DealScout

ARPU Analysis and Key Metrics Framework

Understanding why Stocktwits generates 20x the ARPU of CoinGecko, and what it means for each investment thesis.


💵 The ARPU Puzzle

MetricCoinGeckoStocktwitsRatio
Revenue$40M$15M2.7x
MAU40M750K53x
ARPU$1.00$20.000.05x

CoinGecko has 53x the users but only 2.7x the revenue. Stocktwits monetizes each user 20x more effectively. Why?


📊 Drivers of the ARPU Differential

1. 🌎 Geography Effect (Est. 3-5x contribution)

MarketEst. CPM RangeCoinGecko MixStocktwits Mix
US$5-15~25-30%~80-90%
EU$3-8~20-25%~5-10%
Asia$1-4~30-40%<5%
RoW$0.50-2~15-20%<5%

Insight: A US-heavy audience is worth 3-5x a global audience on ad revenue alone. This is a structural advantage for Stocktwits that CoinGecko cannot easily replicate.

2. ⏱️ Engagement Depth (Est. 2-4x contribution)

BehaviorCoinGeckoStocktwits
Primary use caseLook up price → leavePost, read, argue, follow
Session duration~2-5 min (est.)~15-30 min (est.)
Sessions/day1-23-5+ for power users
Content creationNone (passive consumption)Active (10% create, 90% consume)
DAU/MAU ratioUnknown (likely 5-10%)47% (350K/750K)

Insight: Stocktwits users are living in the app. CoinGecko users are visiting the site. More time = more ad impressions = higher ARPU. The 47% DAU/MAU ratio at Stocktwits is exceptional engagement.

3. 📈 Ad Product Quality (Est. 1.5-2x contribution)

FactorCoinGeckoStocktwits
Revenue mix1/3 ads, 1/3 API, 1/3 referral90% ads
Ad sales modelMostly programmatic45% direct sales
Sales orgMinimal35+ person sales team
Advertiser typeCrypto exchanges, DeFiFinancial services, brokerages
Premium inventoryLimitedSignificant

Insight: Direct-sold ads command 2-5x programmatic CPMs. Stocktwits has invested heavily in ad sales infrastructure. CoinGecko's revenue mix is more diversified but lower-yield per user.

4. 🎯 Intent Quality (Qualitative)

SignalCoinGeckoStocktwits
User mindset"What's the price of X?""Should I buy/sell X right now?"
Trading proximityResearch phaseExecution phase
Advertiser valueAwarenessConversion

Insight: Stocktwits users are closer to the trade decision, making them more valuable to advertisers targeting active traders.


💡 The Core Strategic Insight

CoinGecko: 🟡 Monetization Problem, Not Reach Problem

  • 40M users generating only $1 each
  • Users are passive, global, and transient
  • Thesis: Deepen engagement → Enable transactions → 10-50x ARPU

Stocktwits: 🔴 Growth Problem, Not Monetization Problem

  • 750K users generating $20 each (already 20x CoinGecko)
  • Users are active, US-based, and sticky
  • Thesis: Grow user base → Maintain engagement → Scale revenue

🔮 Refined WYHTB Statements

CoinGecko: "Engagement → Monetization"

We have to believe that we can convert passive price-checkers into active traders within the CoinGecko experience, capturing transaction revenue from users who currently leave to execute elsewhere.

Implicit beliefs:

  • ✅ Users want to trade where they research (convenience > habit)
  • ✅ We can build a trading UX competitive with existing options
  • ✅ DeFi/self-custody is not a barrier for mainstream users
  • ✅ We don't lose users to competitors while building this

Stocktwits: "Growth → Scale"

We have to believe that with better product and marketing, we can 3-5x the user base while maintaining the engagement intensity that makes current users so valuable.

