Signal - Marketing Attribution

Help Content for LayerFive Signal - Marketing Attribution

By Sushil Goel

• 4 articles

Attribution Analytics

Overview The Attribution Analytics dashboard is LayerFive's answer to a question every DTC brand struggles with: which channels are actually driving purchases? https://youtu.be/BZNX3u49TkQ Unlike ad platform reporting — where Google takes credit for Google conversions and Meta takes credit for Meta conversions — LayerFive uses your first-party pixel data to attribute orders consistently across all channels using a single model. This gives you a unified view of marketing performance that isn't inflated by each platform counting the same conversion multiple times. Navigate to: Signal → Marketing Attribution → Attribution Analytics The Three Summary Metrics At the top of the dashboard, three numbers set the context for everything else: | Metric | What It Tells You | | ---------------------------- | ---------------------------------------------------------------- | | LayerFive Tracked Orders | Orders your pixel successfully captured and attributed | | E-Commerce Orders | Total orders from Shopify for the same period | | Coverage % | The percentage of orders LayerFive is tracking (Tracked ÷ Total) | Coverage % is the first number to check. If it's below 80%, your attribution data is incomplete and the channel numbers below it can't be trusted. A low Coverage % almost always means the LayerFive pixel isn't deployed correctly — revisit Tag Management before drawing any conclusions from the rest of the dashboard. A healthy Coverage % is 85% or above. Some gap is normal (offline orders, orders placed on untracked pages, etc.) but anything below 80% needs investigation. Platform Comparison Cards Below the summary metrics, three cards compare platform-reported numbers vs. LayerFive-attributed numbers side by side: - Google Ads Performance and Key Metrics — Google vs. LayerFive - Facebook Ads Performance and Key Metrics — Facebook vs. LayerFive - Aggregate Ads Performance and Key Metrics — All paid channels combined vs. LayerFive Each card shows Revenue, Ad Spend, Orders, ROAS, Cost/Order, and AOV. Why the numbers differ — and what to do about it The platform numbers (Google, Facebook) use each platform's own attribution logic — typically Last Click, within their own attribution windows, counting only conversions they can see. This leads to double-counting when a customer touches both Google and Meta before purchasing. LayerFive's numbers use a single consistent attribution model across all channels, applied to your first-party data. This is closer to reality. How to use the comparison: - If LayerFive ROAS is significantly lower than platform ROAS — the platform is over-counting its contribution. This is normal for Meta (which is aggressive with view-through attribution by default) and means you should be making budget decisions based on LayerFive numbers, not Meta Ads Manager. - If LayerFive Orders are much lower than platform Orders — check your Coverage %. If coverage is healthy, this is attribution overlap: both platforms are claiming the same orders. - If AOV is higher in LayerFive — LayerFive is capturing higher-value orders. This can indicate that tracked orders skew toward certain product categories or customer segments. The MOAT Table The MOAT table is the core of the Attribution Analytics dashboard. It shows attributed performance broken down at four levels of granularity: | Tab | Granularity | | ---------------- | ------------------------------------------------------------------- | | Media Source | Performance by channel (Google Ads, Meta, Email, SMS, Direct, etc.) | | Campaign | Performance by individual campaign within each channel | | Ads | Performance by individual ad creative | | Keywords | Performance by search keyword (Google Ads) | Columns | Column | What It Means | | --------------------- | -------------------------------------------------------------- | | Visits | Sessions LayerFive recorded from this source | | Orders | Conversions attributed to this source under the selected model | | Conversion Rate % | Orders ÷ Visits | | Revenue $ | Revenue attributed to this source | | Revenue % | This source's share of total attributed revenue | | Impressions | Ad impressions (paid sources only) | | Clicks | Ad clicks (paid sources only) | | Ads Cost $ | Ad spend (paid sources only) | | ROAS | Revenue ÷ Ad Spend | | CPA | Ad Spend ÷ Orders | Each row also shows a period-over-period comparison in green (improvement) or red (decline) directly below the current period value. Filters Attribution Model — Changes which model is used to calculate Orders and Revenue across the entire table. Options: Any Click, First Click, Last Click, Equal Weight, View-Through. Customer Type — Filters the table to show performance for All customers, New Customers only, or Returning Customers only. How to Use the MOAT Table to Make Decisions 1. Start at Media Source to understand channel mix Look at Revenue % across sources. This tells you which channels are carrying the most weight in your customer journey under your chosen attribution model. Pay attention to Direct — a high Direct Revenue % often means customers are returning directly to purchase after being influenced by paid channels earlier. It's not a channel you "invest" in, but a high Direct number alongside healthy paid ROAS is a sign your brand awareness is working. 2. Switch attribution models to stress-test your channel view Run the table under Any Click, then switch to Last Click. If a channel looks strong under Any Click but collapses under Last Click, it's influencing the journey but rarely closing it — it's a mid-funnel assist channel, not a conversion driver. This matters for how you budget it. Channels that hold up well across multiple attribution models are your most reliable performers. 