> ## Documentation Index > Fetch the complete documentation index at: https://docs.lunarmc.ai/llms.txt > Use this file to discover all available pages before exploring further. # Attribution Analytics Overview ## Overview **Attribution Analytics** provides a cross-channel view of the customer journey using data captured through the SuperOrbital tracking pixel. Once the tracking pixel is implemented on a client's site, SuperOrbital can connect customer visits with purchases and revenue and attribute that activity back to the marketing sources involved in the customer journey. Unlike channel-specific reporting, Attribution Analytics allows you to analyze multiple marketing sources together using a consistent attribution model. This can help you better understand how different channels participate across the funnel, including which channels are introducing customers to the brand, which are assisting throughout the journey, and which are appearing closer to purchase. Attribution Analytics should be used as an additional lens alongside platform reporting, rather than as a replacement for the performance data reported by individual advertising platforms. *** ## Understanding Your Tracked Data At the top of the Attribution Analytics dashboard, you'll see a snapshot of the order data available for attribution analysis. This includes: * **Tracked Orders** – Orders captured through the SuperOrbital tracking pixel. * **E-Commerce Orders** – Total e-commerce orders for the selected period. * **Coverage %** – The percentage of total orders represented in the tracked dataset. Coverage provides important context when interpreting attribution results. Because Attribution Analytics relies on activity captured through the tracking pixel, not every e-commerce order will necessarily be represented in the attribution dataset. Factors such as consent choices, browser and device privacy settings, cross-device customer journeys, and orders occurring outside of tracked website sessions can all affect coverage. Keep your tracking coverage in mind when interpreting the channel-level results throughout the dashboard. *** ## Attribution Performance by Media Source The primary attribution table provides a cross-channel view of the visits, purchases, and revenue associated with each marketing source. At the **Media Source** level, you can evaluate channels such as paid social, paid search, email, SMS, direct traffic, and other identifiable traffic sources together in one view. Depending on the data available for a source, the table can include metrics such as: * Visits * Orders * Conversion Rate * Revenue * Revenue % * Impressions * Clicks * Ad Spend * ROAS * CPA This provides a consistent framework for understanding how different channels participate in the customer journey. *** ## Change Your Attribution Model Use the **Attribution Model** selector to change how credit for Orders and Revenue is assigned across customer touch points. Available models include: * Any Click * First Click * Last Click * Equal Weight * View-Through There is no single attribution model that answers every marketing question. Instead, changing models allows you to examine the customer journey from different perspectives. For example, a channel that appears more prominently under a **First Click** model may frequently introduce customers to the brand, while a channel that appears more prominently under **Last Click** may commonly occur closer to conversion. Looking at performance across multiple attribution models can provide additional context into the role different channels play across the funnel. See **Understanding Attribution Models** for a complete explanation of each model and when to use it. *** ## Understand Top- and Bottom-of-Funnel Contribution One of the primary benefits of Attribution Analytics is the ability to look beyond a single touchpoint and explore how channels participate at different stages of the customer journey. For example: * **First-touch performance** can provide insight into which channels are helping introduce new visitors to the brand. * **Last-touch performance** can help identify which channels frequently appear immediately before a purchase. * **Multi-touch models** can help illustrate how multiple marketing sources participate throughout longer customer journeys. Rather than using one model to declare a single channel responsible for a purchase, use these different views together to develop a more holistic understanding of your marketing mix. This can be especially useful when evaluating upper-funnel channels whose impact may not always be visible when looking only at the final interaction before purchase. *** ## Drill Into Your Attribution Data In addition to viewing attribution by **Media Source**, you can drill further into available marketing data using additional levels of granularity. Depending on the source and available data, this can include: * **Campaign** – Analyze attributed performance across individual campaigns. * **Ads** – Review performance associated with individual ads and creative. * **Keywords** – Analyze attributed performance across available paid search keywords. Use these views to move from a high-level understanding of channel contribution into the campaigns and tactics associated with that performance. *** ## Filter by Customer Type Use the **Customer Type** filter to analyze attribution across: * All Customers * New Customers * Returning Customers This can be particularly useful when evaluating acquisition strategy. A channel may play a different role in acquiring a first-time customer than it does in bringing an existing customer back to make another purchase. Reviewing these audiences separately can provide additional context into where different channels contribute throughout the customer lifecycle. *** ## Attributed Orders by Media Source The **Attributed Orders by Media Source** visualization shows how attributed orders are distributed across marketing sources over time. Use this graph to understand how channel contribution changes throughout the selected reporting period and to identify shifts in the overall marketing mix. You can also change the attribution model to see how the distribution changes when credit is assigned differently across customer touchpoints. Switch to the table view when you want to review the same information as a date-by-channel breakdown or export the underlying data for additional analysis. *** ## Attribution Analytics vs. Platform Reporting It's normal for Attribution Analytics and individual advertising platforms to report different conversion and revenue values. Each advertising platform uses its own attribution methodology, attribution windows, available customer signals, and reporting rules. Attribution Analytics uses activity captured through the SuperOrbital tracking pixel and applies the attribution model you've selected across the tracked customer journey. Because these systems are answering different attribution questions using different methodologies, the numbers are not expected to match exactly. Rather than treating one source as inherently more accurate than another, use the different views for different purposes: **Platform reporting** helps you understand performance within a specific advertising platform and how that platform measures the campaigns running within its ecosystem. **Attribution Analytics** provides an additional cross-channel perspective, allowing you to apply a consistent attribution model across tracked customer journeys and better understand how channels may be contributing at different stages of the funnel. Used together, these views provide more context for understanding overall marketing performance.