attribution-analyzer

Attribution Analyzer

Performs multi-touch attribution analysis on lead-to-conversion journey data. Applies five attribution models (first-touch, last-touch, linear, time-decay, U-shaped) to the same dataset and compares outputs. Identifies highest-impact channels and touchpoints, calculates channel ROI, and recommends budget allocation shifts. Works with CRM data or manually provided touchpoint logs.

Prerequisites

• agency.config.json in the project root
• Touchpoint data: lead journeys with channel, timestamp, and conversion status
• Optional: channel spend data for ROI calculations
• Optional: crm-writer integration for pulling journey data from CRM

Phase 0: Read Config

1. Read agency.config.json from the project root.
2. Extract channel configuration:
   • outreach.channels[] -- active marketing channels
   • outreach.cadence[] -- touchpoint sequence and timing
3. Extract tools.crm for CRM data access method.
4. Extract tools.analytics for any analytics platform integration.
5. Check for attribution.default_model preference in config.

Phase 1: Gather Touchpoint Data

Collect or retrieve lead journey data. Each journey record needs:

{
  "lead_id": "lead_001",
  "converted": true,
  "conversion_value": 5000,
  "conversion_date": "2024-03-15",
  "touchpoints": [
    {
      "channel": "linkedin_ad",
      "timestamp": "2024-02-01T10:00:00Z",
      "type": "impression",
      "content": "CRO case study carousel"
    },
    {
      "channel": "cold_email",
      "timestamp": "2024-02-05T14:30:00Z",
      "type": "email_open",
      "content": "Touch 1 -- PAS framework"
    },
    {
      "channel": "website",
      "timestamp": "2024-02-06T09:00:00Z",
      "type": "page_visit",
      "content": "Case study page"
    },
    {
      "channel": "cold_email",
      "timestamp": "2024-02-10T11:00:00Z",
      "type": "email_reply",
      "content": "Touch 2 follow-up"
    },
    {
      "channel": "demo_call",
      "timestamp": "2024-02-15T15:00:00Z",
      "type": "meeting",
      "content": "Discovery call"
    }
  ]
}

Data sources:

• Manual input (user provides CSV or JSON)
• CRM pull via crm-writer (read from Outreach CRM tab)
• Analytics export (Google Analytics, Mixpanel)

If data is incomplete, flag gaps and proceed with available data.

Phase 2: Apply Attribution Models

Apply all five models to every converted lead's touchpoint chain:

Model 1: First-Touch Attribution

• 100% credit to the first touchpoint in the journey
• Use case: understanding which channels drive awareness and initial discovery
• Formula: credit = conversion_value assigned entirely to touchpoint[0]

Model 2: Last-Touch Attribution

• 100% credit to the last touchpoint before conversion
• Use case: understanding which channels close deals
• Formula: credit = conversion_value assigned entirely to touchpoint[n-1]

Model 3: Linear Attribution

• Equal credit distributed across all touchpoints
• Use case: valuing every interaction in the journey equally
• Formula: credit_per_touch = conversion_value / touchpoint_count

Model 4: Time-Decay Attribution

• More credit to touchpoints closer to conversion
• Use case: understanding recent influence while acknowledging earlier touches
• Formula: weight = 2^((days_before_conversion * -1) / half_life) where half_life = 7 days
• Normalize weights so they sum to 1.0

Model 5: U-Shaped (Position-Based) Attribution

• 40% to first touch, 40% to last touch, 20% split among middle touches
• Use case: valuing both discovery and closing while acknowledging the nurture
• Formula:
  • First touch: 0.4 * conversion_value
  • Last touch: 0.4 * conversion_value
  • Middle touches: (0.2 * conversion_value) / middle_touch_count

For each model, aggregate credit by channel across all converted leads.

Phase 3: Compare Model Outputs

Build a comparison matrix:

CHANNEL ATTRIBUTION COMPARISON
===
Channel          | First-Touch | Last-Touch | Linear  | Time-Decay | U-Shaped
-----------------|-------------|------------|---------|------------|--------
LinkedIn Ads     | $12,000     | $3,000     | $7,500  | $5,200     | $8,400
Cold Email       | $8,000      | $15,000    | $10,000 | $11,800    | $9,600
Website/Organic  | $3,000      | $2,000     | $6,500  | $4,000     | $3,200
Demo Calls       | $0          | $18,000    | $6,000  | $12,000    | $7,200
Referrals        | $2,000      | $0         | $3,000  | $1,500     | $1,600

