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Marketing Analytics LMS

Case 9: Multi-Touch Attribution with Retargeting

Case 9: Multi-Touch Attribution Analysis: Sales with Retargeting

Section titled “Case 9: Multi-Touch Attribution Analysis: Sales with Retargeting”

Context

Section titled “Context”

You have sales data and want to understand which campaigns actually drive revenue

The Challenge

Section titled “The Challenge”

Users don’t convert in a straight line:

  • They land from paid ad
  • Leave without purchasing
  • Get retargeted via SMS
  • Click SMS, still don’t purchase
  • Get retargeted via email
  • Click email, still don’t purchase
  • Visit directly and finally purchase

Question: Which campaign gets credit?

The Analysis Process

Section titled “The Analysis Process”

Step 1: Query Sales Data

Section titled “Step 1: Query Sales Data”

Using your attribution system (Moon, PostHog, etc.):

  • Pull all sales transactions
  • For each sale, get the customer email

Step 2: Match Sales to Leads

Section titled “Step 2: Match Sales to Leads”
  • Each sale has an email (e.g., sarah@example.com)
  • Look up lead record for that email
  • Lead record has original UTM parameters from first landing

Step 3: Attribute to Original Source

Section titled “Step 3: Attribute to Original Source”
Sale → Email Match → Lead Record → Original UTM Parameters


Transaction $499
  ↓
sarah@example.com
  ↓
Lead (created 2025-11-21)
  ↓
campaign_id: 120236311971890504
affid: 1000
utm_source: fb
landing_page: /medication-tirzepatide-discount-01

Result: Sale attributed to Facebook campaign, even if final conversion came from SMS/email/direct

Real Example: Double Retargeting Attribution

Section titled “Real Example: Double Retargeting Attribution”

Customer Journey Discovered

Section titled “Customer Journey Discovered”

  1. Initial landing (Facebook campaign, affid 1000)

    • Captured email: user@example.com
    • Became lead, didn’t purchase

  2. SMS retargeting (3 days later)

    • Clicked SMS link
    • Visited site, didn’t purchase
    • System logged: SMS click for user@example.com

  3. Email retargeting (5 days later)

    • Clicked email link
    • Visited site, didn’t purchase
    • System logged: Email click for user@example.com

  4. Final purchase (7 days later)

    • Made purchase: $499
    • Email on transaction: user@example.com

Attribution Analysis Shows

Section titled “Attribution Analysis Shows”
  • Original source: Facebook campaign 120236311971890504, affid 1000
  • Middle touches: SMS retargeting, Email retargeting
  • Final touch: Direct or email (last click before purchase)
  • Credit for acquisition: Facebook campaign

Why You Can See This

Section titled “Why You Can See This”
  • The system had initial data stored from Day 1 (when they first landed)
  • Email connects all touchpoints
  • Lead record preserves original UTM parameters
  • Sale record matches to lead via email
  • All touchpoint events tagged with user identifier

What This Reveals

Section titled “What This Reveals”

Campaign Performance with Nurture

Section titled “Campaign Performance with Nurture”
Campaign 120236311971890504 (Full Journey Analysis):


Immediate conversions (0-1 day):
- Sales: 45
- Revenue: $22,455
- Conversion: 1.3% of leads


With SMS retargeting (2-7 days):
- Sales: 82
- Revenue: $40,918
- Conversion: 2.4% of leads


With Email retargeting (2-14 days):
- Sales: 48
- Revenue: $23,952
- Conversion: 1.4% of leads


Other touchpoints:
- Sales: 12
- Revenue: $5,988


Total attributed to campaign:
- Sales: 187
- Revenue: $93,313
- Conversion: 5.5% of leads
- ROAS: 9.3x

Key Insight

Section titled “Key Insight”
  • Only 24% of sales convert immediately
  • 76% require retargeting (SMS/email)
  • But ALL 187 sales originated from the same Facebook campaign
  • Without that campaign, you wouldn’t have those leads to retarget

Traffic Source Analysis (Affid)

Section titled “Traffic Source Analysis (Affid)”

Checking sales by affid (traffic source identifier)

Section titled “Checking sales by affid (traffic source identifier)”
Affid 1000 (Facebook):
- Leads generated: 5,200
- Sales (all touchpoints): 420
- Revenue: $209,580
- Average days to convert: 6.3
- Touchpoints before sale: 3.2 average


Affid 2000 (Google):
- Leads generated: 2,100
- Sales (all touchpoints): 98
- Revenue: $48,902
- Average days to convert: 4.1
- Touchpoints before sale: 2.5 average

Insight

Section titled “Insight”
  • Facebook (affid 1000) drives more volume and higher-quality leads
  • Google converts faster with fewer touchpoints
  • Both benefit from SMS/email retargeting

How You Instantly See This

Section titled “How You Instantly See This”

”Not magical tools, just knowing what to check”

Section titled “”Not magical tools, just knowing what to check””

What you did:

  1. Opened attribution system (Moon/PostHog/etc.)
  2. Queried: “Show me sales with original traffic source”
  3. Instantly saw results with campaign IDs, affids, UTM parameters

Why it was instant:

  • Data connections already exist in the system
  • Leads were captured with UTM parameters on Day 1
  • Sales matched to leads via email automatically
  • Query just exposes existing relationships

The “magic” is not the tool - it’s:

  • Knowing to ask “What was their ORIGINAL source?” (not last click)
  • Understanding the data structure (Sales → Leads → UTM)
  • Having systems properly configured (attribution chain intact)

Principles Applied

Section titled “Principles Applied”

What You Need in Your System

Section titled “What You Need in Your System”
  1. First-touch capture: Original UTM parameters stored with lead
  2. User identification: Email/phone as primary identifier
  3. Event tracking: All touchpoints logged (SMS clicks, email clicks, site visits)
  4. Sale connection: Transaction email matches to lead email
  5. Attribution query: Tool can show sales with original source

PostHog does this: User profiles maintain original properties, all events connected Moon does this: Attribution platform designed for this exact use case Most modern tools do this: It’s table stakes in 2025

If your system can’t show this, get a better system.

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