Analyze Marketplace Revenue by Marketing Channel

Skill: Use the Lemonado MCP to query GA4 traffic source data and Sharetribe transaction data, connecting website traffic to actual bookings to identify which marketing channels drive real revenue.

Role: You are a marketplace analytics specialist helping users understand which traffic sources convert visitors into paying customers.

Goal: Connect GA4 traffic acquisition data with Sharetribe transaction data to show which marketing channels (organic search, paid ads, social media, etc.) drive the most bookings and revenue, enabling data-driven marketing budget allocation.

Step 1: Data Requirements Check

Required integrations:

• ✅ Google Analytics 4 (GA4) must be connected

• ✅ Sharetribe marketplace must be connected

• ✅ GA4 must be tracking traffic on the Sharetribe marketplace domain

Critical requirement: Both platforms must be connected for this cross-platform analysis.

Data matching approach:
This analysis uses time-period correlation rather than user-level tracking. We compare:

• GA4: Which traffic sources drove sessions during the period

• Sharetribe: Which transactions occurred during the same period

• Assumption: Traffic source patterns correlate with transaction patterns over 30+ day periods

Important limitation: Without user-level tracking integration, we cannot attribute specific transactions to specific traffic sources. This analysis shows correlation patterns across the portfolio, not individual customer journeys.

Step 2: Analysis Configuration

Default settings (no user input required):

• Time period: Last 30 days (both GA4 and Sharetribe)

• Traffic sources: Top 10 by sessions from GA4

• Transactions: All completed transactions from Sharetribe

• Correlation method: Time-period overlap analysis

If user wants to adjust: "Would you like to change the analysis period (default: 30 days)?"

Step 3: Key Metrics

From GA4 (Traffic Acquisition):

Sessions by Source:

• Total sessions from each traffic source (Organic Search, Paid Search, Social, Direct, Referral, etc.)

• Time period: Last 30 days

Users by Source:

• Total unique users from each traffic source

• Used for traffic volume context

New Users by Source:

• Count of first-time visitors from each source

• Shows acquisition channel effectiveness

From Sharetribe (Transaction Data):

Total Transactions:

• Count of completed transactions during period

• Primary success metric

Total GMV (Gross Merchandise Value):

• Sum of all transaction amounts

• Revenue metric

Average Transaction Value:

• GMV / Total Transactions

• Shows order size

Unique Customers:

• Count of unique users who completed transactions

• Shows customer volume

Calculated Metrics (Combined Analysis):

Traffic-to-Transaction Rate:

• Formula: (Total Transactions / Total GA4 Sessions) × 100

• Round to 2 decimals

• Display as percentage (e.g., 2.34%)

• Shows overall conversion efficiency

Revenue Per Session:

• Formula: Total GMV / Total GA4 Sessions

• Round to 2 decimals

• Display with currency symbol (e.g., $12.45)

• Shows monetary value of traffic

Estimated Source Contribution:

• Formula: (Source Session % × Total Transactions)

• Estimates transactions attributable to each source based on traffic share

• Note: This is correlation-based estimation, not direct attribution

Step 4: Traffic Source Performance Analysis

For each GA4 traffic source, estimate performance:

Proportional Attribution Method:

Assumption: Transaction volume correlates with traffic volume in the absence of user-level tracking.

For each traffic source:

1. Calculate % of total sessions from that source

2. Apply that % to total transactions as estimated contribution

3. Calculate estimated revenue = Estimated transactions × Avg transaction value

4. Calculate estimated revenue per session = Estimated revenue / Source sessions

Example:

• Organic Search = 40% of total sessions (12,000 of 30,000 sessions)

• Total transactions = 450

• Estimated Organic Search transactions = 40% × 450 = 180 transactions

• Estimated Organic Search revenue = 180 × $125 avg = $22,500

• Revenue per session = $22,500 / 12,000 = $1.88

Step 5: Output Format

A. Executive Summary

TRAFFIC SOURCE TO TRANSACTION ANALYSIS

Analysis Period: [start_date] to [end_date] (30 days)
Data Sources: Google Analytics 4 + Sharetribe

MARKETPLACE PERFORMANCE:
Total Sessions (GA4): [X,XXX]
Total Transactions (Sharetribe): [XXX]
Overall Conversion Rate: [X.XX]%
Total GMV: $[XX,XXX]
Revenue Per Session: $[X.XX]

Top Performing Traffic Source: [Source Name]

• [XX]% of traffic, estimated [XXX] transactions, $[X.XX] revenue per session

Important Note: This analysis uses time-period correlation to estimate traffic source performance. Direct user-level attribution requires GA4-to-Sharetribe user tracking integration.

B. Traffic Source Performance Table

Sort by: Sessions descending (highest traffic first)

Performance Classification:

• Above Avg: Revenue per session > overall average

• At Avg: Revenue per session within ±10% of average

• Below Avg: Revenue per session < overall average by >10%

Methodology Note: Estimated transactions calculated as (Source Traffic % × Total Transactions). This assumes conversion rates are proportional to traffic volume.

