Marketing attribution your data team can actually audit
Your warehouse stays the source of truth. Connect Snowflake today — BigQuery, Redshift, and Postgres in early access — and AttriByte computes six models in a managed, isolated environment you own, export, and govern. In-warehouse pushdown is on the Enterprise roadmap.
The category
What warehouse-native attribution means
Traditional attribution tools copy your data into a vendor's cloud and hand you a dashboard with no lineage. AttriByte keeps your warehouse as the source of truth: you connect it, keep ownership, and audit every credit back to the row.
When you connect AttriByte to your Snowflake instance (BigQuery, Redshift, and Postgres are in early access), it ingests the touchpoint events, CRM records, ad spend, and conversion data it needs into a managed, isolated environment. It computes the attribution models there and makes the results available to your dashboards, to Atlas, and for export back to your warehouse.
You keep ownership of everything. Export or delete your data at any time, and trace every attribution credit back to the identifiable rows behind it — no black box. Raw event data is processed in an encrypted, isolated environment, never pooled across customers.
This architecture was designed specifically for B2B SaaS and enterprise companies where the data team is an active stakeholder in every vendor evaluation — full lineage, full export, and a clear data-handling story for governance review.
The output: six attribution models (first-touch, last-touch, linear, time-decay, U-shaped, and W-shaped) computed in parallel and queryable from day one. Running that computation in-place inside your own warehouse is on the Enterprise roadmap.
Why it matters
Four reasons data-forward teams choose warehouse-native
Data team influence on martech buying
In modern B2B orgs, data teams evaluate and approve martech purchases. AttriByte gives them what they ask for: full lineage from every credit back to the row, a clear data-handling story, and export or deletion on demand — no black-box silo.
Data residency and compliance
AttriByte processes data in a managed, isolated, encrypted environment with ownership, export, and deletion controls. US region is live today; EU/UK data residency is on the Enterprise roadmap.
No vendor lock-in
Attribution results written to your own tables are portable by definition. You can query them with any BI tool, export them to a spreadsheet, or migrate to a different analytics layer without losing historical data or rebuilding dashboards from scratch.
Join attribution to any other data
Because attribution results live in your warehouse, you can JOIN them to revenue data, product usage, support tickets, or any other table. No API calls, no CSV exports: the full picture in a single query.
How it works
How AttriByte runs your attribution
Connect your warehouse once with an OAuth flow or service account. AttriByte ingests the data it needs into a managed, isolated environment and begins listening for incoming touchpoint events via the JavaScript pixel or server-side SDK.
AttriByte's identity layer stitches anonymous sessions to known contacts using first-party deterministic signals — hashed email, CRM ID, login events, and form submissions — first-party by default, with an opt-in cookieless mode and GPC/DNT honored. Attribution is computed against that clean, resolved journey, not against fragmented anonymous sessions.
Atlas, AttriByte's AI analyst, answers questions in plain English from the attribution results, citing every join and aggregation it used. Your raw event rows are not sent to the AI model.
Setup sequence
- 1
Connect your warehouse
OAuth or service account, scoped to read access. Snowflake today; others in early access.
- 2
Install the pixel or SDK
One-line JS snippet or server-side event forwarding.
- 3
Map your conversion events
Point AttriByte at your MQL, SQL, or Opportunity creation event.
- 4
Run attribution
All six models compute in parallel. Results are queryable in minutes.
- 5
Query and activate
Export results to your warehouse and push segments to Meta, Google, LinkedIn, or HubSpot via reverse-ETL.
Side by side
Warehouse-native vs vendor-managed attribution
Both approaches report attribution numbers. Here is what changes when the compute moves into your warehouse.
Supported warehouses
One attribution layer, four warehouses
AttriByte supports every major cloud data warehouse. The attribution models are the same regardless of which warehouse you use.
Ready to connect your stack? See the full product or review pricing.
FAQ
Warehouse-native attribution: common questions
What is warehouse-native attribution?
It means your data warehouse is the source of truth for attribution, not a black-box vendor silo. You connect your warehouse (Snowflake today; BigQuery, Redshift, and Postgres in early access); AttriByte ingests the events, CRM records, and spend it needs into a managed, isolated environment, computes all six models there, and gives you full ownership, export, and deletion. Running the computation in-place inside your own warehouse is on the Enterprise roadmap.
How is this different from traditional attribution tools?
Traditional attribution tools pull your data into their cloud and give you a dashboard with no lineage and no export. AttriByte keeps your warehouse as the system of record: you connect it, you keep ownership, and you can export or delete everything at any time. Every credit traces back to identifiable rows, so your data team can audit every number rather than taking a dashboard on faith.
Do I need a data engineer to set it up?
No. AttriByte's setup wizard handles the warehouse connection and event mapping without SQL knowledge. Data teams can optionally inspect or extend the models, but setup is designed for marketers and revenue ops teams to complete independently.
Which warehouses are supported?
Snowflake is live today. BigQuery, Redshift, and Postgres connectors are in early access. You can also start with no warehouse at all — connect one later when you are ready.
Can I run multiple attribution models at once?
Yes. AttriByte runs six models in parallel: first-touch, last-touch, linear, time-decay, U-shaped, and W-shaped. All six are computed together, so you can compare them side-by-side without re-running queries or switching tools.
Is my data shared with any AI model?
Atlas, AttriByte's AI analyst, operates on aggregated query results — your raw event rows are not sent to the model.
Attribution that lives in your warehouse, not ours.
Connect your Snowflake, BigQuery, Redshift, or Postgres instance and run six attribution models on your own data in minutes.