3. Filter by New Customer to evaluate acquisition efficiency Switch Customer Type to New Customer. This strips out repeat purchases and shows you which channels are actually bringing in first-time buyers. Compare the CPA for New Customers against your target CAC. If a channel looks efficient on blended CPA but collapses on New Customer CPA, it's mostly re-converting existing customers — which may or may not be the goal depending on your growth stage. 4. Drill to Campaign to find budget leaks Switch to the Campaign tab. Sort by Ads Cost $ descending. The top 10 campaigns by spend should also be among your top performers by ROAS or Revenue %. If a high-spend campaign has low or declining ROAS with a red period-over-period indicator, it's a candidate for budget reallocation. Use the period-over-period indicators actively — a campaign with a good absolute ROAS but a significant downward trend (shown in red) needs attention before it becomes a problem. 5. Use Keywords to audit search intent The Keywords tab shows Google Ads keyword performance with Orders, Revenue, ROAS, and CPA. This is particularly useful for identifying: - High-spend, low-converting keywords — CPA is high relative to AOV; consider pausing or reducing bids - High-converting, low-spend keywords — ROAS is strong; consider increasing bids or expanding match types - Brand vs. non-brand split — Brand keywords typically have high ROAS but limited scale; non-brand keywords drive new customer acquisition at lower efficiency 6. Use Order Breakdown by Tags to audit order quality Clicking on the E-Commerce Orders number opens an Order Breakdown by Tags modal. This shows the distribution of Shopify order tags across all orders in the period. Use this to identify how many orders in a given period are: - Replacement orders or returns (CORSO-REPLACEMENT-ORDER, CREW-RETURN) - Discount-driven orders (SINGLESHOE_discount_code_used, Paid with gift card) - Affiliate or partner orders (Goaffpro) - Unknown/untagged orders (unknown) A high proportion of discount or replacement orders can artificially inflate order counts and deflate true ROAS. If your Coverage % looks healthy but ROAS seems low, check whether a large share of orders are discount-driven. Attributed Orders by Media Source Chart Below the MOAT table, a stacked bar chart shows Attributed Orders by Media Source over time. This visualizes the daily mix of channel contribution across your attribution period. Use this chart to: - Spot sudden drops in a channel's contribution on a specific day (indicates a tracking issue, campaign pause, or budget change) - See how the channel mix shifts across the week (some channels perform differently on weekdays vs. weekends) - Identify which channels are most consistent day-to-day vs. which are volatile Switch the Attribution Type dropdown on the chart to see how the channel mix changes under different models. The chart also has a table view toggle (grid icon, top right) which shows the same data as a date × channel matrix — useful for exporting or doing deeper day-by-day analysis. Choosing the Right Attribution Model There's no single correct attribution model. The right choice depends on what question you're trying to answer: | If you want to know... | Use this model | | -------------------------------------------------------- | -------------- | | Which channels touch the most customer journeys? | Any Click | | Which channels initiate discovery? | First Click | | Which channels close purchases? | Last Click | | How to distribute credit fairly across all touchpoints? | Equal Weight | | How much influence do awareness ads have before a click? | View-Through | LayerFive's recommendation for most DTC brands: Start with Any Click as your default for a complete picture of channel involvement, and use Last Click as a secondary check to understand which channels are closing. The gap between the two tells you a lot about your funnel structure. Common Questions Why does LayerFive show fewer orders than my Shopify dashboard? Two reasons: (1) Coverage — LayerFive only attributes orders it tracked via the pixel. (2) Attribution window — if an order occurred outside your look back window, it won't be attributed. Check your Coverage % first; if it's healthy, the remaining gap is typically orders outside the attribution window. Why does LayerFive show fewer orders than Google Ads or Meta Ads Manager? Ad platforms count conversions using their own tracking, which includes view-through attribution and their own click windows. A customer who saw a Meta ad and a Google ad before purchasing may appear as a conversion in both platforms. LayerFive counts the order once and distributes credit based on your chosen model. My Direct traffic has very high Revenue % — is that a problem? Not necessarily. Direct orders in LayerFive are sessions where no UTM source was detected. This includes customers who typed your URL directly, customers who clicked an untagged link, and some attribution that falls through due to iOS/browser privacy restrictions. A high Direct % alongside healthy paid channel performance is normal. A high Direct % with declining paid performance may indicate a tracking gap. Coverage % dropped suddenly — what should I check? First, confirm all integrations show a recent Last Sync in Administration → Integrations. Then check Tag Management to confirm the pixel code hasn't been accidentally removed from your theme (this can happen after a Shopify theme update). Finally, check whether a high volume of orders in the period are of a type that wouldn't be tracked (e.g. marketplace orders, manual orders created in Shopify admin). Related Articles - Tag Management - Glossary of Terms - Attribution — New Customer - Attribution — Returning Customer - Key KPI Comparison - Getting Started with LayerFive

