Attribution Snapshot
A fast, directional read on which channels look closer to revenue when you compare first-touch, last-touch, linear, and time-decay models side by side.
This is not a full attribution warehouse. It is a practical operator tool for spotting where the story changes depending on the model, where budget confidence is stronger, and where your reporting setup is still too thin to trust.
Import a lightweight dataset or start with demo journeys.
Phase 1 stays intentionally simple: Google Ads CSV, Meta Ads CSV, or demo data. The goal is to make the model spread visible fast, then route the deeper data problem into the right service conversation.
Input source
Choose a CSV template
Template
Use this column structure.
Interpretation
Read it like an operator, not an attribution vendor.
Use this when the channel story feels fuzzy.
Most SMB teams are not missing a prettier dashboard. They are missing a trustworthy story about which channels create demand, which ones assist, and where reporting confidence breaks down. Attribution Snapshot makes that ambiguity visible fast.
Use the output to scope the real fix.
If the models roughly agree, the next move may be budget or channel focus. If they disagree sharply, the next move is usually better tracking, cleaner CRM structure, and reporting architecture you can actually run from.
Want the real measurement version of this?
I can turn a directional read like this into a concrete reporting and attribution plan tied to your CRM, conversion flow, and actual operating decisions.