What is Multi-Touch Attribution?

The Problem with Single-Touch Attribution

Imagine this scenario: A customer discovers your brand through a LinkedIn ad, reads three blog posts over two weeks, clicks a retargeting ad on Instagram, and finally converts after receiving an email campaign.

Who gets credit for that conversion?

With last-touch attribution, the email gets 100% of the credit. The LinkedIn ad, blog content, and Instagram retargeting? Zero credit. Your analytics would tell you to cut those "non-performing" channels.

With first-touch attribution, the LinkedIn ad gets 100%. Everything that nurtured and closed the deal? Invisible.

Both approaches are wrong. They create a distorted view of marketing performance that leads to bad budget decisions.

Multi-touch attribution solves this by tracking the complete journey and distributing credit across all the touchpoints that influenced the conversion.

Single-Touch vs Multi-Touch: A Side-by-Side Comparison

The fundamental issue with single-touch attribution is that it treats marketing as a single moment rather than a process. In reality, Forrester research found that B2B buyers average 27 interactions with a vendor before purchasing—15 digital and 12 human. A 6sense study of 900 B2B buyers found buying teams engage in at least 15 interactions, and HockeyStack's analysis of B2B SaaS data shows the average deal requires 266 touchpoints across 3.5 channels. Single-touch attribution ignores all but one of these interactions.

How Multi-Touch Attribution Works

MTA follows three core steps: tracking journeys, applying a model, and aggregating results.

Step 1: Track User Journeys Across Sessions

Every interaction is logged with a consistent user identifier. When someone visits your site from a Google ad, returns a week later organically, and converts from an email, MTA connects these sessions into one journey.

This requires two technical capabilities:

1. Persistent identity: A first-party cookie or device fingerprint that survives across sessions
2. Identity resolution: Connecting anonymous sessions to known users when they identify themselves (login, form submission, purchase)

Without these, you'll have fragmented journeys that appear as separate single-touch conversions.

Step 2: Apply an Attribution Model

An attribution model is the algorithm that determines how conversion credit is distributed across touchpoints. There are two categories: rule-based models (predetermined formulas) and data-driven models (machine learning).

Rule-Based Attribution Models

Linear attribution gives equal credit to every touchpoint. It's simple, transparent, and works well as a baseline. The downside: it treats a random display impression the same as a high-intent branded search.

Time decay attribution gives more credit to touchpoints closer to conversion. A touchpoint from yesterday matters more than one from three weeks ago. This model suits businesses with short consideration cycles where recent interactions are genuinely more influential.

U-shaped (position-based) attribution emphasizes the first and last touchpoints—the introduction and the close—while giving less credit to nurturing touches in the middle. This works well for lead generation where you care about both acquisition source and converting channel.

W-shaped attribution adds a third emphasis point: the lead creation moment (when an anonymous visitor becomes a known lead). This is popular in B2B where the lead capture event is strategically important.

Data-Driven Attribution Models

Data-driven models use machine learning to analyze your actual conversion data and determine credit allocation. Instead of following a fixed formula, they identify which touchpoint patterns correlate with conversions.

The main approaches are:

• Markov chains: Calculate the "removal effect" of each channel—how much would conversions drop if that channel didn't exist?
• Shapley values: Apply game theory to fairly distribute credit based on each channel's marginal contribution

Data-driven models can be more accurate than rule-based models, but they require significant data volume (typically 5,000+ conversions per month) to be statistically reliable. With less data, they may overfit to noise.

Step 3: Aggregate and Report

With individual journeys attributed, you aggregate the results to answer strategic questions:

• Which channels initiate journeys that convert? (First-touch credit by channel)
• Which channels close deals? (Last-touch credit by channel)
• Which channels assist without closing? (Assist credit—total credit minus last-touch)
• What's the true ROI of each channel? (Attributed revenue divided by spend)
• How do channels work together? (Common conversion paths, channel synergies)

The reporting layer is where MTA delivers value. You're not just tracking—you're surfacing insights that inform budget allocation.

A Real-World Example: How MTA Changes Budget Decisions

Consider a hypothetical e-commerce brand spending $100,000/month across five channels:

Based on last-touch data, the obvious move is to cut Paid Social (0.8x ROAS) and shift budget to Email (6.8x ROAS) and Brand Search (5.0x ROAS).

Now look at the same data with multi-touch attribution (linear model):

The picture changes dramatically:

• Paid Social isn't underperforming—it's initiating journeys that other channels close. Its 68% assist rate means it touches most converting journeys but rarely gets last-touch credit.
• Email is over-credited by last-touch. It closes deals but doesn't create demand. Scaling email without top-of-funnel investment won't grow revenue.
• Non-Brand Search has a high assist rate (45%), meaning it often introduces prospects who convert later through other channels.

The strategic insight: Paid Social and Non-Brand Search are pipeline generators. Cutting them would starve the funnel, eventually causing Email and Brand Search performance to decline.

Why MTA Matters for Budget Decisions

Single-touch attribution systematically undervalues awareness channels and overvalues closing channels. This leads to a predictable failure pattern:

1. Cut "underperforming" awareness channels (because they get no last-touch credit)
2. See closing channels decline (because there's no new pipeline entering the funnel)
3. Increase spend on closing channels (chasing diminishing returns from a shrinking audience)
4. Watch CAC rise and revenue plateau (the funnel has been starved)

This is sometimes called the "attribution death spiral." Marketers optimize toward the metric (last-touch ROAS) while destroying the underlying system (the full-funnel customer journey).