Last updated on Jul 16, 2026

Attribution - New Customer

Overview The Attribution — New Customer dashboard isolates marketing performance for first-time buyers only. Where Attribution Analytics shows blended performance across all customers, this view strips out repeat purchases and answers a more specific question: how efficiently is your marketing acquiring new customers? https://youtu.be/3k6jL7B4AWM This is the right view to use when evaluating customer acquisition strategy, setting CAC targets, and understanding which channels are actually growing your customer base vs. re-converting existing ones. Navigate to: Signal → Marketing Attribution → Attribution - New Customer Summary KPIs Eight KPI cards sit at the top of the dashboard, each showing the current period value and a period-over-period change indicator. | Metric | What It Measures | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Revenue | Total revenue attributed to new customer orders | | Orders | Number of orders placed by first-time buyers | | AOV | Average order value for new customers | | aMER | Acquisition MER — New Customer Revenue ÷ Total Ad Spend. Measures the cost efficiency of acquiring new customers across all marketing investment. | | Total Ad Spend | Total spend across all connected paid channels for the period | | Customers | Number of unique new customers acquired | | CPA | Ad Spend ÷ New Customer Orders — your cost to acquire one new customer order | | ROAS | New Customer Revenue ÷ Ad Spend | The relationship between these metrics These eight metrics tell a connected story. Read them together rather than in isolation: - Revenue and Orders trending up while CPA is flat or declining — you're scaling acquisition efficiently - Revenue up but Customers flat — AOV is rising, not volume. Good for revenue, but may mask slowing new customer growth - ROAS declining while aMER holds — your paid channels are becoming less efficient but overall acquisition economics are holding. Investigate which paid channels are dragging ROAS down. - aMER declining — your total ad investment is becoming less efficient at generating new customer revenue. This is a leading indicator of a CAC problem before it shows up in absolute numbers. Key KPIs Trend Chart The Key KPIs Trend chart plots ROAS, CPA, MER, and AOV over time on a dual-axis chart (left axis: value in number; right axis: value in dollars). How to read it: - ROAS and MER move on the left axis — rising lines mean improving efficiency - CPA and AOV move on the right axis (dollar values) — a rising CPA line means acquisition is getting more expensive - Watch for divergence: if ROAS is declining while CPA is rising, your new customer acquisition is under pressure from both sides Use the chart to identify when a metric changed, then correlate it with campaign changes, budget shifts, or external factors (seasonality, competitor activity) in that period. Revenue & Ad Spend Trend Chart The Revenue & Ad Spend Trend chart plots three series over time: - Ad Spend — total paid media investment (left axis) - New Customer Revenue — revenue from first-time buyers (left axis) - New Customer count — number of new customers acquired (right axis) How to read it: - The gap between New Customer Revenue and Ad Spend is your new customer profit contribution before other costs. A widening gap is healthy; a narrowing gap means acquisition economics are tightening. - Watch New Customer count independently from Revenue — if count is flat but revenue is rising, you're getting higher-value first-time buyers. If count is rising but revenue is flat, new customers are buying lower-value items. Both charts support export via the menu icon (top right of each chart): PNG, JPEG, PDF, SVG, CSV, and XLS. The MOAT Table — New Customer View The MOAT table on this page shows the same structure as Attribution Analytics but scoped exclusively to new customer orders. It has three tabs: Media Source, Campaign, and Ads. The Attribution filter applies the same model options as the main dashboard: Any Click, First Click, Last Click, Equal Weight, View-Through. How to use this table differently from the main Attribution view The main Attribution MOAT table includes all orders — new and returning. This one shows only new customers. The difference matters: