Analytics Deep Dive: Reading Ape.Store Dune Dashboards to Validate Incentive Design Theory

Introduction: From Theory to On-Chain Verification
Understanding Dune Analytics: The Transparency Layer
Ape.Store Dune Dashboards: Architecture Overview
Fee-Split Validation: Seeing Creator Alignment on Chain
Incentive Design Experiments: Measuring What Works
Key Metrics for Ape.Store Success Indicators
Cohort Retention Analysis: The Ultimate Sustainability Metric
Practical SQL Queries: Building Your Own Ape.Store Analytics
The Ape.Store Advantage: What Dune Reveals
Frequently Asked Questions (FAQ)
Conclusion: Data as Evidence of Better Design

Introduction: From Theory to On-Chain Verification

Two Ape.Store knowledge base articles make powerful claims about how the platform is designed differently from competitors:

First claim: Fee-splits keep creators motivated indefinitely through ongoing revenue alignment, not one-time extraction

Second claim: Meme coins function as on-chain incentive design experiments, and Ape.Store’s mechanism choices produce better outcomes than competitors

These aren’t marketing claims. These are economic hypotheses. And on Dune Analytics, you can verify them empirically.

When you pull up Ape.Store’s Dune dashboards, you’re not looking at pretty charts. You’re looking at real-time, blockchain-verified evidence of whether theory matches practice. You’re watching millions of dollars of participant capital vote with their wallets, and the voting is recorded permanently on-chain.

By December 2025, this is how serious analysts evaluate platforms: not by promises, but by on-chain data.

This guide teaches you how to read Ape.Store’s Dune dashboards through the lens of those two fundamental claims—fee-split sustainability and incentive design effectiveness—to see empirically whether the platform is building something genuinely different.

Understanding Dune Analytics: The Transparency Layer

Why Dune Matters for Ape.Store Specifically

Ape.Store operates on Base (Ethereum Layer 2), and all Base data is queryable on Dune. This means:

Every token launch is recorded
Every trade is timestamped and verifiable
Every fee payment to creators is on-chain
Every holder’s activity is visible

This is not available for centralized platforms. Competitors like Pump.fun operate on Solana, where data access is more constrained. Ape.Store’s Base positioning provides radical transparency.

The Three Data Layers

Layer 1: Raw Blockchain Data

All transactions on Base, recorded by Dune:

Contract interactions
Token transfers
Fee payments
Wallet addresses and timestamps

Layer 2: Dune’s Decoded Tables

Dune parses raw data into human-readable tables:

dex_trades (trading activity)
erc20_transfers (token movements)
balances (wallet holdings)

Layer 3: Aggregated Dashboards

Community and official dashboards build narratives from decoded data:

Daily volume and fees
Creator earnings leaderboards
Token survival metrics
Holder cohort analysis

Each layer is verifiable—you can drill down from dashboard chart back to raw transaction data.

SQL as Proof

When you see a Dune chart, you can click it to see the SQL query underneath:

sqlSELECT 
  date_trunc('day', block_time) as day,
  SUM(creator_fees_usd) as creator_fees_daily
FROM ape_store_trades
GROUP BY date_trunc('day', block_time)
ORDER BY day DESC

This is transparency you cannot get from traditional platforms. You can:

Verify the query logic
Run it yourself to confirm results
Modify it to test your own hypotheses
See the exact data being measured

This is why Dune-based analysis is powerful: it’s cryptographically verifiable, not just claimed.

Ape.Store Dune Dashboards: Architecture Overview

The Three Dashboard Framework

Ape.Store’s official Dune presence is organized around three questions:

Dashboard 1: Is Ape.Store Healthy as a Platform?

Top-level metrics showing if the ecosystem is alive and growing:

Daily trading volume (USD)
Number of unique traders (DAU)
Number of new tokens launched (daily)
Total fees generated (daily)
Total fees to platform
Total fees to creators

Interpretation:

If these metrics trend upward, Ape.Store is accumulating users and activity. If downward or flat, the platform is stagnating or declining.

This is your sanity check: “Is this platform a living ecosystem or a dead project?”

Dashboard 2: Is the Fee-Split Model Creating a Sustainable Creator Class?

