The Mobile Gaming Metrics Bible

The Mobile Gaming Metrics Bible

Picture your first week at a mobile game studio. You walk into a product review, coffee in hand, notebook ready. The PM opens the deck and says: "Our DAU is up 12% but ARPDAU dropped โ€” we need to fix ROAS before we scale UA, and churn is eating our LTV projections."

If that sentence made you want to quietly Google every third word under the table... welcome. You're in exactly the right place.

Mobile gaming runs on metrics. They're the language of every morning standup, every investor pitch, every post-mortem. This guide covers 20 of the most important ones โ€” what they mean, exactly how to calculate them, real-world examples with actual numbers, and the benchmarks that tell you if your results are actually worth celebrating.

No fluff. Let's get into it.

Jump to any metric
01 โ€” Engagement Metrics

How much people actually love your game

These tell you if your game has a heartbeat โ€” who's showing up, how often, and for how long.

DAU
Daily Active Users
Engagement

"The number everyone asks about in every Monday morning standup. Forever."

DAU is the count of unique users who opened or played your game on a given day. Not sessions โ€” unique people. Someone who plays 10 matches still counts as 1 DAU. It's the heartbeat metric: when it's rising, something's working; when it dips, something went wrong.

Most studios track DAU daily and watch for weekly patterns. Weekends typically spike, Monday dips are normal, and a sudden midweek crash usually means you pushed a bad build or the servers had a rough morning. Always look at the trend line, not just today's number.

Formula
DAU = Count of unique users with โ‰ฅ 1 session on a given day // "Active" = any session start. Always de-duplicate. // 10 sessions by 1 person still = 1 DAU.
Real Example
On July 15th, 73,000 unique accounts opened your game. Some played one quick match; some played for 4 hours straight. Doesn't matter โ€” your DAU = 73,000.
๐Ÿ’ก
Pro tip: DAU alone is almost meaningless without context. A game with 50K DAU growing 10% week-over-week is infinitely healthier than one with 200K DAU declining 15% weekly. Always show the trend, not just the number.
MAU
Monthly Active Users
Engagement

"The number that looks impressive in a pitch deck. Just make sure your DAU/MAU ratio backs it up."

MAU counts unique users who played at least once in the past 30 days. It's a broader, smoother signal than DAU โ€” less volatile, but also slower to react to problems. A game with a million MAU sounds huge, but if those users only open the game once a month to collect a login bonus, it means something very different than a game where 80% of its MAU are showing up daily.

MAU is best used alongside DAU (see Stickiness below) rather than alone. As a standalone number, it's often more of a marketing talking point than an operational signal.

Formula
MAU = Count of unique users with โ‰ฅ 1 session in the last 30 days // Or within a calendar month, depending on your analytics setup.
Real Example
In June, 620,000 unique players opened your game at least once. Some played every single day; some only logged in once for a limited event reward. Either way: MAU = 620,000.
WAU
Weekly Active Users
Engagement

"The middle child of active-user metrics โ€” less noisy than DAU, more responsive than MAU."

WAU counts unique users who played at least once in the last 7 days. It sits between DAU and MAU and is particularly useful for games built around weekly content cycles โ€” weekly tournaments, 7-day login events, weekly PvP seasons.

For many casual games, WAU is actually the most actionable engagement signal. It smooths out the day-to-day volatility of DAU without hiding real trends the way a 30-day MAU window sometimes does.

Formula
WAU = Count of unique users with โ‰ฅ 1 session in the last 7 days
Real Example
From July 1โ€“7, 180,000 unique users played your game at least once. Some played every day; some just popped in for the Tuesday event. WAU = 180,000.
DAU/MAU
Stickiness Ratio
Engagement

"The metric that tells you if your users love your game โ€” or just downloaded it once and forgot about it."

Stickiness is DAU divided by MAU, expressed as a percentage. It answers: "Of all the people who played this month, what share comes back every single day?" High stickiness signals habitual engagement. Low stickiness means you have a large but occasional audience.

WhatsApp sits above 70% stickiness because people check it constantly. Most casual games land between 10โ€“25%. Neither is inherently "bad" โ€” it depends entirely on genre. A bite-sized puzzle game people pick up every morning for 5 minutes has different expectations than a narrative RPG someone deep-dives into on weekends.

