Compare first-click, last-click, linear, time-decay, and position-based attribution models. See how each model distributes credit across marketing channels.
Attribution models determine how credit for a conversion is distributed across the marketing touchpoints in a customer's journey. Different models can dramatically change which channels appear most valuable, directly affecting where you allocate budget.
This calculator lets you enter a customer journey with multiple touchpoints and see how revenue credit shifts between first-click (gives all credit to first interaction), last-click (gives all to the last), linear (equal credit to all), time-decay (more credit to recent touchpoints), and position-based (40% first, 40% last, 20% middle).
Understanding attribution is critical because no single model is "correct." Each illuminates a different aspect of channel performance. Comparing them side by side reveals which channels are over- or under-valued by your default model. Whether you are a beginner or experienced professional, this free online tool provides instant, reliable results without manual computation. By automating the calculation, you save time and reduce the risk of costly errors in your planning and decision-making process.
Most analytics platforms default to last-click attribution, which systematically undervalues awareness channels (social, display) and overvalues closing channels (branded search, email). This calculator shows the discrepancy so you can make more balanced budget decisions. Having a precise figure at your fingertips empowers better planning and more confident decisions. Manual calculations are error-prone and time-consuming; this tool delivers verified results in seconds so you can focus on strategy.
First-Click: 100% to touchpoint 1 Last-Click: 100% to last touchpoint Linear: 100% / N to each touchpoint Time-Decay: Weighted by recency (half-life decay) Position-Based: 40% first + 40% last + 20% split among middle
Result: First: $200/$0/$0/$0 | Last: $0/$0/$0/$200 | Linear: $50 each
A $200 order with 4 touchpoints: First-click gives all $200 to the first channel. Last-click gives all $200 to the last. Linear gives $50 to each. Position-based gives $80/$20/$20/$80. Time-decay gives roughly $25/$35/$55/$85 depending on decay rate.
Modern customer journeys involve an average of 6–8 touchpoints across multiple channels before a purchase. A customer might see a Facebook ad, click an Instagram story, read a blog post via Google, and finally convert from an email. Which channel "caused" the sale? Attribution models attempt to answer this question.
First-click favors awareness channels (social, display). Last-click favors conversion channels (email, branded search). Linear treats all equally. Time-decay favors recency. Position-based balances discovery and closing. Comparing all five reveals which channels your default model over- or under-credits.
Data-driven attribution (DDA) uses machine learning to analyze thousands of customer journeys and determine the actual contribution of each touchpoint. It is more accurate than any rules-based model but requires sufficient data volume (typically 3,000+ conversions per month) to produce reliable results.
There is no single best model. Position-based is a good default for e-commerce because it values both discovery and closing. Data-driven attribution (DDA) is the most sophisticated approach, using machine learning to weight touchpoints based on their actual contribution.
If last-click shows email drives 40% of revenue and social drives 5%, you might cut social budget. But first-click might show social drives 25% of initial discovery. Without comparing models, you might cut the channel that feeds your entire funnel.
Time-decay gives more credit to touchpoints closer to the conversion. A touchpoint 7 days before purchase gets roughly half the credit of one 1 day before. It assumes recent interactions have more influence on the final decision.
GA4 offers data-driven attribution by default. For custom implementation, you need customer journey data (all touchpoints per user) stored in a data warehouse, then apply the model logic. Tools like Rockerbox, Triple Whale, and Northbeam specialize in e-commerce attribution.
Yes. iOS 14.5+ App Tracking Transparency reduced cross-app tracking, making it harder to attribute social media conversions. Server-side tracking, Conversions API, and probabilistic matching help recover some accuracy.
Attribution distributes credit for observed conversions. Incrementality measures whether a channel caused conversions that would not have happened otherwise (using holdout tests). Incrementality is more accurate but harder and more expensive to measure.