Channels that look efficient on blended attribution but weak here are primarily driving repeat purchases, not acquisition. This is common for Email and SMS — high blended ROAS, but most of those orders are from existing customers responding to retention campaigns. Filtering to New Customer gives you their true acquisition contribution. Channels that look weak on blended attribution but strong here are acquisition engines that get buried when repeat purchase revenue from other channels inflates the total. This is sometimes the case for TOF Meta or Google Discovery campaigns. Specific decisions this view drives 1. Evaluating new customer CPA against your CAC target Set your target CAC before opening this dashboard. Compare each channel's CPA in the New Customer MOAT against that target. Channels above your CAC threshold are unprofitable for acquisition — even if their blended ROAS looks acceptable. 2. Finding your most efficient new customer acquisition channel Sort the Media Source tab by ROAS descending. The top channel by new customer ROAS is where additional acquisition budget will likely generate the best return. Cross-reference with the period-over-period trend — a high ROAS channel that's declining week-over-week may be saturating. 3. Identifying campaigns that aren't acquiring anyone Switch to the Campaign tab. Look for high-spend campaigns with low new customer orders. If a campaign is spending significantly but attributing few new customer conversions, it's a retention campaign being funded from your acquisition budget — or it's simply not working for acquisition. 4. Auditing creative performance for new customers Switch to the Ads tab. Compare CPA across individual ads within the same campaign. Large CPA variance between ads in the same campaign means creative is a significant lever — the best-performing ad is worth isolating and scaling, and the worst-performing ad is worth pausing. Comparing New Customer vs. Returning Customer Performance The most useful analysis you can run with this dashboard is a direct comparison between the New Customer and Returning Customer views. Open both in separate tabs and compare the same channel's performance across both views. Ask: - Which channels have high Revenue % in Returning but low Revenue % in New? Those are retention channels — evaluate them on retention metrics, not acquisition metrics. - Which channels are roughly balanced between New and Returning? These channels are working across the full funnel. - Which channels are almost exclusively New Customer? These are your acquisition engines — protect their budget when under pressure. This comparison also helps you have more honest conversations with your media agency. If they're reporting strong blended ROAS on a channel that's almost entirely returning customer revenue, the acquisition case for that channel is weaker than the headline numbers suggest. Common Questions Why are my New Customer Orders lower than I'd expect? LayerFive identifies new customers based on purchase history in your connected e-commerce data. If a customer's first purchase was before your Shopify integration was connected, LayerFive may classify a returning customer as new. This gap typically closes over time as more purchase history is captured. My New Customer ROAS is much lower than blended ROAS — is that a problem? Not necessarily — it's expected. New customers cost more to acquire than returning customers cost to retain. The question is whether your New Customer ROAS is above the threshold needed to recover CAC within your target payback period. Use Cohort Analysis to understand how quickly new customers from each channel go on to make repeat purchases. aMER is declining but ROAS looks fine — which should I trust? Both are telling you something different. ROAS reflects the efficiency of attributed paid spend only. aMER uses your total ad spend as the denominator, including spend that may not have directly attributed orders (brand awareness campaigns, impression-only spend). A declining aMER with stable ROAS means your unattributed spend is growing — worth investigating whether that spend is delivering value. Related Articles - Attribution Analytics - Attribution — Returning Customer - Key KPI Comparison - Cohort Analysis Dashboard - Glossary of Terms

