Meet Djamila, Head of Consumer Insights at a Major FMCG Manufacturer

Djamila has spent fifteen years in qualitative research. She knows how to design a study, how to read a room in a focus group, and how to turn consumer language into strategic clarity. That expertise is irreplaceable.

What she doesn't need to spend her time on is transcribing audio files, manually coding hundreds of verbatim quotes, and reformatting findings into a deck that her internal clients will read once — and never come back to.

Her team runs four to six qualitative studies a year. Demand from the business is growing. Headcount isn't.

The Challenge

Djamila leads a small, highly capable insights team. The bottleneck wasn't talent — it was process. For every week spent collecting data in the field, two more were lost to analysis and reporting.

Three things were holding the team back:

Analysis consumed everything. Transcription, coding, thematic synthesis, report writing — the mechanical work that followed every study could take three weeks before findings reached stakeholders.

Reports had a shelf life of one meeting. Stakeholders received a deck, attended a debrief, defined next steps — and the research quietly disappeared into a shared drive. When a new question arose six months later, nobody went back to the data. They commissioned a new study.

Capacity was the ceiling. Djamila's team wanted to do more, faster. But compressing a 10-week qual cycle isn't possible when 60% of the time is spent in post-field administration rather than analysis and interpretation.

The Approach: Insight Ops & AI Enablement

Djamila didn't need someone to do the research for her. She needed her existing process to run at a different speed — and her outputs to stay alive after delivery.

data IQ embedded its AI workflow directly into her team's existing methodology.

The integration didn't change how Djamila's team designs studies, recruits participants, or runs fieldwork. It changed what happens after the last interview ends.

Audio files are processed and transcribed automatically, with quality checks built in. Thematic coding and synthesis run at AI speed, structured by the research objectives Djamila defines. Her senior researchers review, refine and validate the output — the AI accelerates; the humans decide. The whole analysis phase, from raw audio to validated synthesis, now takes days rather than weeks.

And the final deliverable is no longer a slide deck.

What Her Clients Now Receive

Every study Djamila's team delivers comes with a living, interactive report — built on the anonymised consumer data from the project.

Stakeholders don't just read the findings once. They can query the report directly: "What do under-35s say about the new format?""How do Swiss German and French speakers differ on this claim?" The research keeps answering questions long after the debrief is over.

Swiss data privacy is fully preserved throughout. Consumer data is processed within a Swiss-compliant AI environment, meeting the requirements of Djamila's regulated category without compromise.

Case Study: Concept Research for a New Chocolate Product

A major FMCG manufacturer wanted early-stage consumer input on a new chocolate treat before going into development. Djamila's team ran 24 in-depth interviews across three Swiss regions and two languages.

Before: Transcription and initial coding alone would have taken two weeks. The full analysis and report another two. By the time findings reached the innovation team, six weeks had passed. The client received a PDF and a one-hour Zoom call.

After: With data IQ's workflow embedded, transcription and thematic synthesis were complete within 48 hours of the final interview. Djamila's team spent their time where it counts — validating themes, drawing strategic conclusions, and pressure-testing findings against the research objectives. The client received the validated synthesis in eight days.

More importantly, the client received it as a living report. Three months later, when the product brief evolved and a new claim needed testing, the innovation team put the question directly to the original consumer data. No new fieldwork. No new wait.

The Result

"The AI doesn't replace what we do — it removes the parts that were slowing us down. My team is doing more studies, going deeper on each one, and our clients are actually using the research past the debrief. That's a first."

In the first year, Djamila's team increased their throughput from five studies to eight — with the same headcount. Average time from final interview to client delivery dropped from six weeks to ten days.

More studies. Deeper insight. Research that doesn't die in a folder.

data IQ works with mature insights teams to embed AI-assisted workflows into existing qualitative processes — compressing analysis time by 60–70% while preserving the methodological rigour that senior researchers won't compromise on. Every engagement delivers a living interactive report your stakeholders can keep asking questions of.

[Talk to us about adding AI capacity to your insights function →]