What is Multi-Touch Attribution
What is Multi-Touch Attribution?
Multi-touch attribution (MTA) is a marketing measurement model that assigns credit for a conversion to multiple touchpoints across a customer’s journey, instead of just the first or last. It aims to provide a more complete view of what influenced a customer to buy—recognizing that conversions rarely happen after just one interaction.
By accounting for all meaningful engagements—email clicks, social ads, organic visits, retargeting, and more—MTA helps marketers better understand the true path to purchase and optimize their media mix accordingly.
Why Multi-Touch Attribution Exists
In today’s landscape, customers interact with brands across multiple channels—sometimes over days or weeks—before converting. A podcast may spark interest, an Instagram ad may build trust, and a Google search might close the deal.
Single-touch models like last-click or first-click ignore these complex journeys. MTA tries to solve that by spreading credit across multiple touchpoints, making it easier to:
Understand the real drivers of conversion
Avoid over-investing in “closer” channels like branded search
Justify upper-funnel spend like influencer or awareness campaigns
Common Multi-Touch Attribution Models
1. Linear Attribution
Distributes credit equally across all touchpoints. Simple and fair, but may dilute the importance of key interactions.
2. Time Decay
Gives more weight to recent touchpoints leading up to the conversion. Reflects urgency but may undervalue early awareness drivers.
3. U-Shaped (Position-Based)
Heavily weights the first and last interactions (typically 40% each), splitting the remaining 20% among the middle touches.
4. Custom/Algorithmic Models
Uses machine learning to assign weights based on channel performance. Promising, but often a black box and requires clean, high-volume data.
The Real-World Challenge: MTA Has Blind Spots
While MTA sounds ideal, it breaks down in practice for many marketers—especially those investing in channels without deterministic tracking. For example:
Podcasts, TV, and direct mail often don’t generate a trackable click or impression.
Word of mouth or dark social rarely shows up in analytics.
Privacy restrictions (like iOS 14+) and cookie loss have made cross-device attribution unreliable.
Clickstream data is incomplete and can’t capture qualitative influence like sentiment or intent.
So while MTA attempts to reflect the whole journey, it still misses key drivers of conversion.
Why Fairing Helps
Fairing fills the gaps left by MTA by capturing self-reported attribution—straight from your customers—at the moment of purchase.
Post-purchase surveys ask: “How did you hear about us?”
Open-ended or structured responses reveal true influence across online and offline channels.
Complement your MTA models by layering zero-party data on top of clickstream analysis.
Expose top-of-funnel touchpoints like podcasts, influencer campaigns, OOH, or PR that MTA tools can’t see.
This hybrid model—quantitative + qualitative—helps marketers make smarter decisions, faster.
How to Combine MTA and Survey Data
Many of Fairing’s customers use this blended approach:
Use MTA to evaluate performance of click-based digital channels.
Use Fairing to identify which untracked channels spark discovery and drive consideration.
Align budgets with both click-based performance and customer-reported influence.
Create benchmarks over time to understand shifts in attribution by channel, campaign, or cohort.
The result: full-funnel visibility without over-reliance on incomplete data.
FAQs
Does MTA replace other attribution models?
No—it supplements them. It’s more nuanced than single-touch, but not always more accurate. Combining MTA with Fairing gives you both breadth and depth.
What tools do I need for MTA?
You’ll need a platform that tracks user journeys across sessions and channels. But even the best tools can’t track podcasts or dark social—which is where Fairing comes in.
How accurate is MTA?
It depends on your data quality and touchpoint visibility. If major channels don’t generate clicks, MTA will miss them. That’s why survey data is essential.
TL;DR: MTA Alone Isn’t Enough
Multi-touch attribution offers a more holistic look at the customer journey—but it still relies on trackable data. In a world where awareness often comes from unclickable sources, MTA falls short. Fairing fills those gaps with customer-reported insights, giving you a truer picture of what’s driving growth.
👉 Add zero-party insights to your MTA stack. Try Fairing today
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