Overview
Attribution, the process of assigning credit to the marketing touchpoints that influence a customer’s decision to purchase, is a key part of marketing analysis that helps you determine which touchpoints are driving revenue. Attribution models determine how that credit is distributed, whether to a single interaction or to multiple touchpoints in the customer journey.
Triple Whale provides multiple attribution models so you can align your measurement approach with your decision-making needs. With Triple Pixel, every interaction in the customer journey is tracked and connected to orders. Each attribution model then applies specific credit logic to that first-party click data, allowing you to understand the customer journey and the impact of channels and campaigns on performance.
Attribution Windows
An attribution window defines the time frame during which touchpoints are eligible to receive credit for a purchase.
Different types of businesses require different attribution windows. For some, crediting an ad from a month ago might be too long, but for others with longer sales cycles (such as furniture or luxury goods), even a month might be too short.
Triple Whale gives flexibility in selecting your attribution window. You can choose from:
1 Day
7 Days
14 Days
28 Days
Lifetime (which covers the entire customer journey, no matter how long)
The default window is 28 days, but you can change it to reflect the window that best fits your business model and sales cycle.
Attribution Models
An attribution model is a measurement framework that helps determine which touchpoints in a customer’s journey receive credit for a conversion. It defines how much each marketing effort — whether an ad, email, or any other interaction — contributes to the final sale.
Triple Whale offers seven different attribution models across single-touch and multi-touch frameworks, each designed to provide unique insights that help you optimize marketing strategies based on your goals:
Single Touch Models
First Click
Last Click
Multi-Touch Attribution (MTA) Models
Clicks & Deterministic Views
Linear (All and Paid)
Triple Attribution
Triple Attribution + Platform Views
Total Impact
Note: Clicks & Deterministic Views, Total Impact, Triple Attribution, and Triple Attribution + Platform Views are proprietary Triple Whale multi-touch attribution models
Summary of Attribution Models in Triple Whale:
Attribution Model | Data Source | Attribution Logic | Best For | Insight Delivered |
Total Impact (TI) | First-party click data, zero-party data, Post-Purchase Survey (PPS) data | Distributes credit across all touchpoints in the customer journey. | Complex, multi-channel journeys & longer sales cycles where every touchpoint matters. | Broad view of full funnel impact — shows how all interactions contribute to conversions. |
Clicks & Deterministic Views | First-party click data + deterministic, platform-verified view data | Assigns fractional credit to eligible clicks and deterministic ad impressions across the customer journey. | Understanding the impact of the full-funnel, where views influence conversions, even without a click (Blended, cross-channel). | A balanced view of paid channel influence, where clicks and views share credit without double counting. |
Triple Attribution (TA) | First-party click data | Assigns credit to the last click per platform before conversion (not total revenue, but platform-level credit). | Daily campaign optimization and cross- channel comparisons. | Which channels and ads are driving conversions |
Triple Attribution + Modeled Views | First-party click data + modeled platform-reported view data | TA model enhanced with view-through conversions from platforms where supported | Evaluating paid media using both clicks and views at the platform level (upper funnel + cross-channel). | Platform-level impact of paid ads, including clicks and modeled view-through conversions. |
Linear (All) | First-party click data powered by Triple Pixel | Splits conversion credit equally across all touchpoints (paid + organic). | When you want a balanced view of all marketing efforts. | Balanced picture of how each touchpoint contributes to conversions without favoring one interaction over another. |
Linear (Paid) | First-party click data powered by Triple Pixel | Splits credit equally across paid touchpoints only. | Evaluating paid media performance. | How your paid channels collectively contribute to conversions. |
First Click (FC) | First-party click data powered by Triple Pixel | Gives 100% of credit to the first interaction. | Measuring which channels generate awareness. | Which touchpoint starts demand |
Last Click (LC) | First-party click data powered by Triple Pixel | Gives 100% of credit to the final interaction before purchase. | Short purchase cycles and conversion optimization. | Which channel closes the sale. |
Clicks & Deterministic Views
Clicks & Deterministic Views (C&DV) is a multi-touch attribution model that combines first-party click data with verified view data to provide a more complete view of the entire customer journey. It distributes credit across all eligible click and view-based touchpoints prior to purchase, meaning credit is normalized across the full customer journey, so total attributed revenue reconciles with actual store revenue.
Learn more: Clicks & Deterministic Views Attribution Model
Use When:
You want to analyze full-funnel, cross-platform performance using both clicks and deterministic (non-modeled) ad exposures across digital channels.
You want to measure how clicks and impressions work together to drive conversions
You need to evaluate the combined impact of awareness (like paid social or video) and demand-capture (like Search or email) channels
Advantages:
Provides detailed channel-level insight into both upper- and lower-funnel advertising effectiveness
Combines click and verified (non-modeled) impression data for a more comprehensive, full-funnel view of performance
Model Limitations:
This model does not factor in offline spend. Brands that want to incorporate channels that are non-digital should use Total Impact for marketing mix analysis.