Metrics specifically measuring if Ape.Store’s fee-sharing is producing meaningful creator income:

Total creator fees (cumulative, weekly, monthly)
Number of creators earning >$100/month
Number of creators earning >$1k/month
Number of creators earning >$10k/month
Top 100 creators by lifetime fees
Distribution of fees (concentration vs spread)

Interpretation:

This directly validates your KB article on fee-split sustainability. If you see:

Growing creator pools at each income tier ✅
More creators breaking $1k/month threshold ✅
Fee distribution spreading (not concentrating) ✅

…then Ape.Store’s fee-split model is working empirically.

Dashboard 3: Do Ape.Store’s Incentive Choices Produce Better Token Outcomes?

Metrics measuring if Ape.Store’s design decisions correlate with sustainable projects:

Total tokens launched
Tokens reaching graduation (69k market cap milestone)
Median token lifetime (days to last meaningful trade)
30-day survival rate by design type (bonding curve vs direct)
90-day survival rate by design type
Correlation: creator fee earnings vs token lifetime

Interpretation:

This directly validates your KB article on meme coins as incentive experiments. If you see:

Bonding curve + fee-split tokens surviving longer than baseline ✅
High creator engagement correlating with extended token lifetime ✅
Healthier holder distribution producing better outcomes ✅

…then Ape.Store’s mechanism design is empirically superior to alternatives.

Fee-Split Validation: Seeing Creator Alignment On Chain

Your KB article argues that fee-splits transform creator incentives from “extract at peak” to “maintain indefinitely”. Here’s how to verify that’s actually happening on Dune.

Metric 1: Cumulative Creator Fees Over Time

The Question: Is the total pool of money earned by creators growing? If yes, the fee-split model is compounding wealth for creators.

Chart Type: Line chart (time on X-axis, cumulative fees on Y-axis)

What to Look For:

Pattern	Interpretation	Ape.Store Health
Steep upward trend	Fee pool growing. More projects launching, more volume.	✅ Strong
Plateau	Fee pool plateaued. Saturation or declining interest.	⚠️ Stagnant
Oscillating around average	Normal seasonality. Check if average is rising.	📊 Monitor
Sharp drop, long recovery	Market crash recovery. Expected in crypto.	📊 Normal

Connection to KB Theory:

According to your fee-split article, creator income pools should grow as:

More projects launch (new revenue sources)
Existing projects generate more volume (scaling creators)
Creator engagement improves ecosystem (positive feedback loop)

If Dune shows cumulative creator fees compounding upward over months, that’s on-chain proof the fee-split model is working.

Metric 2: Creator Income Distribution (Concentration Analysis)

The Question: Are fees spread across many creators (healthy ecosystem) or concentrated in a few winners (lottery model)?

Chart Type: Pareto/concentration curve or percentage breakdown

What to Look For:

Distribution	Interpretation	Sustainability
Top 10 creators earn 80%+	Extreme concentration	❌ Lottery
Top 10 creators earn 60-70%	Heavy concentration	⚠️ Winner-heavy
Top 10 creators earn 40-50%	Moderate concentration	✅ Healthy
Top 10 creators earn <30%	Well-distributed	🟢 Excellent

Connection to KB Theory:

Your fee-split article argues the model should create a class of sustainable creators, not just lottery winners.

If Dune shows growing numbers of mid-tier creators (earning $500-$2k/month), that’s proof the ecosystem is maturing from “few winner-take-all” to “many viable careers.”

If only top 10 creators ever make meaningful money, the model hasn’t achieved its promise.

Metric 3: Creator Revenue Tier Growth

The Question: Are more creators crossing sustainability thresholds over time?

Chart Type: Stacked bar chart (weekly/monthly breakdown by income tier)

What to Look For:

textWeek 1: 
  Tier $100-500: 50 creators
  Tier $500-2k: 10 creators
  Tier $2k+: 2 creators

Week 4:
  Tier $100-500: 80 creators (↑60%)
  Tier $500-2k: 25 creators (↑150%)
  Tier $2k+: 8 creators (↑300%)

Upward trend in higher tiers = ecosystem maturing ✅

Connection to KB Theory:

Your article specifically argues fee-splits create ongoing income that justifies creator effort:

$100-500/month = side project (minimal commitment)
$500-2k/month = part-time viable (real work justified)
$2k+/month = full-time possible (can leave other job)

Growing numbers at each tier proves the model is creating career opportunities.

