Choosing an AI tool for qualitative and quantitative research work requires looking beyond how quickly it produces an answer. Setup, verification, revisions, and preparing the final deliverable all affect whether a tool actually saves time while protecting market research data quality.
In Research Rockstar’s October 13th 2026 Live Event, Kathryn Korostoff and Jen Floyd explored three examples spanning qualitative analysis, chart creation, and survey banner tables.
Example 1: Combining ChatGPT and DoReveal for an IDI Study
Jen shared a study involving 24, 45-minute in-depth interviews about how consumers choose between debit cards, credit cards, and cash. She used ChatGPT to develop hypotheses and a listening guide, organize the report, and refine slide language. DoReveal supported detailed exploration of the interviews, comparisons across cohorts, and finding supporting quotes and video clips.
Four practical takeaways:
Assign each tool a specific job. Jen used ChatGPT for report structure and concise communication, while DoReveal helped her examine responses and locate supporting evidence. She combined selected outputs in the final slides.
Keep findings connected to their sources. DoReveal’s links to transcripts and videos helped Jen check the evidence behind a finding and retrieve comments she remembered hearing.
Compare defined groups. Tagging participants by characteristics such as age or primary payment method supported comparisons across cohorts.
Verify quotes and their relevance. Jen preferred DoReveal for sourcing quotes because she could trace them to the interviews. Researchers still need to check that a quote supports the interpretation and represents the finding appropriately.
Example 2: Using Claude to Create and Update Research Charts
Jen demonstrated using Claude with Microsoft 365 to work on research slides, including updating charts. Her examples also covered slide text and recommendations, illustrating how an LLM can assist with both production work and communicating findings.
The demonstration highlighted several details that can affect the time saved.
Five practical takeaways:
Provide visual and writing examples. Give Claude chart templates and examples of preferred slide text so it has concrete guidance on the expected output.
Test changes on one slide first. Duplicate the slide, request the changes, and review the result before applying similar edits across the deck.
Check whether chart edits persist. Jen noted that some chart changes could revert when opening from “Edit Data.” Include that check before treating a chart as finished.
Save useful prompts. Jen reported that the in-app experience retained context only within the session. Keeping prompts separately makes successful instructions easier to reuse.
Let the evidence determine the recommendations. A request for, say, "four recommendations with three supporting bullets" each can impose a useful structure but an arbitrary number of items. Jen described using prompts to have Claude consider the number of items and proof points that would be useful without inadvertently encouraging it to overstate the findings.
Jen also flagged usage limits as a workflow consideration. Allow time for interruptions when planning substantial work in the tool; depending on your account type or access level, you may have to break up a larger task into multiple sessions or buy extra credits.
Example 3: Using ChatGPT and Python to Create Banner Tables
Kathryn shared her experience having ChatGPT write and run Python to analyze survey data and export banner tables to Excel. She reported production times of under four minutes for a 300-respondent dataset and under five minutes for an 820-respondent dataset, once the instructions were ready.
Those times came with an important qualification: writing detailed specifications and verifying the results required additional work.
Key takeaways:
Start with clean data and a reliable codebook. Confirm variable labels, response codes, missing values, and routing before generating tables. Remove identifying information and confirm that uploading the data meets client and company requirements.
Specify the analysis rules explicitly. Define banner groups, question bases, percentages, nets, weighting, and statistical tests. Unspecified rules leave room for assumptions that may differ from your intended approach.
Check data for anything "tricky". Filtered questions, multiple-response items, missing answers, and small bases deserve particular attention because errors can be easy to overlook.
Verify results independently. Kathryn compared her ChatGPT output with SPSS tables created from the same raw data. She found that the results, using her final instruction set, matched.
What AI Tools Will You Choose?
These examples illustrate that neither LLMs nor specialty AI research tools are always preferable across research tasks. Their suitability depends on the project’s requirements, existing investments, and team member skills. And ins some cases, combining an LLM with s specialty tool may offer advantages over using either one on its own. Ultimately, tool selection should consider five criteria: total time, total cost, output quality, insight value, and data protection.
