These are a few examples of how I have approached real research problems. I have left out company-sensitive figures, internal product names, and unreleased details.
How I used conjoint, willingness-to-pay analysis, and market simulation when there was no historical price data to work from.
Method: Choice-based conjoint, Hierarchical Bayes, logit modeling, willingness to pay, interaction effects, market simulation, bundle analysis, and price optimization.
How I improved the sample, made the monthly analysis repeatable, and gave teams a better way to work with user sentiment.
Method: Survey design, oversampling, stratified weighting, significance testing, factor analysis, Python, Amplitude, Streamlit, and executive writing.
How I combined search behavior, community overlap, public data, and marketplace evidence when the usual sources came up short.
Method: Google Trends, audience overlap, community analysis, market sizing, secondary research, web-scraped metadata, and language-model-assisted coding.
How I turned scattered audience and genre data into a market-expansion recommendation for Mobalytics.
Method: Audience research, secondary research, demographic and motivation analysis, platform and spending trends, and competitive review.