Multi-Touch Attribution
| Model | Strength | Weakness |
|---|---|---|
| Linear | Easy to explain | Dilutes high-impact touches |
| Time decay | Values recency | Underweights discovery touches |
| W-shape | Highlights first/lead/last | Assumes fixed journey |
| Data-driven (algorithmic) | Learns actual paths | Needs volume and clean data |
💡TL;DR
Multi-touch attribution (MTA) explains where credit should go across the journey. It is directionally useful but not causal; pair it with experiments for budget moves. Pick models that match your motion—time decay for short cycles, algorithmic for high volume, W-shape for B2B journeys.
Definition
An attribution approach that distributes credit across multiple touchpoints in the buyer journey rather than only first or last touch.
🏢What This Means for SMB Teams
SMBs can start with a simple model, then validate major channels via holdout tests before scaling spend.
📋Practical Example
A 55-person medical device vendor ($32M revenue) marketed to hospital procurement teams through webinars, field demos, and specialist journals. They replaced last-touch with a data-driven MTA that ingested webinar attendance, demo requests, rep visits, and journal QR scans. Before: webinar-heavy credit pushed 48% of spend to events; lead-to-opportunity 14%. After 120 days, the model showed rep visits and journal scans drove 2.1× higher close rates. Budget shifted 10% from webinars to rep travel; lead-to-opportunity rose to 19% and quarterly bookings increased from $7.4M to $8.5M.
🔧Implementation Steps
- 1
Unify touchpoint IDs across web, events, email, and offline scans; require timestamps and account IDs.
- 2
Pick a starter model that matches volume: time decay for low volume, data-driven for 1k+ touches/month.
- 3
Add channel suppression rules (e.g., cap credit from retargeting at 25%) to reduce bias.
- 4
Validate the model quarterly with holdouts or geo tests on top spend channels.
- 5
Publish a simple scorecard showing contribution, cost per influenced deal, and evidence level for each channel.
❓Frequently Asked Questions
Which MTA model should we start with for low-volume B2B data?
Use time-decay or position-based (W-shape) until you collect enough paths for algorithmic models. These provide direction without overfitting small datasets.
How do we handle offline touches like rep visits?
Log every visit with time, attendee, and intent code in CRM, then sync to the attribution model. Without consistent IDs and timestamps, offline touches will be under-credited.
⚡How Optifai Uses This
Optifai supports W-shape and data-driven MTA, then overlays causal lift for decisioning.
📚References
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Related Terms
Revenue Attribution
Methods for assigning revenue credit to marketing and sales interactions. Modern practice blends multi-touch models with causal tests to validate lift.
Revenue Attribution Model
A causal-first framework that attributes incremental revenue to specific plays and signals using holdouts or geo splits, not just multi-touch click weights.
ROI Ledger
A ledger system that tracks every AI action with a UUID and attributes actual revenue contribution using holdout testing.
Experience Revenue Action in Practice
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