Example
A shopper sees a Pinterest ad, views a TikTok ad, and finally clicks a Meta ad before purchasing.
Both views and clicks receive credit. The Meta click receives a larger share (reflecting higher intent), while the Pinterest and TikTok views receive smaller weighted credit for influencing the purchase.
Total Impact
The Total Impact (TI) model distributes credit across all recorded touchpoints prior to purchase. It leverages first-party data, zero-party data, and a proprietary algorithm to deliver a holistic perspective of the entire customer journey.
Total Impact is the only attribution model that uses post-purchase survey (PPS) responses to adjust channel weights, making it beneficial for understanding the overall effectiveness of your marketing efforts across channels. Channels cited more frequently by customers receive more credit while those cited less often receive less.
Note: PPS adjustment requires a connected tool (such as Triple Whale's PPS, Fairing, Kno) and at least 7 days of survey response data. Without PPS data, the model behaves similarly to Linear All.
Learn more: The Total Impact Attribution Model
Use When:
You need to inform budget allocation across your full marketing mix, including digital and offline channels that don’t generate click data (e.g., podcasts, TV, or influencers)
You want to understand total channel contribution beyond what platform data can show
Advantages:
Assesses the overall performance of complex, multi-channel marketing strategies that include offline spend
Captures a wide range of data points, including survey responses, website visits, and email interactions
Provides a holistic understanding of how various marketing tactics contribute to revenue
Model Limitations:
Relies on the Post-Purchase Survey to weigh attribution. Some brands don’t have a PPS or want to use it for attribution purposes.
Example
A customer clicks on a Pinterest ad, but does not purchase. They later click on a Facebook ad, visit your website, and leave without buying. Finally, they click on a Google search ad and complete a purchase.
The Total Impact model distributes credit across all touchpoints (Pinterest ad, Facebook ad, and Google search ad), reflecting the cumulative effect of all interactions on the customer’s decision to purchase.
Triple Attribution
Triple Attribution (TA) focuses on click-based revenue attributed to each channel. In this model, each platform receives 100% credit, which makes this model effective for analyzing campaigns and benchmarking performance at the platform level.
Use When:
You want to align reporting with ad platform dashboards and optimize performance within individual paid channels.
You want to identify which channels and specific ads are most effective in driving conversions for day-to-day campaign, ad-set and ad optimization and decisions.
Advantages:
Helps in optimizing ads based on their final interaction impact.
Helps detect the true impact of campaigns, ad-sets and ads.
Model Limitations:
This model does not include view-through conversions. For advertisers that want to optimize campaigns, ad-sets and ads factoring in platform-reported view-through, use Triple Attribution + Platform Views.
Example
A customer clicks a YouTube ad, later clicks a display ad on a news site, then clicks a Twitter ad but does not purchase. They receive a retargeting email and finally click a Google search ad to complete the purchase.
With Triple Attribution, each platform’s final interaction before conversion receives full credit. So, the YouTube ad, display ad, and Twitter ad each receive full credit for their final interactions before the conversion.
Important Note: Do not use Triple Attribution for financial reporting or total revenue analysis. Use Total Impact, Clicks & Deterministic Views, or Linear (All) for reconciled revenue views.
Because each platform receives 100% credit independently, total attributed revenue across all channels will exceed actual revenue. This is expected as this model is designed for platform-level optimization, not revenue reporting.
When viewing your attribution dashboards on the Ads >All Channel page, using either of the "Triple Attribution" models may result in the bottom-line revenue appearing higher than your native shop sales data, due to the duplicated attribution. As such, we do not suggest using this model on the All Channels dashboard for understanding our total attributed revenue, because the total revenue would be duplicated.
Triple Attribution + Platform Views
Triple Attribution + Platform Views extends the TA framework by incorporating platform-reported view-through conversions and conversion values via platform API. Modern ad platforms attribute conversions to both clicks and views (often referred to as view-through or impression-based conversions). TA + Platform Views layers those reported view-through conversions on top of click-based attribution (it’s modeled, not deterministic).
Note: TA + Platform Views is not available for channels without native integrations.
Use When:
You want to optimize within a channel using platform-aligned data, including both clicks and reported view-through conversions.
You need a comprehensive understanding of paid ads' impact that factors in view-through conversions.
Advantages
Especially strong when platform consistency matters more than independent validation, and you’re evaluating performance across different tactics within the same channel.
Provides insight into top and bottom of funnel paid media effectiveness, especially when platform-reported view-through conversions do not make up a significant percentage of total conversion value.
Combines direct interactions(clicks) and indirect views for a fuller picture of performance.
Model Limitations:
Relies on in-platform view-through conversions which could over-inflated conversions based on the platform's own bias.
