> For the complete documentation index, see [llms.txt](https://docs.bloomanalytics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bloomanalytics.io/marketing/marketing-attribution.md). # Marketing Attribution Bloom's Marketing Attribution uses first-party data from your Shopify storefront to accurately track which channels, campaigns, and ads generate revenue. #### What is Marketing Attribution? Marketing Attribution in Bloom helps Shopify merchants understand the complete customer journey, from the first touchpoint to the final purchase. It tracks paid and organic marketing channels using first-party data collected directly from your storefront, so you can identify which channels, campaigns, ad sets, and individual ads are actually generating revenue. This lets you allocate your ad budget with confidence rather than relying on inflated platform-reported numbers. #### How it Works Bloom Marketing Attribution combines first-party data collection with configurable attribution rules to assign conversion credit accurately. **First-party Data** Bloom uses first-party data collected directly from your Shopify storefront to power attribution, rather than relying on data reported by advertising platforms. Ad platforms have a natural incentive to over-report their own contribution, and each measures only its own performance while ignoring overlap with other channels. This can result in $150,000 of attributed revenue reported across ad platforms when your actual Shopify revenue was only $100,000. Because Bloom reconciles attribution against your real Shopify order data, the numbers reflect what actually happened. **Attribution Models** An attribution model is the set of rules that decides which touchpoints in a customer journey receive credit for a conversion. Bloom supports six models: Last Non-Direct Click, Last Click, First Click, Any Click, Linear, and Linear (Paid Only). Each tells a different story about the same journey, so the right model depends on what decision you are trying to make.


Models	How It Works
Last Non-Direct Click	Gives 100% credit to the last non-direct channel clicked. Direct traffic is ignored.
Last Click	Gives 100% credit to the last channel clicked.
First Click	Gives 100% credit to the first channel clicked.
Any Click	Gives 100% credit to every channel that was clicked.
Linear	Distributes credit equally across all clicks in the customer journey.
Linear (Paid Only)	Distributes credit equally across all paid-channel clicks.

**Lookback Windows** A lookback window controls how far back Bloom looks for customer interactions before a conversion when assigning credit. Available windows are 1, 7, 14, 30, and 90 days. Shorter windows favor bottom-of-funnel channels, while longer windows give upper-funnel prospecting channels fair credit. #### Data Available The Marketing Attribution page is the central hub for reviewing all attribution data. Every metric on the page updates simultaneously when a filter is changed, so you can compare performance across channels, campaigns, ad sets, or individual ads accurately. **Filters and Dimensions** Four filter levels narrow the data to specific marketing activity.

| **Filter** | **What it is** | | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Channel | The marketing platform or traffic source that sent a customer to your store (for example, Facebook Paid, Google Search, Email, Organic). Highest level of grouping. | | Campaign | A collection of ad sets and ads created to achieve a specific marketing objective, such as driving purchases, generating leads, or building brand awareness. | | Ad Set | Sits within a campaign and controls audience targeting, budget allocation, placements, and optimization strategy for a group of ads. | | Ad | The individual creative (image, video, copy, or carousel) shown to customers. | **Page Controls**

Page Controls in Bloom Marketing Attribution

| **Control** | **Purpose** | | ----------------- | ----------------------------------------------------------------------------------- | | Date Range | Determines which orders are included in the analysis, based on order creation date. | | Attribution Model | Defines the rules for how conversion credit is assigned to touchpoints. | | Lookback Window | Sets how far back Bloom looks for customer interactions before a conversion. | | Filters | Narrows data to specific channels, campaigns, ad sets, or ads. | | Metrics Table | Displays all performance metrics for the filtered, attributed data. | **Metrics Table** The metrics table shows performance grouped into Order Metrics, Ad Spend and Traffic Metrics, Revenue Metrics, Profitability Metrics, Return on Spend Metrics, and Efficiency Metrics. Metrics prefixed with "NC" refer specifically to New Customer orders (first-time buyers), so you can track new customer acquisition separately from repeat revenue.

#### How to Use it 1. Set up the Bloom Pixel on your Shopify store. Both the App Embed, the Checkout Extension, and the UTMs must be added for attribution to work. See Bloom Pixel Setup.

2. Wait 24 hours after your first post-setup orders for attribution data to appear. 3. Open the Marketing Attribution page in Bloom. 4. Set the Date Range. Bloom includes only orders placed within this range, based on order creation date. 5. Choose an Attribution Model. Last Non-Direct Click is a good default for most merchants. 6. Choose a Lookback Window. 30 days is the recommended default for general Shopify use.

7. Apply Filters to drill into a specific Channel, Campaign, Ad Set, or Ad.

8. Review the Metrics Table to evaluate performance across the filtered data.