> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getinteract.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Attribution setup
> Configure your BigQuery paths table, column mapping, attribution models, spend data, and scheduling for multi-touch attribution.
Attribution settings are split across three pages, all under **Insights → Settings → Attribution**:
| Page | What it covers |
| ------------------- | ----------------------------------------------------------------------------------------------- |
| **Core settings** | Paths table, column mapping, model selection, label splits, scheduling |
| **Optional tables** | Spend data (for ROAS/CPA), performance data (for revenue coverage), and the credit output table |
| **Run history** | Log of all past runs with status, duration, and trigger type |
***
## Core settings
### Paths table
This is the BigQuery table that contains the customer journey touchpoints. Each row should represent a single marketing touchpoint within a journey.
| Field | Description |
| --------------------- | -------------------------------------------------------------------------------------------------------- |
| **Dataset** | BigQuery dataset containing the paths table |
| **Table** | Table name |
| **Channel column** | Column that identifies the marketing channel (e.g. `channel`, `source_group`) |
| **Journey ID column** | Column uniquely identifying each customer journey (default: `journey_id`) |
| **Converted column** | Boolean or 0/1 column indicating whether the journey converted (default: `converted`) |
| **Revenue column** | Revenue value per journey (default: `revenue`) |
| **Date column** | Column used as the time window filter — typically the journey start date (default: `journey_start_date`) |
After saving, Interact validates all column names against your BigQuery table schema and highlights any that cannot be found.
### Path processing
| Setting | Description |
| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Junk channels** | Comma-separated channel values to exclude before processing (e.g. `Direct, Other, (none)`). These are removed from journey paths before the model runs. |
| **Passthrough channels** | Channels that don't interrupt the journey logic (e.g. brand search that always appears). Kept in the path but treated differently in Markov transitions. |
| **Deduplicate consecutive** | Remove back-to-back touches from the same channel within a journey. Recommended for most setups. |
| **Max journey length** | Maximum number of touchpoints per journey. Longer journeys are truncated. Default: 15. |
| **Min journey length** | Minimum number of touchpoints required. Shorter journeys are excluded. Default: 1. |
### Attribution models
Enable one or both models:
| Model | When to use |
| ------------------------ | --------------------------------------------------------------------------------------------------------------------- |
| **Markov** (recommended) | Fast, reliable, good for operational use. Always enable this. |
| **Shapley** | More thorough game-theory approach. Enable when you need to compare or validate Markov results. Adds processing time. |
### Label splits
Label splits allow you to run attribution separately for each value of a segmentation dimension — for example, running separate models for `NL` and `DE` rather than blending all markets together.
Enable **Label splits** and define one or more split columns. Each split column maps to:
* A column in the **paths table** (required)
* Optionally, a corresponding column in the **spend table** for filtering spend data per segment
When label splits are enabled, the dashboard shows a multi-select filter so you can view results by specific segment combinations.
Use label splits for any dimension where channels differ meaningfully across segments — market, brand, or product line are common examples.
### Scheduling
Automatically run attribution on a schedule so results are always fresh.
| Setting | Description |
| --------------------- | ----------------------------------------------- |
| **Enable scheduling** | Toggle automatic runs on or off |
| **Frequency** | Daily or weekly |
| **Weekday** | For weekly runs, which day to run |
| **Time** | Run time in HH:MM (server time) |
| **Advanced (cron)** | Custom cron expression for fine-grained control |
When scheduling is enabled, Interact queues runs for the standard preset windows (90d, 180d, 365d) at the scheduled time.
***
## Optional tables
### Spend data
Connect a BigQuery table containing channel spend by date to unlock **ROAS** and **CPA** metrics in the attribution dashboard.
| Field | Description |
| ------------------- | --------------------------------------------------------------------------------- |
| **Dataset / Table** | Location of the spend table |
| **Channel column** | Column identifying the marketing channel — should match values in the paths table |
| **Spend column** | Column containing the spend amount (default: `spend`) |
| **Date column** | Column with the spend date (default: `date`) |
| **Label column** | (Optional) Column for label-based filtering when label splits are enabled |
### Performance data
Connect a business-system revenue table (e.g. from your e-commerce platform or CRM) to display **coverage** — what percentage of actual revenue is captured in your attribution paths.
| Field | Description |
| ------------------- | --------------------------------------------------- |
| **Dataset / Table** | Location of the performance/revenue table |
| **Revenue column** | Column with the revenue figure (default: `revenue`) |
| **Date column** | Date column for filtering to the selected window |
### Credit output table
If your pipeline pre-computes per-touchpoint attribution credit and writes it to BigQuery, configure this table to power the **Journey Attribution** page.
| Field | Description |
| ------------------ | ------------------------------------------------------------------------------------ |
| **Dataset** | Dataset containing the credit table (default: `src_attribution_models`) |
| **Table** | Credit table name (default: `fact_attribution_credit__user_id`) |
| **Channel column** | Channel column in the credit table (e.g. `channel`, `source_group`, `channel_group`) |
The credit table is written by a separate pipeline process and is not produced by the interactive attribution runs. It enables the per-journey detail view but is not required for the main channel attribution dashboard.
***
## Run history
The **Runs** page shows all past attribution runs with:
* **Status** — Success, Failed, or Queued
* **Window** — Date range that was processed
* **Label** — Segment label (if label splits are used)
* **Duration** — How long the run took
* **Trigger** — Manual, Scheduled, or Preview
* **Error** — Failure message when status is Failed
Use **Run all presets** to queue fresh runs for the standard 90d, 180d, and 365d windows. This is useful after changing the paths table or model settings.
***
## BigQuery permissions
The service account used by your BigQuery connector needs the following permissions:
| Permission | Why |
| ------------------------------------------------------- | ------------------------------------------------ |
| `bigquery.tables.getData` | Reading the paths, spend, and performance tables |
| `bigquery.jobs.create` | Running queries to build transition matrices |
| `bigquery.tables.create` + `bigquery.tables.updateData` | Writing channel weights output (when enabled) |
The standard `roles/bigquery.dataEditor` + `roles/bigquery.jobUser` roles cover all of these.