Meg Gerli

Research Impact Lead

Conveo

Meg Gerli is Research Impact Lead at Conveo, where she works with insights teams to make qualitative research faster and more scalable without losing depth. She joined in 2026 after six and a half years at Nike, where she worked in global women's insights across the Training and NikeSKIMS businesses, turning consumer understanding into product, brand and strategy decisions. She is a Stanford graduate and is based in San Francisco.

Sessions

Oct 27

1:50 PM - 2:15 PM PDT

Red Stage

AI-Augmented Research Methods

Human-AI Collaboration

Qualitative Depth at Scale

DEMO: MaxDiff and Pricing Studies That Ask Why

Following the fireside chat with Netflix, we’ll run the methods live in Conveo. See MaxDiff, Gabor-Granger and Van Westendorp inside AI-moderated video interviews: participants make the same forced trade-offs and price judgments, then explain them in their own words. We’ll go from study setup to live interviews to results, showing rankings and price thresholds with the reasoning attached.

Key Takeaways:

1. How to set up MaxDiff and pricing methods inside an AI-moderated video interview

2. What the output looks like: rankings and price thresholds with the reasoning attached

3. How AI follow-ups probe each trade-off without breaking the method’s rigor

Also speaking

Charles Allison

Insights Lead, Conveo

Speaker

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