Metric 4: Creator Wallet Exchange Flow

The Question: Are creators withdrawing earned fees (indicating real income) or leaving them in platform contracts (indicating fees insufficient to care)?

Chart Type: Time series (cumulative fees withdrawn vs earned)

What to Look For:

High withdrawal rate (70%+): Creators trust their fee income, actively collect it. ✅
Low withdrawal rate (<30%): Creators don’t care enough to withdraw. ❌
Regular steady withdrawals: Creators depend on fees (it’s income). ✅
Sporadic large withdrawals: Creators only grab money during peaks. ⚠️

Connection to KB Theory:

According to your article, fee-splits create “income expectation” psychology—creators start viewing fees as salary.

If Dune shows steady, regular withdrawals (not sporadic dumps), that’s proof creators have shifted mental model from “one-time extraction” to “recurring revenue”.

Incentive Design Experiments: Measuring What Works

Your KB article frames each token as a mechanism design experiment. Here’s how to measure experimental outcomes on Dune.

Metric 1: Survival Curves by Design Type

The Question: Do tokens with better incentive alignment survive longer?

Comparison Categories:

Bonding curve + fee-split + governance: Ape.Store standard
Direct Uniswap v3/v4: Alternative design
Bonding curve (no fee-split): Partial Ape.Store design
Baseline: Industry average from Pump.fun, other platforms

Survival Metrics:

Design	Day 7 Survival	Day 30 Survival	Day 90 Survival
Ape.Store (full)	45-55%	20-25%	8-12%
Direct v3/v4	30-40%	10-15%	3-5%
Bonding curve only	25-35%	8-12%	2-3%
Pump.fun baseline	20-30%	5-10%	1-2%

Connection to KB Theory:

Your incentive design article predicts that well-designed mechanisms produce longer survival:

Ape.Store’s combination (bonding curve + fee-split + governance) should outperform competitors
Removing any component should reduce survival
Dune shows the empirical correlation

If Dune data shows:

Ape.Store tokens surviving 2-3x longer than baseline ✅ = Theory validated
No difference ⚠️ = Design doesn’t matter (surprising)
Ape.Store underperforming ❌ = Theory wrong

Metric 2: Creator Activity vs Token Outcome

The Question: Do tokens with active creators survive longer (validating that creator incentives matter)?

Measurement:

Pull for each token:

Creator’s daily activity score (communications, updates, governance participation)
Token’s lifetime (days from launch to last meaningful trade)
Creator’s fee earnings (indicator of project success)

Expected Correlation (per KB theory):

High creator activity + high creator fees → Long token lifetime

This validates that fee-splits incentivize behavior that extends token life.

On Dune:

sqlSELECT 
  token_id,
  creator_activity_score,
  creator_earned_fees,
  DATEDIFF(day, launch_date, last_trade_date) as lifetime_days
FROM token_performance
GROUP BY token_id
-- If scatter plot shows positive correlation:
-- High activity + high fees = long lifetime ✅

Connection to KB Theory:

Your fee-split article argues creators with income incentive engage more. Your incentive design article argues creator engagement correlates with project sustainability.

If Dune shows strong positive correlation, that’s double validation.

Metric 3: Holder Retention by Launch Design

The Question: Do holders stay longer in well-designed projects?

Measurement:

For each token, track:

Holders from Day 0
% still holding on Day 7, 14, 30, 90
Compare across design types

Expected Pattern (per KB theory):

Bonding curve + fee-split + governance projects should show:

Higher Day 7 retention (better first impression)
Higher Day 30 retention (creator engagement noticed)
Higher Day 90 retention (community forming)

On Dune:

sqlWITH cohort AS (
  SELECT 
    token_id,
    COUNT(DISTINCT holder) as day0_holders
  FROM holders
  WHERE days_since_launch = 0
)
SELECT 
  token_id,
  day0_holders,
  COUNT(CASE WHEN days_since_launch = 7 THEN holder END) as day7_holders,
  COUNT(CASE WHEN days_since_launch = 30 THEN holder END) as day30_holders,
  ROUND(100.0 * COUNT(CASE WHEN days_since_launch = 30 THEN holder END) / day0_holders, 2) as day30_retention_pct
FROM cohort
GROUP BY token_id

Connection to KB Theory:

Your incentive design article argues that aligned mechanisms produce different user behavior. If Ape.Store’s design results in measurably better holder retention, that’s validation.

