PPC attribution is the process of assigning credit for a conversion (sale, lead, or other action) to the advertising touchpoints that contributed to it. Different attribution models distribute this credit differently. Last-click attribution assigns 100% credit to the final ad clicked before conversion. First-click assigns 100% credit to the first ad. Data-driven attribution (Google's preferred model) uses machine learning to distribute credit across all touchpoints proportionally to their actual contribution. Attribution model choice significantly affects which campaigns appear to be performing well and therefore how budgets are allocated.
Attribution is one of the most consequential and least well-understood aspects of PPC management. Businesses running Google Ads, Meta Ads, and LinkedIn simultaneously face a multi-touch attribution problem: when a customer clicks a LinkedIn ad, then a Google Shopping ad, then searches for the brand name and clicks a Google text ad before purchasing — which channel gets credit? The answer depends on the attribution model, and that model choice determines how budget is allocated across channels.
Attribution models compared
- Last-click — 100% credit to the final click; overvalues direct and branded search; undervalues upper-funnel channels
- First-click — 100% credit to first interaction; overvalues awareness channels; undervalues conversion-stage channels
- Linear — equal credit to all touchpoints; simple but does not reflect actual conversion contribution
- Time-decay — more credit to clicks closer to the conversion; reasonable for short purchase cycles
- Position-based — 40% to first and last click, 20% distributed among middle touches
- Data-driven (DDA) — uses Google's ML to distribute credit based on actual conversion likelihood; requires sufficient conversion data (300+/month)
- Cross-channel DDA — available in GA4; models attribution across multiple platforms simultaneously
Google now defaults to data-driven attribution (DDA) for accounts with sufficient conversion data. DDA is the most accurate model available within Google Ads and should be used by accounts meeting the 300+ monthly conversion threshold. For smaller accounts, position-based or linear attribution are reasonable alternatives to pure last-click. The most important principle: use the same attribution model consistently across time — changing models makes historical performance comparison impossible.
Budget allocation follows attribution — if last-click attribution shows Google branded search driving most conversions, you might reduce LinkedIn budget that is actually driving the brand awareness creating those searches. If Meta Ads' view-through attribution shows post-view conversions you would not otherwise attribute, over-crediting Meta reduces Google and organic search budget. Getting attribution right means advertising budget flows to the channels that genuinely drive growth, not the ones that happen to be at the end of the conversion path.