Formula
Stickiness = (DAU รท MAU) ร— 100 // Example: DAU = 60,000 | MAU = 400,000 Stickiness = (60,000 รท 400,000) ร— 100 = 15%
Real Example
60,000 DAU and 400,000 MAU โ†’ Stickiness = 15%. That means on an average day, roughly 1 in every 7 monthly users is playing. For a mid-core strategy game, that's solid. For a hyper-casual title, you'd want to push closer to 20โ€“25%.
10โ€“15%Typical casual
15โ€“25%Good mid-core
30%+Excellent
D1 / D7 / D30
Day-N Retention Rate
Engagement

"The most brutally honest feedback your game will ever receive."

Retention tracks what percentage of users who installed on Day 0 came back on a specific day afterward. D1 is the next day. D7 is a week later. D30 is a month later. These numbers cascade hard โ€” if your D1 is terrible, your D7 and D30 will be even more devastating.

This is the single most important signal for game health. No amount of marketing spend compensates for a game that people don't return to. Low D1 usually means the onboarding experience isn't landing. Low D7 often means the core loop doesn't have enough depth to sustain interest past the first week.

Formula
D1 Retention = (Users who returned on Day 1 รท Day 0 installs) ร— 100 D7 Retention = (Users who returned on Day 7 รท Day 0 installs) ร— 100 D30 Retention = (Users who returned on Day 30 รท Day 0 installs) ร— 100 // Example: 10,000 installs on Day 0 // 4,100 returned on Day 1 โ†’ D1 = 41% โœ“ healthy // 2,000 returned on Day 7 โ†’ D7 = 20% โœ“ healthy // 950 returned on Day 30 โ†’ D30 = 9.5% โœ“ healthy
Real Example
10,000 users install your game on Monday. On Tuesday, 4,100 play again โ†’ D1 = 41%. The following Monday, 2,000 of the original cohort are still playing โ†’ D7 = 20%. Hit both of those and you're tracking well above industry average.
D1Good: 35โ€“45%
D7Good: 15โ€“25%
D30Good: 8โ€“12%
ASL
Average Session Length
Engagement

"Are people actually playing your game, or just opening it, blinking, and leaving?"

Average Session Length is how long users spend per play session โ€” from launching the game to closing it. It's a quality-of-engagement metric. Short sessions in a puzzle game are by design. Short sessions in an open-world RPG are a problem.

Always interpret ASL in the context of your genre and intended play pattern. For hyper-casual games, 3โ€“5 minute sessions are completely normal and healthy. For a mid-core strategy game, 20โ€“40 minutes is more appropriate. And if your ASL is unusually long (say, 4+ hours), double-check your session timeout logic โ€” the game might not be ending sessions when users minimize it.

Formula
ASL = Total Session Time (all users, all sessions) รท Total Number of Sessions // Example: 8,500 sessions yesterday, totaling 425,000 minutes ASL = 425,000 รท 8,500 = 50 minutes per session
Real Example
Yesterday: 8,500 sessions totaling 425,000 minutes. ASL = 50 minutes. For a strategy RPG, that's excellent. For a hyper-casual runner? That number seems suspicious. Worth checking the session-tracking code.
02 โ€” Revenue Metrics

The numbers that keep the lights on

How much money is your game making, how efficiently, and what is each user actually worth over their lifetime?

ARPU
Average Revenue Per User
Revenue

"A bird's-eye view of monetization. Useful for benchmarking, but too blunt for decision-making."

ARPU divides your total revenue by your total users over a given period. It's a broad overview metric that's great for comparing across games or tracking high-level monetization trends, but it hides a lot of nuance underneath. The average is dragged down heavily by the 95%+ of free-to-play users who never spend anything, and boosted by the small number of big spenders.

Think of ARPU as a first approximation. When someone says "our ARPU is $0.50," what they're really saying is that 2% of users spend $25 on average, and 98% spend $0. For anything more nuanced, look at ARPPU.