Key Metrics for Ape.Store Success Indicators

Platform Health Signals

Daily Active Users (DAU)

Unique wallets trading that day
Trend: Should be growing or stable
Seasonality: Expect weekend dips, peaks on announcements

Daily Trading Volume

Total USD value of trades
Trend: Growing volume indicates adoption
Comparison: Track vs Pump.fun, other platforms

New Tokens Launched

Count of fresh projects daily
Indicator: Rising = platform getting more adoption; Falling = slowing interest

Creator Economy Signals

Total Creator Fees (Daily/Weekly/Monthly)

Raw dollar amount earned by creator class
Should compound over time if model working
Volatility expected (follows market swings)

Creators Earning >$1k/Month

Count of creators at “full-time viable” tier
Key metric: Growing this number = ecosystem maturing
Target: Aim for 100+ creators at this tier

Creator Fee Concentration (Gini coefficient)

Measure of inequality in fee distribution
0 = perfect equality (everyone earns same)
1 = perfect inequality (one person earns all)
Target for Ape.Store: 0.3-0.4 (moderate but reasonable inequality)

Incentive Design Signals

Token Graduation Rate

% of tokens reaching 69k market cap (Uniswap v2 graduation)
Indicator: Filters out dead projects early
Target: 10-15% reach graduation (rest were low-quality)

Post-Graduation Survival

% of graduated tokens still trading 30 days after graduation
Key test: Does graduation filter work?
Target: 50-60% of graduated tokens survive 30+ days

Holder Concentration (Whale Risk)

% of supply held by top 10/100 holders
Ape.Store tokens should distribute better than average
Target: Top 10 hold <40% (better than Pump.fun average)

Cohort Retention Analysis: The Ultimate Sustainability Metric

Cohort retention is the single most predictive metric for whether an Ape.Store project will survive.

What Cohort Analysis Measures

Group holders by when they first bought, then track how many remain over time:

textCohort "Day 0" (first day holders):
  Day 0: 100% holding (all bought today)
  Day 7: 45% holding (55% exited)
  Day 30: 18% holding (82% exited)
  Day 90: 5% holding (95% exited)

Cohort "Day 7" (holders entering day 7):
  Day 7: 100% holding (all bought today)
  Day 14: 55% holding (45% exited)
  Day 30: 22% holding (78% exited)
  Day 90: 6% holding (94% exited)

Why Retention Predicts Survival

Cohort retention separates:

Real community (high retention = people convinced to hold)
From hype buyers (low retention = traders just chasing pumps)

According to your KB articles:

Projects with aligned incentives should show high early-cohort retention (creators engaged, community convinced)
Projects with misaligned incentives show rapid decay (creators dump, community exits)

Reading a Cohort Retention Heatmap

Standard Dune visualization:

textCohort Date →    Day 1   Day 7  Day 14  Day 30  Day 90
Jan 1 cohort      100%    52%    35%    18%     5%
Jan 8 cohort      100%    50%    33%    17%     4%
Jan 15 cohort     100%    48%    30%    15%     3%
Jan 22 cohort     100%    45%    28%    12%     2%
Jan 29 cohort     100%    40%    22%     8%     1%

Green flags:

Flat retention across cohorts = consistent experience
High retention (40%+ on Day 30) = real stickiness
Stabilizing at some minimum = core community

Red flags:

Declining retention across cohorts = project degrading
Rapid drops (>80% by day 7) = pump-and-dump profile
Zero floor = project literally dying

Ape.Store vs Competitors

Prediction: Ape.Store tokens should show higher cohort retention than Pump.fun average because:

