To measure creative player quality in mobile UA, you need more than installs and surface-level metrics. Most teams still rely on incomplete post-install signals, making it difficult to understand what kind of players their creatives actually bring into the game. If you can’t measure creative player quality, you can’t truly optimize your UA strategy.
In a privacy-constrained environment, install volume alone tells you very little. Even downstream metrics often lack the context needed to explain who your creatives are actually bringing into the game. User properties in Audiencelab change that.
They extend Audiencelab’s measurement model by adding in-game context to acquired users, so you can move from analyzing installs to understanding player identity.
What are user properties in Audiencelab?
User properties allow you to attach meaningful, game-specific attributes to users after install. Instead of stopping at “which creative drove the install,” you can now answer “which creative drove the right kind of player”.
This could include properties like:
- Faction choice
- Class selection
- Play style
- Social behavior (e.g. guild joining)
- Engagement patterns
These properties give UA teams visibility into the type of audience each creative recruits.
How the measurement model works
At its core, user properties extend your measurement flow:
Creative → Install → User Property → Retention / LTV outcome
This connects three layers that were previously disconnected:
- The promise in the ad
- The identity formed inside the game
- The business outcome (retention, payer rate, LTV)
How user properties differ from custom events
It’s important to understand how user properties differ from custom events. Custom events are sent and analyzed at the creative level, just like installs, IAP, ad revenue, or retention, giving you performance data tied directly to the ad.
User properties work differently. They are attached to users alongside all incoming data, meaning every metric (retention, revenue, engagement) can be broken down through those properties.
In practice, this lets you slice a creative’s performance into smaller, more meaningful segments, where each segment represents a specific player identity. Instead of just seeing how a creative performs overall, you can see how it performs for each type of player it attracts.
What this unlocks for UA teams
- Creative performance becomes more honest
Not all installs are equal, and now you can prove it.
A creative is not “good” because it’s cheap, it’s good if it attracts players who:
- Retain better
- Monetize more
- Behave like your ideal audience
User properties expose the difference.
- You can validate creative hypotheses
Every strong creative is built on a hypothesis:
“This message will attract this type of player.”
User properties let you verify whether that actually happened.
For example:
- Did your “elite commander” ad attract players who choose dominant factions?
- Did your “rare hero” ad attract collectors or just curious installers?
Instead of guessing, now you’re measuring.
- Segmentation moves to the center of UA
Instead of analyzing performance at the campaign level, you can now evaluate:
- Which creatives over-index into high-value player segments
- Which ones attract low-intent or mismatched audiences
- How different audiences behave post-install
This turns your dashboard into a decision-making tool instead of a reporting layer.
- Privacy-constrained UA becomes actionable again
Even when attribution is limited, user properties give you a reliable signal. You may not see everything downstream, but you can see which creatives produce the right player identity and it’s a way to measure creative player quality instead of relying on CPI alone.
That’s often enough to make better decisions, faster.
Example: From creative promise to player reality
Let’s say you’re running a strategy game campaign.
Creative angle:
Power, conquest, faction dominance
What you expect:
Players who:
- Choose a specific faction
- Play aggressively
- Join alliances early
With user properties, you can validate:
- Did users actually choose that faction?
- Did they behave like high-intent strategy players?
- Did they show early signals linked to higher LTV?
This helps you distinguish between:
- A creative that sounds good
- And one that actually recruits valuable players
Why this matters for creative strategy
Audiencelab makes it possible to measure creative player quality at the segment level, not just at the campaign level.
- One creative = one player motivation
Strong creatives focus on a single motivation:
- Power
- Mastery
- Speed
- Status
- Belonging
User properties let you check if that motivation translated into real player behavior.
- Match the player mindset
Whether your creative is framed around:
Aspiration (gain, progression, dominance)
Or avoidance (falling behind, weak builds, missed rewards)
You can now measure if it attracted players in that mindset.
- Reflect the player’s internal monologue
Great ads don’t introduce new ideas, instead they mirror what players already think:
- “I want to dominate early”
- “I want the rare class”
- “I don’t want to waste time”
User properties show whether users actually behaved that way after install.
- Show the mechanism, not just the promise
When creatives demonstrate how something works (e.g. class systems, factions, progression paths), they become more credible.
User properties then make that credibility measurable.
Genre examples
4X strategy games
Creative focus: Power, conquest, control
User properties: faction choice, alliance behavior, play style
Measure whether your “domination” creative actually recruits dominant players.
Hyper-casual games
Creative focus: Speed, flow, instant reward
User properties: session depth, revive usage, engagement loops
Separate shallow installs from players who stick and repeat.
Midcore RPGs
Creative focus: Class fantasy, rarity, social identity
User properties: class choice, rarity preference, guild joining
Understand whether your creative attracts progression-driven players.
The real shift from creative testing to audience engineering
User properties change a fundamental question in UA.
From:
“Which ad got the most installs?”
To:
“Which ad created more of the players we actually want?”
This is a shift from:
Surface-level performance → To segment-level truth
From:
Campaign optimization → To audience design
Final takeaway
Audiencelab’s user properties connect the promise in your creative to the identity formed inside your game.
That turns creative testing into something far more valuable:
A system for understanding, and intentionally shaping, the audience your game grows with.
Want to get started? Let’s talk!