Last updated on Jul 16, 2026

Attribution - Returning Customer

Overview The Attribution — Returning Customer dashboard shows marketing performance for customers who have purchased from you before. Where Attribution Analytics shows blended performance, and Attribution — New Customer isolates acquisition, this view answers a different question: how well is your marketing retaining and re-engaging existing customers? https://youtu.be/BHzQqt4uZMo Retention economics are typically more favorable than acquisition — returning customers have lower CPA, higher AOV, and higher conversion rates. But that efficiency can mask acquisition problems if you're not looking at new and returning customer performance separately. This dashboard makes the retention contribution explicit. Navigate to: Signal → Marketing Attribution → Attribution - Returning Customer Summary KPIs Eight KPI cards sit at the top of the dashboard, each showing the current period value and a period-over-period change indicator. | Metric | What It Measures | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Revenue | Total revenue attributed to returning customer orders | | Orders | Number of orders placed by existing customers | | AOV | Average order value for returning customers | | rMER | Retention MER — Returning Customer Revenue ÷ Total Ad Spend. Measures how much returning customer revenue your total ad investment is generating. | | Total Ad Spend | Total spend across all connected paid channels for the period | | Customers | Number of unique returning customers who purchased in the period | | CPA | Ad Spend ÷ Returning Customer Orders | | ROAS | Returning Customer Revenue ÷ Ad Spend | Reading these metrics together - High returning customer Revenue % of total with low new customer growth — your brand is retaining well but not expanding. Sustainable short-term, but a risk to long-term growth if new customer acquisition isn't keeping pace. - Returning customer CPA rising — it's costing more to re-engage existing customers. This often signals list fatigue, declining email/SMS engagement, or rising retargeting costs. - Returning customer AOV higher than new customer AOV — normal and healthy. Returning customers know the product and tend to buy more. A widening gap over time suggests strong product loyalty. - rMER declining — your total ad spend is generating less returning customer revenue. Worth checking whether retention-focused spend (email, SMS, retargeting) is holding efficiency, or whether overall spend growth is diluting the ratio. The MOAT Table — Returning Customer View The MOAT table is scoped to returning customer orders only, with three tabs: Media Source, Campaign, and Ads. The same attribution model options apply: Any Click, First Click, Last Click, Equal Weight, View-Through. How to use this table 1. Identify which channels are driving retention — and whether they're the right ones Look at Revenue % by Media Source. Email and SMS should typically appear near the top — these are owned channels with high returning customer conversion rates. If Paid Social or Google Ads is driving a disproportionate share of returning customer revenue, you may be spending paid budget to re-acquire customers you could be reaching through lower-cost owned channels. 2. Evaluate retention campaign efficiency at the Campaign level Switch to the Campaign tab. Look for campaigns explicitly designed for retention (remarketing, loyalty, win-back, post-purchase sequences). Check their CPA against your average retention CPA. A win-back campaign with a high CPA relative to the value of the customers it's recovering may not be worth the spend. 3. Assess whether TOF campaigns are accidentally re-converting existing customers Filter the main Attribution Analytics MOAT to New Customer, then compare the channel Revenue % to what you see here in Returning Customer. If a campaign you're running as a TOF acquisition campaign shows high returning customer revenue attribution, it's reaching your existing audience more than new ones — worth adjusting targeting exclusions. 