Example
A customer views a Facebook ad but does not click. Later, they click a Twitter ad but do not purchase. They see another Facebook ad, click it, and eventually purchase after receiving an email.
Triple Attribution + Modeled Views distributes credit not only to the direct clicks (Twitter ad, Facebook ad, and email) but also takes into account the view-through conversions from the Facebook ads.
Regarding survey data: Triple Attribution and Triple Attribution + Views pulls in the Post Purchase Survey (PPS) results for each channel and adds those results to the total Conversion Value reported for each channel.
Difference between Clicks & Deterministic Views and Triple Attribution + Platform Views
Clicks & Deterministic Views integrates first-party click data and verified ad impressions into a single, unified customer journey. Credit is distributed across all eligible touchpoints and normalized across clicks and views.
Triple Attribution + Platform Views starts from the Triple Attribution framework and appends platform-reported view-through conversions. Credit is assigned at the platform level, which can result in duplicated revenue across channels.
| Clicks & Deterministic Views | Triple Attribution + Platform Views |
Model foundation | Unified, cross-channel customer journey | Platform-level last-click framework |
Revenue reconciles | Yes | No |
Deduplicated across platforms | Yes | No |
Credit distribution | Divided across all eligible touchpoints | 100% credit per platform (last click logic) |
View-through data | Verified impressions integrated directly into the model | Platform-reported view-through conversions layered on top of click data |
Revenue across channels | Matches Shopify totals | Exceeds Shopify totals (by design) |
Best for | Cross-channel optimization and budget allocation | Platform benchmarking and validation against native reporting |
Choose this model when | You need a cross-platform view of performance for optimization decisions. | You are comparing performance against native platform reporting at the channel level. |
Linear Attribution
The Linear model gives equal credit to every touchpoint along the customer’s journey by dividing the attributable revenue amongst all touchpoints. This model is ideal for analyzing how various marketing efforts collectively contribute to conversions without overemphasizing any single touchpoint.
Triple Whale provides two versions of linear attribution:
Linear All: Distributes equal credit amongst all traffic sources, including earned, owned and paid traffic. It provides a balanced view of all marketing efforts by recognizing the contribution of each interaction a customer has with your brand.
Linear Paid: Distributes equal credit only across paid channels. By focusing on paid media, Linear Paid helps you understand how your investment in paid marketing channels contributes to conversions.
Use When:
Use Linear All when you want to establish a neutral baseline and a balanced, unbiased view of your entire channel mix, without factoring in view through conversions.
Use Linear Paid when you want to evaluate paid media in isolation, without the influence of organic, owned, or earned touchpoints.
Advantages
Best for marketing mixes that are paid search heavy and where there is little predominance of view based platforms
Provides an equitable analysis of all marketing efforts.
Ensures that no single channel or interaction is over or under-valued.
Model Limitations:
Fractionalizes credit which may dilute true performance of certain campaigns
Misses out on any view through or halo effects of top-of-funnel platforms which makes any Linear model best used for marketing mixes that are very search heavy.
Example
A customer clicks a Twitter ad, then a blog link in a marketing email, then a Facebook retargeting ad, and finally a Google search ad before purchasing.
In Linear (All), credit is distributed equally across all four touchpoints (Twitter ad, blog post link, Facebook retargeting ad, and Google search ad).
In Linear (Paid), credit is distributed equally across the paid interactions only (Twitter ad, Facebook retargeting ad, and Google search ad).
Regarding survey data: Linear All and Linear Paid do not use Post-Purchase Survey data. Credit is distributed equally regardless of survey responses. If you want channel weights informed by survey responses, use the Total Impact model instead.
First Click and Last Click Models
The First Click (FC) model attributes all credit to the first touchpoint, while the Last Click (LC) model gives all credit to the final touchpoint before a conversion. These models offer insights into the awareness and conversion phases of the customer journey, respectively.
Use When:
First Click: Use when you’re trying to identify which channels are driving initial customer interest and acquisition
Last Click: Use when you want to understand which channels or ads are most effective at converting prospects into customers.
Advantages
First Click: Highlights the effectiveness of top-of-the-funnel marketing efforts.
Last Click: Focuses on the touchpoints that seal the deal, aiding in optimizing lower-funnel activities.
Disadvantages
Models only look at a single touchpoint in the customer journey, they do not factor in the entire customer journey or view-through data from ad platforms
Last Click tends to over-index on demand capture channels like Search at the expense of Meta and other top of funnel channels.
Example
Imagine a customer who first sees a Facebook ad and clicks on it but doesn't make a purchase. Two weeks later, they receive an email from your brand, click on it, and still don’t buy. Finally, they see a Google search ad, click on it, and decide to make a purchase. In the First Click model, the Facebook ad receives all the credit because it was the initial touchpoint that started the customer's journey. In the Last Click model, the Google search ad would receive all the credit.