Formula
ARPU = Total Revenue รท Total Active Users (in a period) // Example: $80,000 revenue in June | 400,000 MAU ARPU = $80,000 รท 400,000 = $0.20 per user
Real Example
June revenue: $80,000. MAU: 400,000. ARPU = $0.20. Reasonable for a casual F2P game. Top-performing mid-core games often reach $1โ€“$3 monthly ARPU. Gacha RPGs can push $5+.
ARPDAU
Average Revenue Per Daily Active User
Revenue

"This one separates games that look impressive from games that actually make real money."

ARPDAU is your daily revenue divided by that day's DAU. It tells you exactly how much revenue each active daily player generates, on average, every day they play. Numbers typically range from $0.02 to $0.50 depending on genre, but small changes in ARPDAU multiplied across millions of sessions compound into enormous revenue differences.

The sneaky trap: DAU going up while ARPDAU goes down. This often means your growth campaigns are bringing in lower-quality, non-spending users. You're getting bigger audiences, but your revenue per person is shrinking. More users, same revenue, higher server costs. Not the direction you want to be heading.

Formula
ARPDAU = Total Revenue for the Day รท DAU for that Day // Example: $6,500 revenue on Tuesday | 65,000 DAU ARPDAU = $6,500 รท 65,000 = $0.10 per daily user // Annualized: $0.10 ร— 365 = $36.50 if user stays a full year
Real Example
Tuesday: 65,000 DAU, $6,500 revenue. ARPDAU = $0.10. Multiply by 365: a user who sticks around a full year is theoretically worth ~$36.50 in revenue.
$0.01โ€“$0.05Hyper-casual
$0.05โ€“$0.15Casual
$0.15โ€“$0.50Mid-core
$0.50+Hardcore/Gacha
ARPPU
Average Revenue Per Paying User
Revenue

"ARPU looks at everyone. ARPPU only looks at the people who actually opened their wallets."

ARPPU ignores your free players completely and zooms in only on paying users. It answers: "When someone does decide to spend money in my game, how much do they spend on average?" This is crucial for understanding monetization depth โ€” are you relying on many light spenders or a smaller group of heavy spenders?

Here's a useful relationship to remember: ARPPU = ARPU รท Paying User Rate. A game with 2% paying rate and $50 ARPPU has the same revenue per user as one with 5% paying rate and $20 ARPPU โ€” but the monetization philosophy and design strategies are completely different.

Formula
ARPPU = Total Revenue รท Number of Paying Users (in a period) // Example: $100,000 revenue in July | 2,500 paying users ARPPU = $100,000 รท 2,500 = $40.00 per paying user // Shortcut: ARPPU = ARPU รท Paying User Rate
Real Example
July: $100,000 revenue from 2,500 paying users. ARPPU = $40. Typical for mid-core. Compare that to a hardcore gacha RPG where ARPPU can hit $200โ€“$500+ because a handful of whales are driving the vast majority of revenue.
LTV
Lifetime Value (also LCV)
Revenue

"The most important number in mobile gaming โ€” and the one everyone argues about the most."

LTV (Lifetime Value) is the total revenue a single user generates from install to the day they stop playing for good. It's the bedrock of your entire business model. If your LTV is greater than your CPI (what you pay to acquire a user), you're profitable. If it's lower, you're paying to slowly go out of business.

The catch: you can't wait 12 months to measure LTV for a new cohort โ€” you need to act now. So studios use prediction models or the simplified formula below to estimate it early. Most use D30 or D90 cohort data extrapolated based on historical retention curves.

Formula (Simplified)
LTV = ARPDAU ร— Average User Lifespan (in days) // Example: ARPDAU = $0.10 | Avg. user plays for 75 days LTV = $0.10 ร— 75 = $7.50 // Alternative (monthly): LTV โ‰ˆ Monthly ARPU รท Monthly Churn Rate // Example: $0.30 ARPU | 10% monthly churn โ†’ LTV โ‰ˆ $3.00
Real Example
ARPDAU = $0.10. Historical cohort analysis shows users play for an average of 75 days. LTV โ‰ˆ $7.50. If your CPI is $3.00, great โ€” you're profitable. If CPI is $10.00... that's a very stressful spreadsheet to look at.
โš ๏ธ
Critical caveat: LTV is always a prediction, not a fact. When someone shows you an LTV number, immediately ask: "Predicted or measured? Over what time window?" A D30 LTV and a D365 LTV for the same game can differ by 5โ€“10x.
03 โ€” Acquisition Metrics

The cost of finding your players

How much are you spending to bring users in, and is that spend actually returning value?