Calculate the consensus score per channel:

• Average attribution value across all 5 models
• Rank channels by consensus score
• Flag channels with high variance (score differs more than 2x between models)

Phase 4: Channel ROI Analysis

If spend data is available, calculate ROI per channel per model:

CHANNEL ROI (by attribution model)
===
Channel          | Spend   | First-Touch ROI | Linear ROI | Time-Decay ROI | Consensus ROI
-----------------|---------|-----------------|------------|----------------|-------------
LinkedIn Ads     | $2,000  | 6.0x            | 3.8x       | 2.6x           | 4.2x
Cold Email       | $500    | 16.0x           | 20.0x      | 23.6x          | 19.2x
Website/Organic  | $1,000  | 3.0x            | 6.5x       | 4.0x           | 3.2x

Identify:

• Highest ROI channel: best return per dollar regardless of model
• Most scalable channel: high ROI with room to increase spend
• Underperforming channel: low ROI across multiple models
• Discovery channel: high in first-touch but low in last-touch
• Closing channel: high in last-touch but low in first-touch

Phase 5: Touchpoint Sequence Analysis

Beyond channel-level attribution, analyze touchpoint patterns:

Winning sequences (most common paths among converted leads):

TOP CONVERSION PATHS:
1. LinkedIn Ad -> Cold Email -> Website -> Demo Call (35% of conversions)
2. Cold Email -> Cold Email -> Demo Call (25% of conversions)
3. Referral -> Website -> Demo Call (20% of conversions)

Average touches to conversion:

• Overall: X touchpoints
• By channel entry point: which first-touch channel leads to fastest conversion
• By deal size: do larger deals require more touches

Drop-off points:

• Where in the sequence do leads most commonly go cold
• Which channel transitions have the highest drop-off rate

Phase 6: Recommendations

Generate actionable recommendations:

BUDGET RECOMMENDATIONS:
===
1. INCREASE: [Channel] -- [reason, supported by data]
   Current spend: $X -> Recommended: $Y (+Z%)
   Expected impact: [projected additional revenue]

2. DECREASE: [Channel] -- [reason, supported by data]
   Current spend: $X -> Recommended: $Y (-Z%)
   Expected savings: [amount freed up]

3. TEST: [Channel/tactic] -- [hypothesis to validate]
   Budget: $X for [duration]
   Success metric: [what to measure]

SEQUENCE RECOMMENDATIONS:
===
1. Prioritize [channel] as first touch for [segment]
2. Ensure [channel] is always in the path -- linear model shows consistent contribution
3. [Channel] is most effective as the final touch before conversion

Phase 7: Output

Return structured JSON:

{
  "attribution_report": {
    "analysis_date": "2024-03-15",
    "data_summary": {
      "total_leads": 150,
      "converted_leads": 45,
      "total_revenue": "$225,000",
      "total_touchpoints_analyzed": 487,
      "average_touches_to_conversion": 4.2,
      "date_range": "2024-01-01 to 2024-03-15"
    },
    "models": {
      "first_touch": { "channel_scores": {} },
      "last_touch": { "channel_scores": {} },
      "linear": { "channel_scores": {} },
      "time_decay": { "channel_scores": {}, "half_life_days": 7 },
      "u_shaped": { "channel_scores": {} }
    },
    "consensus_ranking": [
      { "channel": "cold_email", "consensus_score": 19200, "consensus_roi": "19.2x" }
    ],
    "top_conversion_paths": [],
    "drop_off_analysis": {},
    "channel_roi": {},
    "recommendations": {
      "increase": [],
      "decrease": [],
      "test": [],
      "sequence_changes": []
    }
  }
}

Example Usage

Trigger phrases:

• "Run attribution analysis on our pipeline"
• "Which channel is driving the most revenue?"
• "Compare first-touch vs last-touch attribution"
• "Analyze our marketing touchpoints for Q1"
• "What's the ROI of our cold email vs LinkedIn ads?"
• "Show me the conversion paths for our best deals"

User: Run attribution analysis on our Q1 pipeline
Assistant: [pulls touchpoint data from CRM, applies 5 models, builds comparison matrix, identifies cold email as highest ROI channel, recommends increasing email volume and testing LinkedIn content changes]

User: Which channel should we invest more in?
Assistant: [runs full attribution, finds LinkedIn drives discovery but email closes deals, recommends maintaining LinkedIn for awareness and doubling email follow-up sequences]