Please Note: Neither First Click nor Last Click uses Post-Purchase Survey data. All credit is assigned to a single touchpoint based on its position in the journey. Survey responses have no influence on how credit is distributed in these models.
How Direct Behaves Across Attribution Models
Direct is one of the most frequently misunderstood sources in Triple Whale because its behavior changes depending on which attribution model you're using.
Direct typically captures traffic with no referrer: customers typing your URL directly, clicking links in native apps, or arriving via channels that remove referrer data.
Here is how Direct behaves in each model:
Clicks & Deterministic Views: Direct is not a participating channel in this model. Only channels with first-party click data or verified view-through signals are included.
Linear All: Direct receives no credit if any other qualifying marketing touchpoint exists in the same attribution window. This is intentional — direct is treated as a pass-through when paid or organic channels are present in the journey.
Linear Paid: Direct receives no credit. Only paid channels are included in this model.
Triple Attribution and Triple Attribution + Platform Views: Direct can receive last-touch credit as a standalone channel if it was the final interaction before purchase.
Total Impact: Direct can receive credit, but its weight is adjusted if customers rarely cite it in PPS responses.
First Click: Direct receives full credit only if it was the very first touchpoint with no prior marketing interaction recorded in the attribution window.
Last Click: Direct receives full credit if it was the final touchpoint before purchase, even if paid channels appeared earlier in the journey.
Note: If Direct appears unexpectedly high in your reporting, common causes include: broken or missing UTM parameters on campaigns, a high proportion of returning and brand-loyal customers, or using Last Click, which tends to credit the final touchpoint regardless of what happened earlier in the journey.
Recommended Filters by Model
Applying the right filters when using each model can significantly improve the accuracy and usefulness of what you see. Below are the recommended filter settings for each attribution model.
Total Impact
Date range: 7 days minimum; 28 days recommended for budget planning decisions.
Attribution window: 28 days for most brands. Use Lifetime for products with long consideration cycles.
Avoid filtering to a single channel; Total Impact is designed for cross-channel analysis.
Avoid short date ranges; the model lacks sufficient signal to weight touchpoints reliably on fewer than 7 days of data.
Clicks & Deterministic Views
Ensure supported ad platforms are connected for view-through data to be included.
Allow 24–48 hours after enabling data to populate.
Clicks update in real time but views refresh daily.
Recommended attribution window: 7 or 28 days depending on your typical purchase cycle.
Triple Attribution / Triple Attribution + Platform Views
Use at the channel or campaign level, not the ‘All Channels’ view for revenue totals.
Recommended Attribution window: 1-day click for same-day optimization; 7 days for weekly performance review.
Pair with the individual platform filter (e.g., Meta only, Google only) when benchmarking against native ad platform reporting.
For TA + Views, check which platforms have view-through data available before comparing channels, not all integrations include view data.
Linear All
Date range: 28 days recommended to capture the full customer journey across all touchpoints.
Avoid short date ranges — with fewer touchpoints per journey, equal weighting can produce misleading channel distributions.
Linear Paid
Use the ‘Paid’ channels filter active in the dashboard.
Attribution window: 7 or 28 days depending on your typical purchase cycle.
First Click
Filter to New Customers only for the clearest new customer acquisition signal.
Practical Applications
Selecting the appropriate attribution model ensures your marketing analysis aligns with your specific goals. Doing so ensures that you gain actionable insights into your marketing efforts.
Awareness Campaigns: Clicks & Views and the First Click model is ideal for identifying and optimizing channels that drive initial interest and generate brand awareness.
Bottom-of-funnel Campaigns: The Last Click model helps pinpoint and enhance the touchpoints that convert prospects into customers, making it perfect for campaigns focused on driving sales.
Comprehensive Insights: The Total Impact model is best for obtaining an overall view of marketing efforts, especially for products with longer sales cycles and multiple touchpoints. Ideal for brands that have offline channels.
Balanced View: The Clicks & Deterministic Views and Linear models are suited for campaigns where every interaction is significant. It provides an equitable analysis of all touchpoints, ensuring a balanced understanding of their collective contribution to conversions. Ideal for brands that mostly advertise on paid digital platforms.
Campaign, Ad-set, Ad-specific Analysis: The Triple Attribution model is beneficial for multi-platform campaigns, offering detailed insights into cross-platform advertising effectiveness. The Triple Attribution model is beneficial for optimizing in-platform campaigns, ad-sets and ads. The Triple Attribution + Platform Views can enhance the analysis of campaigns, ad-sets and ads by pulling in view through conversions from the platform which is helpful for campaigns that are video heavy.
By understanding and utilizing the right attribution model in Triple Whale for each use case, you will gain accurate insights into campaign performance, empowering you to make informed decisions and optimize your marketing strategies.