Multi-Touch Attribution: From Blind Spend to Full Visibility

If you're spending $50K+/month on marketing and can't confidently say which channels drive revenue, you're operating blind.

The Problem

Most companies track:

• Last-click attribution (Google Analytics)
• Platform-reported conversions (Facebook, Google Ads)
• Revenue in their CRM

What they don't see:

• The 7 touchpoints before a $50K deal closed
• Which content pieces influenced the buyer committee
• Cross-channel interaction effects
• Time-decay patterns in long sales cycles

Why It Matters

Real example from a B2B SaaS client:

Before attribution:

• Spending $80K/month across 8 channels
• Optimizing based on last-click data
• LinkedIn showing 2% of conversions

After attribution:

• Discovered LinkedIn influenced 67% of deals (first or mid-touch)
• Reallocated $30K/month from low-influence channels
• Increased pipeline 40% at same budget

Architecture Overview

A production attribution system has 4 layers:

Data Collection Layer

Capture every touchpoint:

CREATE TABLE touchpoints (
  id UUID PRIMARY KEY,
  user_id VARCHAR(255),
  session_id VARCHAR(255),
  timestamp TIMESTAMP,
  channel VARCHAR(50),
  source VARCHAR(100),
  medium VARCHAR(100),
  campaign VARCHAR(255),
  content_id VARCHAR(255),
  event_type VARCHAR(50),
  event_properties JSONB
);

Key sources:

• Website tracking (Segment, custom events)
• Ad platforms (Facebook, Google, LinkedIn)
• Email engagement (opens, clicks)
• Content engagement (downloads, video views)
• Sales interactions (calls, demos, meetings)

Identity Resolution

Merge anonymous → known users:

// User starts anonymous
const anonId = trackPageView({ page: '/pricing' });

// Signs up for trial
const knownId = identifyUser({
  email: '[email protected]',
  anonId
});

// Merge all previous touchpoints
await mergeIdentities(anonId, knownId);

Attribution Model Engine

Support multiple models:

First-touch: Who introduced them? Last-touch: What closed them? Linear: Equal credit across journey Time-decay: Recent touches weighted higher Custom: Your proprietary model

Reporting and Optimization

Surface insights that drive decisions:

• Channel performance over time
• Content influence by deal size
• Attribution by customer segment
• Cross-channel interaction effects

Implementation Timeline

Week 1-2: Data collection setup Week 3-4: Identity resolution & modeling Week 5-6: Reporting dashboards Week 7-8: Optimization workflows

Typical cost: $35K-$65K for full implementation.

What You'll See

Within 30 days of going live:

• Complete visibility into customer journeys
• Confidence in channel optimization decisions
• Data to justify budget allocation
• Insights for creative and targeting

One client saved $280K/year by cutting underperforming channels and doubling down on hidden winners.

Ready to see what's actually working? Book a diagnostic call to review your current setup.