Implicit beliefs:

  • ✅ Product stagnation (not market saturation) explains flat growth
  • ✅ New users will engage as deeply as existing users
  • ✅ We can out-execute Robinhood/WeBull on social
  • ✅ Crypto-rails products attract new users without alienating existing ones

📈 Key Metrics to Track

CoinGecko: Engagement & Conversion Funnel

CategoryMetricWhy It MattersBaselineTarget
ReachMAUScale of opportunity40MMaintain
ReachTraffic source mixPlatform riskTBDGoogle <40%
EngagementDAU/MAU ratioEngagement depthTBD20%+
EngagementSession durationTime on platform~3 min?10+ min
EngagementPages per sessionExploration depthTBD
EngagementReturn visitor rateHabit formationTBD
ConversionWallet connection rateTop of funnel0%5%+
ConversionFunded wallet rateMoney in system0%1%+ of MAU
ConversionFirst trade rateActivation0%50%+ of funded
MonetizationTrading MAUActive traders01-2M
MonetizationTrades per trading userIntensity05+/month
MonetizationRevenue per trading userUnit economics$0$50-100/yr
MonetizationBlended ARPUOverall health$1.00$2.50+ Y1

North Star Metric: Trading MAU as % of total MAU

Stocktwits: Growth & Engagement Preservation

CategoryMetricWhy It MattersBaselineTarget
GrowthMAUCore challenge750K2M+ (Y2)
GrowthMAU growth rateVelocity0%20%+ annually
GrowthNew user acquisitionFunnel healthTBDTrack
GrowthCACEfficiencyN/A<$20
EngagementDAU/MAU ratioMust preserve47%>40%
EngagementPosts per DAUContent creationTBDMaintain
EngagementTime spent per DAUDepthTBDMaintain
RetentionD7 retention (new users)Early stickinessTBD30%+
RetentionD30 retention (new users)Long-term valueTBD20%+
RetentionCohort engagement parityQuality of growthN/ANew = existing
ConversionCrypto product MAUNew product adoption015%+ of MAU
ConversionWallet funding rateTransaction readiness05%+ of MAU
MonetizationCrypto ARPUNew revenue stream$0$50+/yr
MonetizationBlended ARPUOverall health$20$50+
RiskChurn rateTransition riskTBDFlat

North Star Metric: MAU growth rate while maintaining DAU/MAU > 40%


⚖️ The Fundamental Trade-off

DimensionCoinGeckoStocktwits
What you're buying📊 Reach (40M users)👥 Engagement (47% DAU/MAU)
What you need to build🟡 Engagement + monetization🔴 Growth engine
Which is harder🟡 Behavior change is hard🔴 Growth has been elusive
Risk profile🟡 Can you change passive users?🔴 Can you grow without dilution?
Time to signal🟢 6-12 months🟡 12-18 months
Downside protection🟢 $40M revenue base🟡 $15M revenue base

🚦 Metric Dashboards

CoinGecko Board Scorecard

CategoryMetricBaselineQ1Q2Q3Q4Y1 Target
ReachMAU40M40M+
EngagementDAU/MAUTBD20%
EngagementAvg session (min)TBD8+
ConversionWallet connects02M
ConversionFunded wallets0400K
MonetizationTrading MAU0200K
MonetizationARPU$1.00$2.50
RevenueTotal$40M$60M
RiskGoogle traffic %TBD<40%

Stocktwits Board Scorecard

CategoryMetricBaselineQ1Q2Q3Q4Y1 Target
GrowthMAU750K1M
GrowthGrowth rate0%33%
EngagementDAU/MAU47%>40%
EngagementPosts/DAUTBDMaintain
RetentionD7 (new users)TBD30%
ConversionCrypto MAU0100K
MonetizationARPU$20$30
RevenueTotal$15M$25M
RiskChurn rateTBDFlat

📐 Implications for Financial Modeling

The ARPU analysis suggests the following modeling priorities:

CoinGecko Model Drivers

  1. Conversion funnel rates (wallet → funded → trading)
  2. Trading intensity (trades per user per month)
  3. Take rate (revenue per $ traded)
  4. Engagement improvement (session duration, DAU/MAU)

Stocktwits Model Drivers

  1. User growth rate (with/without marketing spend)
  2. Engagement preservation (DAU/MAU by cohort)
  3. Crypto product adoption rate
  4. ARPU expansion (base + crypto)

See: [[FWDI - Financial Modeling Plan]] for detailed model specifications.

Other Financial Analysis