Cohort Analysis for Casino Affiliates: Understanding Player Value Over Time

Players you referred in January behave differently from those in March. A big traffic spike might bring low-quality signups. A small channel might deliver high-value players.

Cohort analysis groups users by when they joined and tracks their behavior over time. This reveals patterns that aggregate data hides.

For basics, see our beginner's guide to casino affiliate marketing.

What is Cohort Analysis?

The Basic Concept

Instead of looking at all players together, group them by a shared characteristic—typically when they signed up:

January cohort: All players who registered in January February cohort: All who registered in February March cohort: All who registered in March

Then track how each cohort behaves over time: Week 1, Week 2, Month 1, Month 3, etc.

Why It Matters

Aggregate data misleads. If your average player value seems steady, you might not notice:

• Recent players converting better (or worse) than historical
• Seasonal patterns in player quality
• The impact of specific campaigns
• Long-term retention trends

Cohort analysis surfaces these patterns.

Cohort Types for Affiliates

Time-Based Cohorts

Most common approach. Group by:

• Week of signup
• Month of signup
• Quarter of signup

Useful for tracking trends and seasonal patterns.

Source-Based Cohorts

Group by acquisition channel:

• SEO signups vs paid traffic
• Different content pieces
• Different landing pages
• Specific campaigns

Reveals which sources bring highest-value players.

Behavior-Based Cohorts

Group by first action:

• First game played
• Initial deposit size
• Bonus claimed
• Device used

Shows how initial behavior predicts long-term value.

Key Metrics to Track

Retention Rate

What percentage of each cohort remains active over time?

Week 1: 100% (by definition) Week 2: What % are still active? Week 4: Still active? Month 3: Retention after 90 days

Higher retention = more commission potential. Understanding churn prediction helps you anticipate when players will drop off.

Cumulative Revenue

Total revenue generated by cohort over time:

Month 1: $X in commissions Month 3: $Y cumulative Month 6: $Z cumulative

Reveals how long cohorts continue producing value.

Average Player Value

Per-player revenue within each cohort:

Cohort A average: $50/player Cohort B average: $30/player

Shows quality differences between cohorts. For deeper analysis, calculate full player lifetime value to understand long-term revenue potential.

Activity Metrics

Beyond revenue:

• Games played
• Sessions per week
• Deposit frequency
• Bet volumes

Activity often predicts future value.

Building Cohort Analysis

Data Requirements

You need:

• Player signup dates
• Revenue/commission data over time
• Ideally: traffic source information

If your affiliate program provides detailed reporting, use it. Otherwise, you may need to track independently.

Spreadsheet Approach

For simple analysis:

1. Export player and revenue data
2. Assign each player to a cohort (signup month)
3. Calculate metrics by cohort at intervals
4. Create visualization (cohort table or chart)

Cohort Table Format

Cohort	Month 0	Month 1	Month 2	Month 3
Jan	$1000	$800	$600	$500
Feb	$1200	$900	$650	...
Mar	$900	$700	...	...

Read across: How does Jan cohort perform over time? Read down: How do different cohorts compare at same age?

Tool Options

Google Analytics: Has basic cohort features for site behavior.

Affiliate dashboards: Some programs provide cohort views.

Spreadsheets: Flexible but manual.

BI tools: Power BI, Looker, Tableau for more sophisticated analysis.

Interpreting Results

Healthy Patterns

Gradual retention decay: Some dropoff is normal. Steep early dropoff then stabilization is common.

Consistent cohorts: Similar cohorts performing similarly suggests stable quality.

Improving trends: Newer cohorts outperforming older ones indicates improving traffic quality.

Warning Signs

Rapid decay: High initial activity followed by quick abandonment suggests bonus hunters or low-quality traffic.

Declining cohort quality: Newer cohorts worse than older ones—something changed negatively.

High variance: Wildly different cohort performance suggests inconsistent traffic sources.

Seasonal Patterns

Expect variation:

• Holiday cohorts may behave differently
• Slow seasons might have different player profiles
• Major promotions affect cohort composition

Account for seasonality when comparing cohorts.

Using Insights

Traffic Source Evaluation

Compare cohorts by source:

SEO cohort: Higher initial value, stronger retention Paid cohort: Lower initial value, faster decay

This informs budget allocation. Quality matters more than quantity.

Content Performance

If you track signup source:

• Which content pieces bring best players?
• Which drive volume but low quality?
• Where should you invest more?

Campaign Timing

Cohort data helps plan campaigns:

• When do cohorts perform best?
• How long until cohorts stabilize?
• What's the payback period on marketing investment?

Program Evaluation

Compare how players perform across different casinos:

• Which programs have better retention?
• Where do players generate more lifetime value?
• Are commission differences justified by player value?

For programs with no negative carryover like PureOdds, cohort analysis is especially valuable—their 50% RevShare means understanding long-term player value directly impacts your earnings.

Practical Challenges

Data Access

Many affiliate programs don't provide granular data:

• Aggregated reports only
• Limited historical data
• No source tracking

Workarounds:

• Request detailed reports from affiliate managers
• Track independently where possible
• Use site analytics as proxy

Attribution Issues

Matching players to traffic sources requires:

• Proper UTM tracking
• Cookie tracking limitations
• Cross-device considerations

Attribution is imperfect. Accept some noise. For deeper understanding, see our attribution modeling guide.

Sample Size

Small cohorts produce unreliable data:

• 10 players isn't a meaningful cohort
• Monthly cohorts need sufficient volume
• Consider quarterly groupings for smaller sites

Delayed Revenue

Affiliate revenue often lags:

• Player activity happens over time
• Reporting delays exist
• Full cohort value takes months to realize

Don't judge cohorts too early.

Building a Cohort Practice

Regular Analysis

Schedule cohort reviews:

• Monthly: Quick scan for anomalies
• Quarterly: Deeper analysis and trends
• Annually: Comprehensive review

Consistent Methodology

Use the same definitions over time:

• Same cohort periods
• Same metrics
• Same calculation methods

Consistency enables comparison.

Documentation

Record your analysis:

• What you measured
• What you found
• What actions you took
• Results of those actions

Build institutional knowledge.

Simple Starting Point

If this seems complex, start simple:

1. Export last 12 months of player/commission data
2. Group by signup month
3. Calculate total commission per cohort
4. Compare: which months produced most value?
5. Ask: what was different about high-value months?

Even basic analysis reveals useful patterns.

Action Items

Get your data. Request detailed reports if not automatically available. Use proper analytics tools to track cohort performance.

Start with time cohorts. Monthly signup cohorts are easiest.

Track one key metric. Cumulative revenue per player is a good start.

Compare 6-12 month history. See patterns over meaningful time.

Act on insights. Knowing is useless without action.

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Cohort analysis depends on data availability. Work with what your affiliate programs provide, and request additional reporting when possible.