CPI
Cost Per Install
Acquisition

"How much did you pay to get one person to download your game? Just one."

CPI is the most common user acquisition (UA) metric โ€” your total ad spend divided by total installs generated from that spend. It's the first number any UA manager looks at when evaluating a campaign.

CPI varies dramatically by genre, platform, geography, and creative quality. A hyper-casual game targeting broad audiences might achieve a $0.50 CPI. A hardcore strategy RPG targeting competitive gamers might run $15โ€“$25 CPI. Neither is "good" or "bad" in isolation โ€” what matters is whether your LTV justifies the cost. CPI is always just one half of the equation.

Formula
CPI = Total Ad Spend รท Total Installs Generated // Example: $25,000 ad spend โ†’ 8,000 installs CPI = $25,000 รท 8,000 = $3.13 per install
Real Example
Last week's Meta Ads campaign: $25,000 spent, 8,000 installs. CPI = $3.13. Compare to LTV: if LTV is $7.50, you're clearly profitable. If LTV is $2.50, you need to either lower CPI or urgently improve your monetization.
$0.30โ€“$1Hyper-casual
$1โ€“$5Casual/Puzzle
$5โ€“$25+Mid-core/RPG
CPA
Cost Per Action / Acquisition
Acquisition

"CPI is the cost of a download. CPA is the cost of something that actually matters."

CPA measures what you spend to get a user to complete a specific valuable action โ€” most commonly making their first purchase, reaching a target level, or completing the tutorial. The action you define should correlate strongly to long-term value, which is why "first purchase" is the most popular CPA target in mobile gaming.

Studios that run campaigns optimized for CPA (rather than raw CPI) consistently see better ROI. You pay more per event โ€” but you're paying for users who actually do something valuable.

Formula
CPA = Total Ad Spend รท Number of Target Actions Completed // Example: $25,000 spend | 400 first-time purchases CPA = $25,000 รท 400 = $62.50 per paying user acquired
Real Example
Same $25,000 campaign (8,000 installs, CPI = $3.13). Only 400 users made a purchase. CPA = $62.50. If your ARPPU is $90+, this campaign was profitable. If ARPPU is $30, it wasn't.
CTR
Click-Through Rate
Acquisition

"How many people saw your ad and actually cared enough to tap on it."

CTR measures the percentage of people who saw your ad and clicked. It's your primary signal for creative quality and audience targeting โ€” a high CTR means your ad caught people's attention; a low CTR means it's wallpaper that people scroll past without a second thought.

Formula
CTR = (Clicks รท Impressions) ร— 100 // Example: 3,200 clicks | 200,000 impressions CTR = (3,200 รท 200,000) ร— 100 = 1.6%
Real Example
Your playable ad ran 200,000 times and received 3,200 taps. CTR = 1.6%. That's solid for mobile video or playable formats (industry average is roughly 1โ€“2%). Banner and static ads often sit at 0.1โ€“0.5%.
CVR
Conversion Rate
Acquisition

"Getting people to tap your ad is one thing. Getting them to install โ€” then pay โ€” is something else entirely."

CVR shows up in two contexts in mobile gaming. Ad-to-Install CVR tracks how many clicks turn into actual downloads โ€” this is mostly a function of your app store page. Install-to-Pay CVR tracks how many of all your users eventually make a purchase โ€” this is a measure of your monetization design.

Formula
// Type 1: Ad Click to Install Ad CVR = (Installs รท Ad Clicks) ร— 100 // Example: 8,000 installs from 40,000 clicks โ†’ Ad CVR = 20% // Type 2: Install to First Purchase (Payer Conversion) Pay CVR = (Paying Users รท Total Installs) ร— 100 // Example: 400 paying users from 10,000 installs โ†’ Pay CVR = 4%
Real Example
40,000 ad clicks โ†’ 8,000 installs: Ad CVR = 20%. Of those 8,000 users, 320 made a purchase: Pay CVR = 4%. Industry average F2P Pay CVR is 1โ€“5%. Above 5% means your monetization design is genuinely strong.
ROAS
Return on Ad Spend
Acquisition

"The simplest possible question in UA: did you make more money than you spent?"

