Watch the full video where our UX team discusses how to utilize playtest insights
It All Comes Down to Analysis
A single playtest can generate a lot of information, be it survey answers, transcripts or hours of gameplay footage. But the raw data alone is useless without analysis.
The evaluation process begins with strict quality controls. Before analyzing qualitative feedback, sessions undergo filtering to ensure player data is valid and uncorrupted.
1) Survey Consistency
Verifying that participants took time to read and answer questions thoroughly, rather than rushing or providing generic AI-generated text.
2) Gameplay Cross-Referencing
Validating survey claims against actual session recordings. If a player notes a specific mechanics issue in a survey, researchers check the video to see what actually occurred.
3) Perception vs. Reality
Distinguishing between how a player felt and how they performed. A player might report that a boss fight felt brutally difficult, but gameplay footage might reveal they defeated it on their second attempt. Combining perception data with physical telemetry ensures design changes address genuine friction rather than misremembered experiences.
How Much Feedback Do You Need?
Sample sizes depend directly on the goals of the study and the specific hypotheses being tested. However, while there’s no universal number, there are some practical guidelines we like to follow:
For qualitative behavioral studies (testing things like FTUE or narrative clarity) 10 to 15 players per profile is typically enough to surface clear patterns. If one player gives an outlier response, you still have enough data to contextualize it without it skewing the whole picture.
For quantitative studies, you need a bigger sample. Somewhere between 150 and 250 responses is a reasonable target, depending on how broad the demographic is.
For multiplayer games, it depends entirely on what you’re testing. A small group of 20 players might be enough to check if the cooperative experience works between a few friends. But if you want to stress test server stability under real load conditions, you need hundreds of players online simultaneously. The question you’re trying to answer drives the number you need.
The most important factor across all of these is recruiting the right profiles. When players give wildly different feedback from each other, it usually means the player pool was too broad or poorly defined. Players with similar profiles tend to surface similar issues – when they don’t, the profile definition is almost always where the problem started.
Spotting Bad Feedback
Not every response that comes through is usable. To protect data integrity, here are some screening mechanisms that we use:
Trap questions: Screener surveys frequently include non-existent game titles or specific attention checks (e.g., “Select ‘Often’ if you are reading this”). Participants who select every title to secure a test are immediately disqualified.
Account verification: Integrating platform accounts like Steam or Xbox allows teams to verify actual play history and hours logged in specific genres before issuing invitations.
In-session validation: Transcripts are monitored during analysis. If a participant bypasses screener filters but admits on camera, “I don’t usually play this type of game“, their session is flagged and replaced.
Prioritizing What Gets Fixed
When a playtest uncovers dozens of design issues, development teams need a structured way to prioritize fixes. UX issues are generally split into three severity tiers:
- High severity (Blocking Issues): Critical obstacles that prevent players from progressing or cause immediate drop-off, such as broken tutorials, confusing onboarding, or major progression blockers.
- Medium severity (friction points): Pain points that frustrate players but do not stop progression entirely, such as cumbersome inventory navigation, unclear UI icons, or restrictive stamina mechanics during exploration.
- Low severity (minor enhancements/preferences): Small cosmetic or subjective requests, such as minor audio adjustments or personal feature wishes.
One thing to keep in mind is that positive findings deserve as much attention as the problems. Knowing what players really enjoy is just as important, because the last thing you want is a future update accidentally fixing something that was never broken.
Another good reminder is that UX researchers aren’t game designers. The data shows where players struggled and why, but how to solve it is still the studio’s call. Sometimes a suggested fix doesn’t fit the game’s direction and that’s completely fine.
The research informs the decision, it doesn’t make it for you.
How Findings Get Communicated
Research findings must be communicated in formats that fit the studio’s production schedule. Depending on the scope and urgency, insights are typically delivered through two main formats:
1) A topline report is a faster turnaround option, a high-level summary of the most notable findings from surveys and platform insights. It works particularly well for prototype testing, where there isn’t enough material to warrant a deep analysis yet or when quick answers are needed to keep development moving. Some studios use it as a first pass after an initial playtest, make changes, then run a second test before commissioning something more detailed.
2) A full UX report goes deeper. Each issue gets its own breakdown with a description, supporting evidence (gameplay timestamps, survey quotes, charts), an impact rating and sometimes a suggested direction for improvement. Every finding ties directly back to data so it’s clear exactly where it came from and why it matters.
One useful approach is treating these two formats as part of an iterative loop rather than standalone deliverables. Run a playtest, get a topline, make changes, run another playtest, then commission the full report that compares both rounds. You can then see what has improved, what stayed the same and what still needs attention.
Studios that work this way tend to get more out of each round of testing because they’re building on what they already know rather than starting from scratch each time.
Key Takeaways
Playtest data is only as valuable as the work that goes into analyzing it. Recurring patterns across players are the signal for what matters. Perception and behavior don’t always match, so you need both survey responses and gameplay footage to get the full picture.
And when it comes to acting on findings, a clear priority system stops studios from trying to fix everything at once and losing focus on what actually moves the needle for players.
