Multi-Touch Attribution
Multi-Touch Attribution (MTA) is a marketing measurement approach that assigns fractional credit for conversions or revenue to multiple touchpoints across the buyer journey — instead of giving 100% credit to just the first or last interaction.
A $100K deal has 5 touchpoints before close: paid ad, webinar, email, case study, demo. Under different models: First-Touch credits $100K to the paid ad. Last-Touch credits $100K to the demo. Linear credits $20K to each. Position-Based might credit $40K each to paid ad and demo, $6.67K to middle touches. The company uses W-Shaped, giving 30% each to ad (first), webinar (lead creation), and demo (opportunity creation), 10% split between email and case study.
Multi-touch attribution is either a breakthrough insight or a complicated way to justify whatever the loudest team wants to claim credit for. The difference is whether you're measuring influence or manufacturing narratives.
The hard truth: most B2B companies don't have the data infrastructure to do MTA well. You need: contacts properly associated to opportunities in your CRM, consistent UTM tracking across all channels, a defined lookback window, clear rules for what counts as a "touch." Without those, you're building models on garbage data.
Start with simple models (linear, position-based) before investing in algorithmic. And remember: attribution should inform decisions, not win internal credit wars.
Define ItOther Definitions
“Multi-touch attribution (MTA) is a marketing measurement methodology that assigns credit to multiple touchpoints along the customer journey, providing a more comprehensive view of which channels and campaigns contribute to conversions.”
“Multi-touch attribution uses statistical or machine-learning methods to learn how much each touchpoint increases the probability of conversion and assigns data-driven fractional credit accordingly.”
“Multi-touch attribution is a measurement technique that evaluates and assigns credit to all touchpoints in the buyer's journey, from first interaction to conversion, enabling marketers to understand how different marketing channels work together.”
Multi-touch attribution distributes conversion credit across all significant touchpoints rather than crediting only the first or last interaction. Salesforce emphasizes the comprehensive view of channel contribution. Nielsen highlights statistical and ML-based credit allocation. Adobe focuses on understanding how channels work together.
Common MTA models include: (1) Linear — equal credit to all touches; (2) Time-Decay — more credit to touches closer to conversion; (3) Position-Based (U-Shaped) — higher weight to first and last touches; (4) W-Shaped — extra credit to first touch, lead creation, and opportunity creation; (5) Algorithmic/Data-Driven — ML-based credit based on actual influence.
For B2B, W-shaped and algorithmic models often work better because they account for the multiple milestone moments in complex buying journeys.
MistakesCommon Mistakes
Poor contact-to-opportunity association in CRM causing missed touches
Inconsistent UTM tracking making touch data unreliable
Using overly long lookback windows that credit irrelevant old touches
Relying on last-touch in complex B2B journeys with long sales cycles
Building attribution without defining what counts as a qualifying touch
Is your attribution informing decisions or just winning credit wars?
We build attribution models on clean data foundations — so you know what's actually driving pipeline and revenue.
Experience across
