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?
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