Credit every channel by what it actually drove

Attribution modeling is the practice of crediting each marketing channel by its true contribution to a sale, instead of handing all the credit to the last click. We capture every touchpoint — ad, email, search, direct — stitched to one identity, then apply a model that distributes credit across the whole journey. The engagement covers identity and event capture, model selection and fit, credit distribution, incrementality validation, and the channel-ROI and budget reporting that comes out of it.

We scope it by the complexity of your data — how many channels feed the journey, how clean your tracking already is, and whether we're building the data foundation or modeling on top of one that exists. MarkAge360 typically runs a fixed-fee build to get capture and modeling live, then a lighter monthly retainer to keep the model honest and the reporting current. We give you a clear scope and number before any work starts, with no per-conversion surcharges.

A first usable model is usually live within a few weeks once we have access to your analytics, ad platforms, and conversion data. Data-driven models need enough conversion volume and history to be trustworthy, so we're transparent early about whether you have the volume for one yet or should start with a rules-based model. We treat the first model as a starting point and tune it as more path data accumulates.

Last-click hands every dollar of credit to the final touch before purchase, which flatters bottom-funnel channels and starves the ads, content, and email that actually built the demand. We move you to a model that distributes credit across the path the buyer really took, so assisting channels show the contribution they earned. The result is a channel-ROI ranking you can trust to move budget, rather than one that quietly rewards the same few channels every time.

We work across GA4 and its data-driven attribution, the native attribution in Google Ads and Meta, and warehouse-level modeling in BigQuery or your data warehouse when you need control beyond what the platforms expose. For capture we lean on clean event tracking and server-side tagging so identity holds together across devices. We fit the stack to what you already run rather than forcing a rip-and-replace.

Attribution lives or dies on data you're allowed to collect, so we build capture around consent and respect the choices visitors make. As third-party cookies and signal loss erode click-level tracking, we lean on first-party events, server-side capture, and modeled paths to keep the picture honest where raw tracking goes dark. We validate the model with holdout and lift tests so credit reflects real influence rather than gaps in the data.