4. Use the Ads tab to identify retention creative that's working Retention creative works differently from acquisition creative. In the Ads tab, look for ads with high returning customer conversion rates (Orders ÷ Clicks) rather than just high ROAS. An ad that converts returning customers at a high rate with lower spend is often more valuable for retention than a high-ROAS ad that happens to reach a lot of existing customers. Key KPIs Trend Chart The Key KPIs Trend chart plots ROAS, CPA, MER, and AOV for returning customers over time on a dual-axis chart. Use this chart to identify when retention efficiency changed and correlate it with specific actions — a new email flow, a retargeting campaign launch, a promotion, or a product drop. Retention metrics tend to be more stable than acquisition metrics week-over-week, so a sudden shift is usually meaningful and worth investigating. Revenue & Ad Spend Trend Chart The Revenue & Ad Spend Trend chart plots three series: - Ad Spend — total paid media investment - Returning Customer Revenue — revenue from existing buyers - Returning Customer count — number of returning customers purchasing in each period The Returning Customer count line is the most important one to watch here. Revenue can rise simply because returning customers are buying more per order (AOV effect), but count shows whether you're actually re-engaging a growing or shrinking share of your customer base. A flat or declining Returning Customer count alongside rising revenue means fewer customers are buying more — which may be fine, but also may signal that your reachable returning customer pool is shrinking. Both charts support export via the menu icon (top right): PNG, JPEG, PDF, SVG, CSV, and XLS. New Customer vs. Returning Customer: The Comparison That Matters The single most useful analysis you can do is open Attribution — New Customer and Attribution — Returning Customer side by side for the same period and compare channel performance across both. | What you're looking for | What it means | | ------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | | Channel has high Revenue % in Returning, low in New | It's a retention channel. Evaluate it on retention CPA, not acquisition ROAS. | | Channel has high Revenue % in New, low in Returning | It's an acquisition engine. Protect its budget; don't penalize it for low repeat purchase attribution. | | Channel has roughly equal share in both | It's working across the full funnel. | | Channel has high blended ROAS but almost all Returning | Blended ROAS is flattering — acquisition contribution is much weaker than the headline suggests. | This comparison is particularly important when reviewing performance with a media agency. Agencies typically report blended ROAS because it looks better. Showing the New vs. Returning split holds the reporting to a higher standard. Common Questions My retention ROAS is much higher than my acquisition ROAS — does that mean I should shift budget toward retention? Not necessarily. Retention campaigns reach customers who are already predisposed to buy again — they're cheaper to convert, which is why ROAS is higher. But you can only retain customers you've already acquired. Cutting acquisition budget to improve average ROAS by shifting toward retention is a short-term gain that shrinks your total customer base over time. Use Cohort Analysis to understand whether your existing customer base is large enough to sustain your revenue targets before reallocating acquisition spend. Why does Email show $0 Ad Spend but high Revenue in returning customer attribution? Email is an owned channel — there's no direct ad spend associated with sends. LayerFive still attributes returning customer revenue to email touchpoints when a customer clicked an email link before purchasing. The ROAS and CPA columns will show 0 for email because there's no spend denominator, but the Orders and Revenue columns reflect real attribution contribution. rMER is declining but my email and SMS metrics look fine — what else could explain it? rMER uses total ad spend as its denominator, including all paid channels. If your total paid spend has increased (even on acquisition campaigns), rMER will decline even if retention channel efficiency is unchanged. Check whether the decline in rMER correlates with a period of increased paid media investment overall. Related Articles - Attribution Analytics - Attribution — New Customer - Key KPI Comparison - Cohort Analysis Dashboard - Glossary of Terms

