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How To Market A Game
This method combines open-ended interviews with direct observation. It mitigates bias often found in self-reported data, allowing for observation of subconscious habits.
Authentic Behavior. Researchers should use contextual inquiry to capture authentic user behavior, avoiding self-reported data bias.
An independent game developer with seven years of UX design experience outlines the methodology behind a qualitative study of how Steam users discover, wishlist, and buy games. Motivated by uncertainty over what customers want to see on a store page, the author set out to gather the kind of public qualitative data on Steam buying habits that is largely absent, replacing untested developer theories with direct observation.
The study used a contextual inquiry, combining open-ended one-on-one interviews with observation of participants completing tasks. Sessions ran about 30 minutes, were conducted remotely via Discord screen share and recorded with OBS, and used participants' own gaming PCs. The protocol moved from how and why people access Steam, to what they check first, to a task of wishlisting two games they were genuinely interested in, and finally to the experimental Steam Labs features, which not every participant had time to reach. Participants were never asked to buy a game, since most wishlist first, purchases cluster around seasonal sales, and compelling a luxury purchase or gifting spending money would be ethically questionable or distort behavior.
Recruitment targeted the roughly top 1% of Steam users by library size, defined as owning 107 or more games, on the reasoning that they are the likely buyers of niche indie titles. Participants had to be 18 or older, and the author sought diversity in gender, country, and background. Candidates were found through direct messages to commenters on Reddit, Steam reviewers with large libraries, and a local Discord, then screened with a Google survey. Participants received a $25 honorarium and signed a consent form, and the author concealed his own status as a Steam developer to avoid bias.
Analysis was inductive. The author transcribed relevant moments from recordings and grouped observations into emergent categories such as the Discovery Queue, reviews, wishlisting behavior, and screenshots, which structured the resulting findings article. Plans for future inquiries during a seasonal sale were noted.