Which attribution model should you use as a default?

If you're just starting: position-based (U-shape: 40% first, 40% last, 20% middle) — a reasonable balance between awareness and conversion. If you have more than 1,000 conversions/month: data-driven (GA4 offers this natively). Last-touch alone is the worst choice in B2B, where cycles are long and multi-touchpoint.

Attribution vs MMM (Marketing Mix Modeling): what's the difference?

Attribution = micro, user-level, based on cookies / identifiers — weakened by iOS 14 and GDPR. MMM = macro, aggregate-level, based on spend ↔ revenue time series — resistant to tracking loss, but requires 18–24 months of data. The modern practice combines both, plus incrementality tests (holdout, geo-lift).

Glossary

Marketing attribution model

A marketing attribution model is the rule that determines how to assign credit for a conversion among the multiple touchpoints a prospect encountered before buying — first-touch, last-touch, linear, time-decay, position-based (U-shape), or data-driven (algorithmic).

Also known as

attribution model
attribution model
multi-touch attribution
MTA

The problem: a B2B buyer touches an average of **8 to 13 touchpoints** before converting (LinkedIn, Google Ads, SEO blog, podcast, demo, nurturing email…). Attributing 100% of the conversion to the last click (last-touch — the historical Google Analytics default) massively undervalues top-of-funnel awareness and leads to over-investment in bottom-of-funnel.

The 6 classic models: (1) **First-touch** (100% to the first contact — favors awareness), (2) **Last-touch** (100% to the last — favors conversion), (3) **Linear** (equal weight shared across all touchpoints), (4) **Time-decay** (increasing weight toward conversion), (5) **Position-based / U-shape** (40% first, 40% last, 20% middle), (6) **Data-driven / algorithmic** (ML model that learns the true weights from your conversion data — the modern approach, natively supported by GA4, Adobe, and some CDP tools). Forrester / Bizible research: brands using data-driven attribution reallocate an average of 15–30% of their marketing budget within the first 6 months, with a cumulative ROI lift of +20–40%. 2026 caveat: cross-device tracking loss and the effects of iOS 14 / GA4 / GDPR have weakened deterministic attribution — the modern practice combines attribution + **MMM (Marketing Mix Modeling)** + incrementality studies (geo-lift, holdout tests).

In the getchatsocial.com product

getchatsocial.com integrates with Brandyze's `attribution_run_model`, `attribution_get_journey`, and `attribution_pixel_snippet` (multi-touch attribution for e-commerce) and surfaces per-channel attribution insights directly in the chat.

FAQ

Which attribution model should you use as a default?
If you're just starting: position-based (U-shape: 40% first, 40% last, 20% middle) — a reasonable balance between awareness and conversion. If you have more than 1,000 conversions/month: data-driven (GA4 offers this natively). Last-touch alone is the worst choice in B2B, where cycles are long and multi-touchpoint.
Attribution vs MMM (Marketing Mix Modeling): what's the difference?
Attribution = micro, user-level, based on cookies / identifiers — weakened by iOS 14 and GDPR. MMM = macro, aggregate-level, based on spend ↔ revenue time series — resistant to tracking loss, but requires 18–24 months of data. The modern practice combines both, plus incrementality tests (holdout, geo-lift).