MTA breaks this cycle by showing the true contribution of each channel at every stage of the funnel.

When to Use Multi-Touch Attribution

MTA provides the most value when:

• Customer journeys span multiple sessions — B2B sales cycles, considered purchases, subscription businesses
• Multiple channels contribute to conversions — you're running campaigns across search, social, display, email, and content
• You need tactical, real-time data — weekly or monthly budget optimization, campaign-level decisions
• Budget allocation decisions are frequent — you're actively moving spend between channels based on performance
• You have sufficient conversion volume — at least 500 conversions per month for rule-based models

When MTA Is Less Useful

MTA isn't always the right tool:

• Journeys are single-session — Impulse purchases, low-consideration products. If 80%+ of conversions happen in one session, simple last-touch is fine.
• One channel dominates — If 90% of traffic is direct or organic, there's little to attribute.
• You need to measure brand lift or offline impact — MTA tracks digital touchpoints. For brand awareness or offline channels, you need media mix modeling (MMM) or incrementality tests.
• Low conversion volume — Below 500 conversions/month, statistical noise overwhelms signal. Use simpler models.

Common MTA Implementation Mistakes

Mistake 1: Ignoring Cross-Device Journeys

A user researches on mobile, then purchases on desktop. Without cross-device identity resolution, this looks like two separate journeys: one "failed" mobile session and one "direct" desktop conversion.

Solution: Implement identity resolution through logged-in experiences, email link decoration, or probabilistic matching.

Mistake 2: Short Lookback Windows

If your lookback window is 7 days but your sales cycle is 30 days, you're missing the touchpoints that started the journey.

Solution: Set lookback windows based on your actual sales cycle. Analyze time-to-conversion data before choosing a window.

Mistake 3: Trusting Platform-Reported Attribution

Google Ads, Meta, and LinkedIn each report their own attributed conversions. These platforms have an incentive to claim credit, and they can't see touchpoints on other platforms.

Solution: Use a neutral, first-party attribution system that tracks all channels equally.

Mistake 4: Not Accounting for View-Through Conversions

Display and video ads often influence without getting clicked. If you only track click-through conversions, you undervalue these channels.

Solution: Include view-through conversions with a shorter attribution window (1-7 days) and lower credit weight than click-through.

Mistake 5: Over-Engineering the Model

Spending months building a custom Shapley value model when you have 800 conversions per month. Complex models need data to be reliable.

Solution: Start with linear attribution. It's transparent, easy to explain, and good enough for most decisions. Graduate to complex models when you have the data volume to support them.

Data Requirements for MTA

Different attribution approaches require different data volumes:

As a general rule, you want at least 10x more transitions than the number of touchpoints you're analysing. With fewer than that, the model may produce misleading results—consider grouping similar channels or expanding your data collection window.

Beyond conversion volume, you need:

• Journey data: Touchpoints, timestamps, channel/source information
• User identity: Consistent identifier across sessions
• Conversion data: Revenue, conversion type, timestamp
• Cost data: Spend by channel for ROI calculations

Getting Started with MTA

Implementing effective multi-touch attribution requires four foundational capabilities:

1. Consistent User Identity

You need to track users across sessions. This typically means:
- First-party cookies for anonymous tracking
- User IDs for logged-in tracking
- Identity resolution to connect anonymous and known sessions

2. Server-Side Tracking

Client-side tracking (JavaScript) is blocked by ad blockers, Safari ITP, and Firefox ETP. With ad blockers affecting 25-30% of web traffic and blocking over 40% of client-side pixels, client-side tracking typically captures only 60-70% of actual conversions. Server-side tracking captures 95-98% by sending data directly from your servers, recovering the 20-40% of events that client-side misses.

3. Journey Stitching

Connect individual events into complete journeys. This requires:
- Session detection (grouping events into sessions)
- Journey assembly (connecting sessions for the same user)
- Conversion linkage (connecting journeys to conversion outcomes)

4. Attribution Logic

Choose and implement an attribution model. Start simple (linear), then add complexity as your data and needs grow.

Summary

Multi-touch attribution gives you a complete picture of how your marketing channels work together to drive conversions. Unlike single-touch models that credit only the first or last interaction, MTA distributes credit across all touchpoints based on their contribution to the journey.

The key benefits:
- Accurate channel ROI that accounts for both initiating and closing contributions
- Better budget allocation based on true performance, not distorted last-touch data
- Full-funnel visibility into how channels assist each other

The main requirements:
- Journey tracking with consistent user identity
- Sufficient conversion volume (500+ per month for rule-based, more for data-driven)
- A neutral measurement system that isn't biased toward specific platforms

Start with linear attribution as a baseline, validate against your business intuition, and evolve your approach as your data maturity grows.

"I've sat in countless attribution meetings where teams were about to cut their best channels because last-touch said they weren't working. MTA doesn't just give you better data—it stops you making decisions that starve your funnel."

Holly Henderson

Co-Founder, mbuzz

Holly Henderson is Co-Founder of mbuzz. With 10+ years in marketing including roles at Westpac, Avon, and Forebrite, she's obsessed with making measurement actually useful.

Harvard Extension School Forebrite Westpac Avon