Multi-touch vs single-touch attribution

Attribution models fall into two families:

• Single-touch — all credit to one touchpoint (either the first or the last).
• Multi-touch — credit split across multiple touchpoints.

First-touch and last-touch are on every plan. Linear and time decay are on Starter and higher.

This article explains when to reach for each.

Single-touch models

First-touch and last-touch are both single-touch. They’re simple, intuitive, and famously wrong in opposite ways.

• First-touch over-credits the channel that opens the door.
• Last-touch over-credits the channel that happens to be the last URL the customer clicked before buying.

When single-touch is fine

• You spend 90%+ of your budget on one channel (e.g. all Meta). No multi-channel story to tell.
• Your typical customer has 1-2 touches before converting. Short funnels don’t need fancy allocation.
• You need a simple, explainable number for a stakeholder who doesn’t want to hear about half-credits.

Multi-touch models

Linear splits credit evenly across the distinct touches in the journey — each different source and campaign gets an equal share, so clicking the same ad twice doesn’t double its credit. Time decay weights recent touches higher.

When multi-touch is worth it

• You run multiple channels (Meta + Google + email + influencer) and want to see how they stack.
• Customer journeys are long — your typical buyer touches 4-6 UTMs before converting, often across weeks.
• You’re trying to discover undervalued channels. A channel that rarely appears as first or last touch can still show meaningful linear-attributed revenue if it shows up mid-funnel frequently.

Worked comparison

Pretend a customer had this journey:

1. Podcast sponsorship link (utm_medium=podcast) — day -14
2. Meta retargeting ad (utm_source=facebook, medium=paid) — day -3
3. Email with discount (utm_source=email) — day 0 (ordered $100)

Single-touch:

• First-touch: Podcast $100, Meta $0, Email $0
• Last-touch: Podcast $0, Meta $0, Email $100

Multi-touch:

• Linear: Podcast $33, Meta $33, Email $34
• Time decay: Podcast $13, Meta $37, Email $50

The podcast gets zero credit in last-touch, 100% in first-touch, and a meaningful share in linear. If you’re deciding whether to renew the podcast sponsorship, single-touch gives you a badly distorted answer; multi-touch at least acknowledges the channel mattered.

Which should I use for day-to-day reporting?

No single answer. Most teams we see settle into one of these patterns:

• Last-touch for reporting, first-touch for a sanity check. Pick last-touch as your canonical model (it matches what most ad platforms report), and occasionally switch to first-touch when evaluating awareness channels.
• Linear all the time. Treats every channel fairly. Good for teams who don’t want to argue about model choice — pick one and move on.
• Time decay for performance, first-touch for brand. Time decay rewards channels that drove urgency; first-touch rewards channels that introduced the customer. Report on both.

The model is one shared preference across Ordinary: switch it on Marketing Mix (or a campaign’s detail page) and every report follows. Switch back and forth to see how the same numbers move under each model.

The model is not ground truth

Important caveat: no attribution model is correct. They’re heuristics for dividing up credit among channels that all contributed somehow. The “truth” — did the customer buy because of channel X? — is unknowable without randomized experiments.

Use attribution models for relative channel comparisons and directional planning. Don’t treat them as proof that channel X generated exactly $Y — that’s not what they measure.

For causal claims, run an experiment — Ordinary’s built-in A/B testing (variants with a randomized control) measures the real lift attribution can’t. See A/B testing and experiments.

Related articles

• Attribution models explained — per-model definitions and examples.
• Attribution reports — where you pick the model.
• Why channel and campaign totals don’t add up — the one source of confusion that surprises every operator the first time they see it under linear or time-decay.