analytics-attribution

Attribution tracking requires implementing UTM parameters on campaign links, integrating data from your analytics platform and CRM, and selecting an attribution model (last-click, first-click, linear, time-decay, or data-driven) that matches your funnel. The Skill provides frameworks and templates to automate this setup across channels, devices, and geographies.

Last-click attribution credits only the final touchpoint before conversion, while data-driven attribution uses machine learning to weight each touchpoint based on its actual influence. Data-driven models reveal true channel contribution but require more historical data. This Skill lets you compare both to identify which model suits your campaigns.

ROI, CAC, and LTV calculations require attributing revenue to campaigns, tracking acquisition costs by channel, and modeling lifetime value from cohorts. This Skill automates these calculations across dimensions like channel, device, and geography, then exports results to dashboards and reports for performance analysis.

Yes. Multi-touch attribution requires connecting online touchpoints to offline conversions through CRM data integration. This Skill supports offline integration and cross-domain tracking to unify online and offline customer journeys in a single attribution and ROI model.

Proper attribution requires clean UTM discipline (consistent parameter naming), synchronized data from analytics and CRM systems, aligned KPI definitions across teams, and documented tracking rules. This Skill includes governance frameworks and exportable templates to standardize these before modeling begins.

Last-click and linear models work immediately with minimal data, but data-driven attribution models require sufficient historical conversion volume to train accurately. This Skill supports all model types, so you can start simple and graduate to data-driven as your dataset grows.