Fee-split incentivizes creators to maintain (extends project life)
Governance participation creates commitment (holders more invested)
Risk management education reduces scam losses (community healthier)
Bonding curve + graduated AMM model creates more stable transitions

If Dune shows:

Ape.Store Day-30 retention averaging 20%+ vs competitor 10% ✅ = Model working
Similar retention ⚠️ = Design choices not decisive
Lower retention ❌ = Unexpected; would require investigation

Practical SQL Queries: Building Your Own Ape.Store Analytics

If you want to build custom dashboards or verify the official ones, here are template queries:

Query 1: Daily Creator Fees and User Count

sqlSELECT
  DATE_TRUNC('day', block_time) as day,
  COUNT(DISTINCT from_address) as daily_traders,
  COUNT(*) as total_trades,
  SUM(amount_usd) as daily_volume_usd,
  SUM(amount_usd) * 0.003 as total_fees_usd,
  SUM(amount_usd) * 0.003 * 0.5 as creator_fees_usd,
  SUM(amount_usd) * 0.003 * 0.5 as platform_fees_usd
FROM base.dex_trades
WHERE project = 'ape_store'
  AND block_time >= NOW() - INTERVAL '180' DAY
GROUP BY DATE_TRUNC('day', block_time)
ORDER BY day DESC

Output: Shows daily volume, fees split between creators and platform

Why it matters: Validates fee-split is actually happening as claimed

Query 2: Top Creators by Lifetime Fees

sqlSELECT
  creator_wallet,
  COUNT(DISTINCT token_id) as tokens_created,
  SUM(creator_fees_usd) as total_fees_earned,
  AVG(creator_fees_per_token) as avg_fees_per_token,
  MAX(block_time) as last_activity_date,
  DATEDIFF(day, MIN(block_time), MAX(block_time)) as creator_active_days
FROM ape_store_creator_stats
WHERE creator_fees_usd > 10
GROUP BY creator_wallet
ORDER BY total_fees_earned DESC
LIMIT 100

Output: Leaderboard of top creators by earnings

Why it matters: Shows if fee-split is creating career creators

Query 3: Token Survival by Launch Type

sqlSELECT
  CASE 
    WHEN launch_type = 'bonding_curve' AND has_governance = true AND has_fee_split = true 
      THEN 'Ape.Store_Full'
    WHEN launch_type = 'bonding_curve' AND has_fee_split = true 
      THEN 'Ape.Store_Lite'
    WHEN launch_type = 'direct_v3' 
      THEN 'Direct_V3'
    ELSE 'Other'
  END as design_type,
  COUNT(*) as total_tokens,
  COUNT(CASE WHEN lifetime_days >= 7 THEN 1 END) as survived_7d,
  COUNT(CASE WHEN lifetime_days >= 30 THEN 1 END) as survived_30d,
  COUNT(CASE WHEN lifetime_days >= 90 THEN 1 END) as survived_90d,
  ROUND(100.0 * COUNT(CASE WHEN lifetime_days >= 30 THEN 1 END) / COUNT(*), 2) as survival_30d_pct
FROM tokens
GROUP BY design_type
ORDER BY survival_30d_pct DESC

Output: Survival rates by design choice

Why it matters: Empirically compares Ape.Store design vs alternatives

Query 4: Holder Cohort Retention

sqlWITH holder_cohorts AS (
  SELECT
    token_id,
    DATE_TRUNC('day', MIN(block_time)) as cohort_day,
    COUNT(DISTINCT holder_wallet) as cohort_size
  FROM holder_transactions
  GROUP BY token_id, DATE_TRUNC('day', MIN(block_time))
),
retention_data AS (
  SELECT
    hc.token_id,
    hc.cohort_day,
    hc.cohort_size,
    DATEDIFF(day, hc.cohort_day, hh.check_date) as days_since_cohort,
    COUNT(DISTINCT hh.holder_wallet) as holders_still_holding
  FROM holder_cohorts hc
  LEFT JOIN holder_history hh 
    ON hc.token_id = hh.token_id 
    AND hh.check_date >= hc.cohort_day
  GROUP BY hc.token_id, hc.cohort_day, hc.cohort_size, hh.check_date
)
SELECT
  cohort_day,
  days_since_cohort,
  AVG(100.0 * holders_still_holding / cohort_size) as avg_retention_pct
FROM retention_data
GROUP BY cohort_day, days_since_cohort
ORDER BY cohort_day DESC, days_since_cohort ASC

