, and a clearer view of what audiences want next.