Marketing attribution that runs inside your own data warehouse
AttriByte is the category creator for warehouse-native attribution. Six models run directly in Snowflake, BigQuery, Redshift, or Postgres. Your raw data never leaves your environment. Your data team keeps control.
The category
What warehouse-native attribution means
Traditional attribution tools copy your data into a vendor's cloud. Warehouse-native attribution is different: the attribution models run inside your own warehouse, on your own compute, against your own tables.
When you connect AttriByte to your Snowflake, BigQuery, Redshift, or Postgres instance, it creates a set of managed schemas in your warehouse. Touchpoint events, CRM records, ad spend, and conversion data all land in tables that belong to your account. AttriByte then pushes attribution SQL into your warehouse's query engine, computes model results, and writes them back to output tables, also in your account.
Nothing is copied to AttriByte's servers for computation. The only data AttriByte receives is the aggregated dashboard payload needed to render your reports. Raw event rows, PII, and spend figures stay in your warehouse.
This architecture was designed specifically for B2B SaaS and enterprise companies where the data team is an active stakeholder in every vendor evaluation. It passes data governance reviews that traditional attribution SaaS tools routinely fail.
The output: six attribution models (first-touch, last-touch, linear, time-decay, U-shaped, and W-shaped) computed in parallel and available as queryable tables in your warehouse from day one.
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. A tool that pulls data into a vendor silo fails their review. Warehouse-native architecture passes the data team's checklist by default: no new data copies, no PII egress, full lineage.
Data residency and compliance
GDPR, CCPA, and sector-specific regulations increasingly require that personal data stay within defined geographic and organisational boundaries. When attribution runs in your warehouse, residency is determined by where you provisioned that warehouse, not where a vendor hosts their SaaS.
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 attribution in your warehouse
Setup takes minutes, not weeks. Connect your warehouse once with an OAuth flow or service account. AttriByte creates its schemas, installs the attribution models as compiled SQL, and begins listening for incoming touchpoint events via the JavaScript pixel or server-side SDK.
AttriByte's cookieless identity layer stitches anonymous sessions to known contacts using first-party deterministic signals: hashed email, CRM ID, login events, and form submissions. The stitched journey is written to your warehouse. Attribution is then computed against that clean, resolved journey, not against fragmented anonymous sessions.
Atlas, AttriByte's AI analyst, reads the attribution results directly from your warehouse tables to answer questions in plain English. It cites every join and aggregation it used. Your raw data is never sent to the AI model.
Setup sequence
- 1
Connect your warehouse
OAuth or service account. Read + write to your AttriByte schema only.
- 2
Install the pixel or SDK
One-line JS snippet or server-side event forwarding. Events land in your warehouse.
- 3
Map your conversion events
Point AttriByte at your MQL, SQL, or Opportunity creation table.
- 4
Run attribution
All six models compute in your warehouse. Results are queryable in minutes.
- 5
Query and activate
Join attribution tables to any other dataset. 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?
Warehouse-native attribution means the entire attribution computation runs inside your own data warehouse (Snowflake, BigQuery, Redshift, or Postgres) rather than on a vendor's servers. Your raw event data, CRM records, and spend data never leave your environment. AttriByte connects to your warehouse via OAuth or a service account, pushes down SQL, and writes results back to tables you own.
How is this different from traditional attribution tools?
Traditional attribution tools like Google Analytics, Rockerbox, or Northbeam pull your data into their own cloud. You get a dashboard but lose control of the underlying data. With warehouse-native attribution, the source of truth stays in tables you already own and govern. You can query those tables directly, join them against any other dataset, and audit every number.
Do I need a data engineer to set it up?
No. AttriByte's setup wizard handles warehouse permissions, schema creation, and connector configuration without SQL knowledge. Data teams can optionally inspect or extend the generated models, but setup is designed for marketers and revenue ops teams to complete independently.
Which warehouses are supported?
AttriByte supports Snowflake, BigQuery, Redshift, and Postgres natively. Each integration uses the warehouse's own query engine, so there is no middleware or proprietary data format between your data and your results.
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 in the same warehouse job, so you can compare them side-by-side without re-running queries or switching tools.
Is my data shared with any AI model?
No raw data is sent to any AI model. Atlas, AttriByte's AI analyst, operates on aggregated query results only. The underlying event rows and PII stay in your warehouse.
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.