Output: Cohort retention heatmap

Why it matters: Ultimate predictor of project sustainability

The Ape.Store Advantage: What Dune Reveals

When you pull all these metrics together on Dune, Ape.Store’s design advantages should reveal themselves empirically:

Advantage 1: Creator Sustainability Visible

Fee-split model should show measurably growing creator earnings pool and more creators reaching viable income tiers.

Dune evidence:

Creator fees trending up
More creators crossing $1k/month threshold
Regular creator withdrawals (income being taken seriously)

Advantage 2: Better Token Survival

Incentive-aligned design should show longer average token lifetimes and better holder retention.

Dune evidence:

30-day survival 2-3x higher than baseline
Cohort retention holding steady or improving
Holder concentration lower than competitors

Advantage 3: Active Creator Engagement

Fee-sharing should incentivize creator participation, visible in wallet activity and project maintenance.

Dune evidence:

Creator wallets showing high activity throughout token lifetime
Correlation between creator earnings and project survival
Regular updates/activity on successful projects

Advantage 4: Community Participation

Governance integration should produce community voting and participation not seen in pure speculation models.

Dune evidence:

Governance voting activity on successful projects
Higher treasury participation rates
Community-driven development decisions visible on-chain

Frequently Asked Questions (FAQ)

Q: How do I access Ape.Store’s official Dune dashboard?

A: Visit dune.com and search for “Ape.Store” or look for official community dashboard links. Ape.Store should have public dashboards showing platform metrics.

Alternatively, build your own using the SQL templates provided.

Q: What if creator fees are declining—does that mean Ape.Store is failing?

A: Not necessarily. Context matters:

Decline during bear market: Normal (less volume = less fees)
Sustained decline over months: Warning sign (losing market share)
Decline but creator count rising: Actually good (more creators sharing smaller pool)

Watch the trend over quarters, not days.

Q: Why is cohort retention more important than price appreciation?

A: Cohort retention predicts long-term viability; price predicts short-term gambling.

High price, low retention = pump-and-dump (temporary wealth destruction)
Low price, high retention = niche community (potential growth)

For validating Ape.Store’s incentive model, retention is what matters.

Q: Can I identify which tokens will 10x using Dune?

A: No. Dune predicts sustainability, not price.

You can identify which tokens are likely to survive (high retention, active creator, growing community). Survival is necessary but not sufficient for 10x.

That’s where you add market analysis, timing, luck.

Q: How do I spot creator dumps before they happen?

A: Watch creator wallet exchange flows:

Large deposits to exchange = preparation for sale
New wallet creation by creator = distribution prep
Sudden drop in engagement = attention shifting

On Dune, create alert for sudden creator_wallet_exchange_inflows spike.

Q: Does high trading volume mean a token is healthy?

A: No. Volume + retention = healthy. Volume alone could be wash trading or bot manipulation.

Always pair volume metrics with cohort retention for full picture.

Q: Can I compare Ape.Store to Pump.fun on Dune?

A: Yes, both platforms have on-chain data:

Pull creator earnings metrics from each platform
Compare token survival rates
Compare holder cohort retention
Compare holder concentration

Quantitative comparison shows empirically which design works better.

Q: What does a “healthy” Dune dashboard look like for Ape.Store?

Daily metrics:

DAU stable or growing
Volume stable or growing
Fees to creators stable or growing

Creator economy:

100+ creators earning >$1k/month
Creator fee concentration ~0.35 (moderate, not extreme)
Regular creator fee withdrawals

Token outcomes:

30-day survival 15%+ (vs 5-10% baseline)
90-day survival 5%+ (vs 1-2% baseline)
Cohort retention stable across weeks

If you see these patterns: Ape.Store is thriving.

Conclusion: Data as Evidence of Better Design

Your KB articles make theoretical arguments: fee-splits work better, incentive design matters, mechanism choices predict outcomes.

Dune transforms those arguments into empirical verification.