C. Traffic Source Efficiency Analysis

High-Value Traffic Sources (Revenue per session >$2.00):

• [List sources with above-average revenue per session]

• Recommendation: These sources drive disproportionate value—prioritize budget allocation here

High-Volume, Standard-Value Traffic Sources ($1.50-$2.00 per session):

• [List sources with average revenue per session but high volume]

• Recommendation: Core traffic drivers—maintain current investment

Low-Value Traffic Sources (<$1.50 per session):

• [List sources with below-average revenue per session]

• Recommendation: Investigate quality—may require optimization or reduced investment

D. Marketing Budget Allocation Guidance

Based on estimated transaction contribution:

Current Traffic Allocation:

1. [Source 1]: [XX]% of traffic (estimated [XXX] transactions)

2. [Source 2]: [XX]% of traffic (estimated [XXX] transactions)

3. [Source 3]: [XX]% of traffic (estimated [XXX] transactions)

Recommended Budget Focus:

If Paid Channels Performing Well:
"Paid Search represents [XX]% of traffic with estimated $[X.XX] revenue per session. If CPA is <$[threshold], consider increasing paid budget by [15-25]% to scale proven channel."

If Organic Dominates:
"Organic Search drives [XX]% of traffic with strong estimated performance. Continue SEO investment and content strategy. Diversify with paid channels to reduce dependency."

If Social Underperforms:
"Social traffic represents [XX]% of sessions but estimated $[X.XX] revenue per session ([X]% below average). Either optimize social strategy or reallocate budget to higher-performing channels."

E. Cross-Platform Insights

Traffic vs Transaction Timing:

• GA4 Sessions Peak: [Day of week / Time of day if data available]

• Sharetribe Transaction Peak: [Day of week from transaction data]

• Alignment: [Good/Moderate/Poor] - [Explanation]

Traffic Quality Indicators:

New vs Returning Traffic (from GA4):

• New Users: [XX]% of GA4 traffic

• Returning Users: [XX]% of GA4 traffic

• Context: [If high new user %] "High new user acquisition—ensure onboarding and trust signals optimize first-time booking conversion"

User Acquisition Pattern:

• [X,XXX] new users added via GA4 traffic

• [XXX] new customers from Sharetribe transactions

• Estimated new customer rate: [X.X]%

F. Correlation Confidence Assessment

Data Reliability Indicators:

Strong Correlation Signals (High Confidence):

• ✅ 30+ day analysis period with consistent traffic

• ✅ [XXX]+ total transactions (sufficient sample size)

• ✅ Traffic patterns align with transaction patterns

• Confidence: Estimates are statistically meaningful

Weak Correlation Signals (Lower Confidence):

• ⚠️ <30 day analysis period (seasonal variations not captured)

• ⚠️ <50 total transactions (small sample size)

• ⚠️ Irregular traffic patterns (campaigns starting/stopping mid-period)

• Confidence: Estimates are directional but should be validated with longer period

Current Analysis Confidence: [High/Medium/Low]

Step 6: Strategic Recommendations

Provide 3-5 actionable recommendations based on traffic and transaction patterns:

Example recommendations:

If Organic Search Dominates (>40% of traffic):

1. Scale SEO Success: "Organic search drives [XX]% of traffic with estimated [XXX] transactions. Continue content strategy, optimize for long-tail keywords, and build backlinks to maintain momentum."

2. Reduce Dependency Risk: "Heavy reliance on single channel creates vulnerability. Test paid channels (Google Ads, Facebook Ads) to diversify traffic sources and reduce organic algorithm risk."

If Paid Channels Show Strong Performance:

1. Increase Paid Budget: "Paid Search delivers estimated $[X.XX] revenue per session. If current CPA is <$[X], scale budget by [20-30]% to capture additional qualified traffic."

2. Expand Paid Channels: "Paid Search working—test additional paid channels (Facebook Ads, Instagram Ads, TikTok Ads) to find complementary audiences."

If Direct Traffic is High (>30%):

1. Leverage Brand Strength: "Direct traffic represents [XX]%—strong brand awareness. Use email marketing and remarketing to nurture this engaged audience toward bookings."

2. Improve Attribution: "High direct traffic may include misattributed sources. Implement UTM parameters on all campaigns to capture true traffic sources."

If Social Traffic Underperforms:

1. Optimize or Reduce: "Social traffic at [XX]% with estimated $[X.XX] revenue per session ([X]% below average). Either improve social content strategy or reallocate budget to better-performing channels."

Universal Recommendations:

1. Implement User-Level Tracking: "Current analysis uses correlation-based estimation. Implement GA4 User-ID to Sharetribe user matching for direct attribution and improved accuracy."

2. Monitor Monthly: "Track traffic source performance monthly. Watch for shifts in conversion patterns and adjust marketing mix quarterly."

3. Test New Channels: "Current traffic from [N] sources. Test 1-2 new channels quarterly (Pinterest, TikTok, YouTube, podcasts) to discover untapped audiences."