ROAS divides the revenue generated from a campaign by what you spent running it. 100% = breakeven. Above 100% = profitable. Below 100% = you're paying to grow, betting that long-term LTV will make up the difference.

Always specify the measurement window. "D30 ROAS of 60%" often means "we're deliberately losing money now, betting on a 6-month payback period." That can be a rational strategy โ€” but only if your LTV model backs it up.

Formula
ROAS = (Revenue Generated from Campaign รท Ad Spend) ร— 100 // Example: $30,000 revenue | $15,000 ad spend ROAS = ($30,000 รท $15,000) ร— 100 = 200% โ† also written as "2x"
Real Example
You spent $15,000 on a campaign. Over the next 30 days, those users generated $30,000 in revenue. D30 ROAS = 200%. Excellent โ€” you doubled your money in a month. 150%+ at D30 is generally healthy; 300%+ means you should be scaling that campaign aggressively right now.
04 โ€” Monetization Health

Are users sticking, paying, and bringing friends?

These metrics diagnose the long-term health of your game's monetization and organic growth engine.

PUR
Paying User Rate
Health

"What percentage of your players ever open their wallets? Spoiler: almost always less than 5%."

The Paying User Rate tells you what fraction of your total user base has spent real money. In free-to-play mobile gaming, this almost universally sits below 5%. The vast majority of your players never pay a cent โ€” and that's by design. The F2P model works because those who do spend, often spend significantly.

Formula
Paying User Rate = (Paying Users รท Total Active Users) ร— 100 // Example: 3,200 paying users | 160,000 MAU Paying Rate = (3,200 รท 160,000) ร— 100 = 2%
Real Example
160,000 MAU, 3,200 of whom have made at least one purchase. Paying Rate = 2%. For a casual game, 1โ€“2% is average. Mid-core games often hit 3โ€“5%.
Churn
Churn Rate
Health

"The metric that quietly tells you how fast your game is losing players โ€” whether you're watching or not."

Churn rate is the percentage of users who stop playing in a given period. It's the inverse of retention, and it matters enormously for LTV. High churn means users aren't staying long enough to generate meaningful revenue, refer friends, or even finish experiencing the content you built.

Formula
Monthly Churn Rate = (Users Lost in Month รท Users at Start of Month) ร— 100 // Example: 200,000 users on June 1st | 178,000 at end of June Churn = (22,000 รท 200,000) ร— 100 = 11% // Avg. lifespan estimate: 1 รท Monthly Churn Rate = ~9 months
Real Example
Started June with 200,000 users; 22,000 didn't log in at all during the month. Monthly churn = 11%, implying an average user lifespan of ~9 months. Under 5% is excellent. Above 20% is a signal your core loop needs serious work.
K-Factor
Viral Coefficient
Health

"Does your game grow on its own โ€” or are you completely dependent on paid ads to stay alive?"

The K-Factor quantifies how much organic growth your existing users drive. A K-Factor above 1.0 means viral, exponential growth without paid acquisition. Even a K-Factor of 0.3 is meaningful in practice: every 10 paid users you acquire bring in 3 free ones โ€” effectively reducing your blended CPI by 23%.

Formula
K-Factor = Average Invites Sent per User ร— Invite Acceptance Rate // Example: Each user invites 4 friends | 15% of friends accept K-Factor = 4 ร— 0.15 = 0.6 // K > 1.0 โ†’ viral | K = 1.0 โ†’ neutral | K < 1.0 โ†’ needs paid UA
Real Example
Your referral system: each user invites 4 friends, 15% install. K-Factor = 0.6. For every 1,000 users you buy, your game organically acquires 600 more โ€” dropping your blended CPI from $3.00 to $1.88.
05 โ€” Ad Revenue Metrics

For games monetizing through in-app advertising

If your game shows ads to players โ€” rewarded video, interstitials, banners โ€” these tell you how efficiently you're monetizing that inventory.

eCPM
Effective Cost Per Mille
Ad Revenue

"The universal language of mobile advertising. How much you earn per 1,000 ad impressions."

eCPM tells you how much money you generate for every 1,000 times an ad is shown. eCPM varies hugely based on geography, ad format, and time of year (Q4 holiday season inflates eCPM 30โ€“50% industry-wide). Always segment your eCPM by these variables โ€” your blended average hides a lot of important signal.