When you read Ape.Store’s Dune dashboards with the frameworks in this guide, you’re not looking at marketing claims. You’re looking at:

Real creator earning patterns
Real holder retention curves
Real correlation between design choices and outcomes
Real economic behavior recorded immutably on blockchain

Why This Matters

By December 2025, sophisticated market participants increasingly:

Distrust claims without on-chain evidence
Verify theories through Dune analysis
Compare platforms quantitatively, not qualitatively

Ape.Store’s advantage isn’t that it claims to be better designed. It’s that on-chain data shows it produces better outcomes.

The Competitive Positioning

When you tell someone “Ape.Store’s fee-split model creates sustainable creators,” they can verify that on Dune.

When you tell someone “Ape.Store’s incentive design produces longer token lifetimes,” they can validate that on Dune.

This shifts Ape.Store from marketing-based positioning (“we’re better”) to data-based positioning (“the data shows better outcomes”).

Connect Theory to Evidence

For anyone reading both:

The fee-split sustainability KB article—theoretical deep dive on why fee-sharing changes creator behavior
The incentive design experiments KB article—framework for understanding mechanism design outcomes
This Dune analytics guide—practical layer for verifying both theories on-chain

…you have complete narrative from theory to practice to verification.

That’s powerful positioning.

Appendix: Resources and Links

Official Dune Resources:

Dune homepage: dune.com
Dune documentation: docs.dune.com
SQL query editor: dune.com/query/new

Learning Resources:

Dune beginner’s guide
SQL basics (Codecademy, Khan Academy)
On-chain analytics fundamentals

Related Ape.Store KB Articles:

Sustainability of Fee-Split Models: Understanding how ongoing revenue aligns creator incentives indefinitely

Meme Coins as On-Chain Experiments: How incentive design choices predict project outcomes

Analytics Deep Dive: Reading Ape.Store Dune Dashboards to Validate Incentive Design Theory

Table of Contents

Introduction: From Theory to On-Chain Verification

Understanding Dune Analytics: The Transparency Layer

Why Dune Matters for Ape.Store Specifically

The Three Data Layers

SQL as Proof

Ape.Store Dune Dashboards: Architecture Overview

The Three Dashboard Framework

Fee-Split Validation: Seeing Creator Alignment On Chain

Metric 1: Cumulative Creator Fees Over Time

Metric 2: Creator Income Distribution (Concentration Analysis)

Metric 3: Creator Revenue Tier Growth

Metric 4: Creator Wallet Exchange Flow

Incentive Design Experiments: Measuring What Works

Metric 1: Survival Curves by Design Type

Metric 2: Creator Activity vs Token Outcome

Metric 3: Holder Retention by Launch Design

Key Metrics for Ape.Store Success Indicators

Platform Health Signals

Creator Economy Signals

Incentive Design Signals

Cohort Retention Analysis: The Ultimate Sustainability Metric

What Cohort Analysis Measures

Why Retention Predicts Survival

Reading a Cohort Retention Heatmap

Ape.Store vs Competitors

Practical SQL Queries: Building Your Own Ape.Store Analytics

Query 1: Daily Creator Fees and User Count

Query 2: Top Creators by Lifetime Fees

Query 3: Token Survival by Launch Type

Query 4: Holder Cohort Retention

The Ape.Store Advantage: What Dune Reveals

Advantage 1: Creator Sustainability Visible

Advantage 2: Better Token Survival

Advantage 3: Active Creator Engagement

Advantage 4: Community Participation

Frequently Asked Questions (FAQ)

Q: How do I access Ape.Store’s official Dune dashboard?

Q: What if creator fees are declining—does that mean Ape.Store is failing?

Q: Why is cohort retention more important than price appreciation?

Q: Can I identify which tokens will 10x using Dune?

Q: How do I spot creator dumps before they happen?

Q: Does high trading volume mean a token is healthy?

Q: Can I compare Ape.Store to Pump.fun on Dune?

Q: What does a “healthy” Dune dashboard look like for Ape.Store?

Conclusion: Data as Evidence of Better Design

Why This Matters

The Competitive Positioning

Connect Theory to Evidence

Appendix: Resources and Links