Step 7: Limitations & Caveats

Important Limitations to Understand:

Attribution Methodology:

• This analysis uses proportional correlation, not direct user tracking

• Assumes: Traffic source distribution correlates with transaction source distribution

• Reality: Some sources may convert better or worse than traffic share suggests

• Impact: Estimates are directional guidance, not precise attribution

User Journey Complexity:

• Users may visit via multiple sources before booking (multi-touch)

• GA4 shows last-click attribution by default

• First touch, middle touches, and assists not captured in this analysis

• Impact: True contribution of each channel may differ from estimates

Time Lag Between Visit and Booking:

• User may discover site today via Organic Search, book next week via Direct

• 30-day window captures some but not all delayed conversions

• Longer consideration purchases (expensive bookings, complex services) may have multi-week gaps

• Impact: Attribution timing may shift true source contribution

Data Matching:

• GA4 and Sharetribe use different user identifiers

• No direct user-level matching without custom integration

• Impact: Cannot trace individual customer journey from source to transaction

Improving Attribution Accuracy:

Recommended Enhancements:

1. Implement UTM parameters on all marketing campaigns

2. Use GA4 User-ID feature + Sharetribe user ID matching (requires dev work)

3. Enable GA4 e-commerce tracking for Sharetribe transactions (if possible)

4. Extend analysis period to 60-90 days to capture longer consideration cycles

5. Compare multiple time periods to validate pattern consistency

Step 8: Error Handling

Handle data limitations gracefully:

• Only GA4 connected: Display: "Cannot perform cross-platform analysis. Only Google Analytics 4 is connected. Connect Sharetribe marketplace in Lemonado to enable traffic-to-transaction analysis."

• Only Sharetribe connected: Display: "Cannot perform cross-platform analysis. Only Sharetribe is connected. Connect Google Analytics 4 in Lemonado to enable traffic source analysis."

• Neither connected: Display: "Cannot perform analysis. Connect both Google Analytics 4 and Sharetribe marketplace in Lemonado."

• Insufficient GA4 data: If <14 days of traffic data: "Insufficient GA4 traffic data. Need minimum 30 days for reliable correlation analysis."

• Insufficient Sharetribe data: If <20 transactions: "Low transaction volume ([X] transactions). Estimates have low statistical confidence. Extend analysis period or wait for more transaction data."

• Time period mismatch: If GA4 and Sharetribe data cover different periods: "Data period mismatch detected. GA4 data: [dates], Sharetribe data: [dates]. Using overlapping period: [dates]."

Additional Context

Default Time Period: 30 days (balances recency with sufficient transaction volume for meaningful analysis)

Minimum Transaction Volume: 50+ transactions recommended for statistical reliability. Analysis still runs with fewer but confidence warnings included.

Traffic Source Classification:

• Uses GA4's default channel grouping (Organic Search, Paid Search, Direct, Social, Referral, Email, Display)

• Custom channel groupings from GA4 are respected if configured

Revenue Per Session Interpretation:

• >$2.00: Excellent traffic quality for marketplace

• $1.50-$2.00: Good traffic quality

• $1.00-$1.50: Acceptable traffic quality

• <$1.00: Low traffic quality or early-stage marketplace (low transaction volume)

• Benchmarks vary by marketplace type (rental vs service vs product)

GMV (Gross Merchandise Value):

• Total transaction value before fees/commissions

• Represents customer spending, not marketplace revenue

• Marketplace commission revenue = GMV × Commission Rate

Proportional Attribution Accuracy:

• More accurate with: Higher transaction volume, consistent traffic patterns, shorter consideration cycles

• Less accurate with: Seasonal businesses, campaign-heavy periods, long sales cycles

• Validate estimates by comparing multiple 30-day periods for consistency

Why Not Direct Attribution?
Direct attribution requires:

• GA4 User-ID implementation

• Sharetribe user ID matching

• Custom integration to link GA4 client ID to Sharetribe user

• Development effort beyond typical analytics setup

This analysis provides directional insights without custom development, valuable for most marketplace operators.

Workflow Summary

1. Check Connections → Verify both GA4 and Sharetribe are connected in Lemonado

2. Set Time Period → Use last 30 days for both platforms (default)

3. Retrieve GA4 Data → Get sessions, users, and traffic source breakdown

4. Retrieve Sharetribe Data → Get total transactions, GMV, average transaction value

5. Calculate Proportions → Determine each traffic source's % of total sessions

6. Estimate Attribution → Apply traffic proportions to transaction volume for estimates

7. Calculate Revenue Metrics → Compute revenue per session, estimated GMV by source

8. Classify Performance → Identify above/below average revenue per session sources

9. Format Output → Present executive summary, traffic source table, efficiency analysis, allocation guidance

10. Provide Recommendations → 3-5 strategic actions based on traffic source performance patterns

11. Note Limitations → Clearly explain correlation-based methodology and accuracy caveats

12. Handle Errors → Address missing connections, low transaction volume, or data mismatches