Formula
eCPM = (Ad Revenue รท Ad Impressions) ร— 1,000 // Example: $850 ad revenue | 200,000 impressions eCPM = ($850 รท 200,000) ร— 1,000 = $4.25
Real Example
Yesterday: 200,000 ad impressions, $850 earned. Blended eCPM = $4.25. But your rewarded video alone might be $14 eCPM, while banners are $0.60. Breaking it out by format shows you where to focus.
$0.20โ€“$1Banner ads
$3โ€“$8Interstitials
$8โ€“$25+Rewarded video
Fill Rate
Ad Fill Rate
Ad Revenue

"Every unfilled ad request is money your game earned that nobody paid for."

Fill Rate is how often your ad requests are answered with an actual ad. A fill rate below 90% is a red flag to add more demand through ad mediation โ€” using multiple ad networks competing simultaneously to fill your inventory.

Formula
Fill Rate = (Ads Served รท Ad Requests Made) ร— 100 // Example: 9,400 ads served | 10,000 ad requests Fill Rate = (9,400 รท 10,000) ร— 100 = 94%
Real Example
10,000 ad requests yesterday. 9,400 filled; 600 went empty. Fill Rate = 94%. At $5.00 eCPM, that 6% gap is $300/day lost โ€” $109,500/year walking out the door unfilled.
๐Ÿ’ก
Target 95%+ fill rate. Anything below 90% is a signal to add more demand partners or review whether your price floors are too high for lower-CPM geographies.

Quick Cheat Sheet

All 20 metrics, formulas, and categories at a glance.

TermFull NameFormulaCategory
DAUDaily Active UsersUnique users / dayEngagement
MAUMonthly Active UsersUnique users / 30 daysEngagement
WAUWeekly Active UsersUnique users / 7 daysEngagement
StickinessDAU/MAU Ratio(DAU รท MAU) ร— 100Engagement
D1/D7/D30Day-N Retention(Returned Day N รท Day 0) ร— 100Engagement
ASLAvg. Session LengthTotal time รท Total sessionsEngagement
ARPUAvg. Revenue Per UserRevenue รท Total usersRevenue
ARPDAUAvg. Revenue Per DAUDaily revenue รท DAURevenue
ARPPUAvg. Revenue Per Paying UserRevenue รท Paying usersRevenue
LTVLifetime ValueARPDAU ร— Avg. lifespan (days)Revenue
CPICost Per InstallAd spend รท InstallsAcquisition
CPACost Per ActionAd spend รท Target actionsAcquisition
CTRClick-Through Rate(Clicks รท Impressions) ร— 100Acquisition
CVRConversion Rate(Installs รท Clicks) ร— 100Acquisition
ROASReturn on Ad Spend(Revenue รท Ad spend) ร— 100Acquisition
PURPaying User Rate(Paying users รท Total users) ร— 100Health
ChurnChurn Rate(Lost users รท Start-of-period users) ร— 100Health
K-FactorViral CoefficientAvg. invites ร— Accept rateHealth
eCPMEffective Cost Per Mille(Ad revenue รท Impressions) ร— 1,000Ad Revenue
Fill RateAd Fill Rate(Ads served รท Ad requests) ร— 100Ad Revenue

Now go build something people actually love playing.

Here's what nobody tells you at first: the metrics are just proxies. They're measuring signals of something deeper โ€” whether your game is genuinely fun. DAU grows when the loop is satisfying. Churn falls when there's always something worth coming back to. ARPPU rises when players encounter something they genuinely want to own.

The studios that obsess purely over metrics often end up making games that are optimized to the point of being joyless. The best mobile games โ€” the ones that build real businesses over years โ€” are made by teams who understand these numbers deeply but never lose sight of what the numbers are actually tracking: real people spending real time with something you made.

Learn every formula in this guide. Then use them to understand your players better โ€” not to replace your instincts about what makes a great game.

โ€” Written for everyone who's sat in a meeting, heard "our D7 is under 15%" and had absolutely no idea what to say next. You've got this.
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