Last updated on Jul 16, 2026

Key KPI Comparison

Key KPI Comparison Overview Every other attribution dashboard in LayerFive shows you a single time period. Key KPI Comparison is built to do one thing those can't: show you how a metric moved across time, broken down by channel, so you can separate a real trend from a single good or bad week. https://youtu.be/BplpO9gD5Bs Use this dashboard when you need to answer "is this getting better or worse, and which channel is driving the change?" — before a budget decision, a QBR, or a stakeholder update. Navigate to: Signal → Marketing Attribution → Key KPI Comparison Choosing Your Metric Use the Metric dropdown (top right) to select what you're comparing: | Metric | Use It To Answer | | ----------- | ----------------------------------------------------------- | | Revenue | Which channels are growing or shrinking their contribution? | | ROAS | Are your channels getting more or less efficient over time? | | CPA | Is your cost to convert creeping up anywhere? | Every chart and table on the page updates to match the metric you select. Setting Your Comparison Periods This is where the dashboard earns its name. A single period tells you what happened. Multiple periods tell you the direction you're heading. | Field | What It Does | | ---------------------------- | -------------------------------------------------------- | | Number of Periods | How many periods to compare against each other | | Base Period (Date Range) | The reference date range your comparison is built around | Set both, then click Run Comparison. The charts and tables refresh with your results. Tip: Match your period length to your decision cadence. Comparing week-over-week catches fast-moving campaign issues; month-over-month is better for spotting genuine trends without weekly noise. Reading the Trend Chart The trend chart breaks your selected metric down by media source rather than showing one blended number. This matters because a blended figure hides the story. If revenue dipped, the chart shows you whether it was one channel that fell or the whole account softening — two very different problems with two very different responses. Reading the Comparison Table Below the chart, the table gives you the exact numbers by channel. The columns depend on your selected metric: | Metric Selected | Columns Shown | | --------------- | ------------------ | | Revenue | Revenue, Revenue % | | ROAS | ROAS, ROAS % | | CPA | CPA, CPA % | The percentage column is the one to watch. The absolute value tells you where a channel stands; the percentage change tells you where it's going. A channel can look healthy on absolute revenue while quietly declining period-over-period — the percentage is what surfaces that. How to Use This Dashboard to Make Decisions The most useful workflow runs all three metrics in sequence: 1. Start with Revenue. See which channels are growing their share and which are shrinking. This tells you where your revenue is actually coming from now versus your base period. 2. Switch to ROAS. Check whether your growing channels are still efficient, or just getting more expensive to run. A channel growing revenue while its ROAS declines is buying that growth, not earning it. 3. Finish with CPA. Catch any channel where your cost to acquire is creeping up before it becomes a margin problem. The pattern to watch for across all three: rising revenue paired with rising CPA or falling ROAS is a warning, not a win. It means a channel is scaling on cost, not efficiency. This dashboard is the fastest way to catch that early. Exporting for Reports and Reviews The chart menu (top right of the chart) exports your comparison for use elsewhere: - Images: PNG, JPEG, SVG - Documents: PDF - Data: CSV, XLS Use these to drop trend visuals straight into QBR decks, board updates, or stakeholder reports without rebuilding the analysis. Common Questions Why does a channel show strong absolute revenue but a negative percentage change? The absolute number reflects the current period; the percentage reflects the movement from your base period. A channel can still be large while trending down. Both are true, and the percentage is the one that predicts where it's heading. Should I compare week-over-week or month-over-month? Shorter periods catch campaign-level issues faster but carry more noise. Longer periods smooth out weekly volatility and surface genuine trends. Match the period to the decision — tactical budget shifts favor shorter windows; strategic reviews favor longer ones. My blended metric looks flat but I know something changed. Why? A flat blended number often hides offsetting channel movements — one channel up, another down. Switch to the trend chart and read it by media source; the movement is almost always visible at the channel level even when the total looks unchanged. Related Articles - Attribution Analytics - Attribution — New Customer - Attribution — Returning Customer - Glossary of Terms

Last updated on Jul 16, 2026