Shopify Ad Attribution

Cut through attribution lies — find out which channels actually drive profit, not just which ones take credit.

Paste your Shopify order UTM data and ad spend by channel. The agent calculates true ROAS, profit-adjusted ROAS, and surfaces channels that over- or under-claim credit.

Commands

CODEBLOCK0

What Data to Provide

The agent works with:

• - Shopify orders export — paste UTM source/medium/campaign columns from order export CSV
• Ad spend by channel — "Meta: $3,200 | Google: $1,800 | TikTok: $900 this month"
• COGS and margin — "product cost is 30% of revenue, Shopify fees ~3%"
• Channel setup — list of active ad channels and their primary UTM source values
• LTV data — if available: average repeat purchase rate and second-order value

No integrations needed. Paste exported data directly.

Workspace

Creates ~/shopify-attribution/ containing:

• - setup.md — store configuration, COGS%, channel mapping, UTM conventions
• INLINECODE2 — monthly attribution reports
• INLINECODE3 — historical ad spend by channel
• INLINECODE4 — flagged attribution anomalies

Analysis Framework

1. UTM Parameter Mapping

• - Map UTM source to channel: facebook/instagram → Meta, google/cpc → Google, tiktok → TikTok, email → Email, organic → Organic, (none)/(direct) → Direct
• Clean UTM data: normalize case, strip typos, consolidate variants (e.g., "FB" and "facebook" → Meta)
• Flag orders with missing UTM data — these are attribution dark zones (often direct/email/organic)
• Compute UTM coverage rate: % of orders with valid UTM source attribution
• Group by: source, medium, campaign for granular analysis

2. Last-Click Attribution Model

• - Assign 100% of order revenue to the last UTM source before purchase
• Compute per-channel: total revenue, order count, average order value
• Match against ad spend to get last-click ROAS: Revenue / Spend
• Flag: channels with very high last-click ROAS — may be capturing credit from upper-funnel channels
• Flag: direct/(none) volume — if >30% of revenue is unattributed, attribution picture is incomplete

3. Linear Attribution Model

• - Distribute revenue equally across all touchpoints in a customer journey
• Requires multi-session UTM data — if not available, estimate using channel mix ratios
• Compare linear attribution revenue vs. last-click revenue per channel
• Channels that gain credit under linear: typically top-of-funnel (Meta, TikTok, YouTube)
• Channels that lose credit under linear: typically bottom-of-funnel (Google Brand, Email)

4. Time-Decay Attribution Model

• - Weight touchpoints more heavily the closer they are to the purchase
• Decay formula: weight = e^(−λ × daysbeforepurchase), λ = 0.1 for 7-day half-life
• Useful for longer purchase cycles (furniture, high-ticket items)
• Compare time-decay vs. last-click — large differences indicate assisted conversion patterns

5. ROAS Calculation

• - Reported ROAS = Total Revenue Attributed / Ad Spend
• Gross Profit ROAS = (Revenue × Gross Margin%) / Ad Spend
• Net Profit ROAS = (Revenue × Net Margin% after fees) / Ad Spend
• Profitability threshold: Net Profit ROAS must exceed 1.0 to be contribution-positive
• True break-even ROAS = 1 / (Gross Margin% − Platform Fee%)
• Example: 60% margin, 3% Shopify fee → Break-even ROAS = 1 / 0.57 = 1.75

6. Channel Overlap and LTV Adjustment

• - Identify customers who converted via multiple channels in a 30-day window
• Flag: Meta + Google overlap — common pattern where Meta drives discovery, Google captures conversion
• LTV adjustment: multiply first-order ROAS by repeat purchase multiplier

• - Cohort LTV by acquisition channel — some channels acquire better long-term customers

7. Attribution Anomaly Detection

• - Flag: channel spend increased but attributed revenue flat → ad performance degrading or UTM broken
• Flag: direct/(none) revenue spike without organic traffic explanation → UTM tags broken in campaign
• Flag: single campaign taking disproportionate credit (>40% of revenue) → potential tracking issue
• Flag: ROAS dramatically higher than industry benchmark → verify UTM data quality

Output Format

INLINECODE5 delivers:

Channel Summary Table

Channel	Spend	Revenue (LC)	ROAS (LC)	Profit ROAS	Orders
Meta	$X	$X	X.Xx	X.Xx	N
Google

... | ... | ... | ... | ... |

Attribution Model Comparison

Key Findings

1. 1. Best true-ROAS channel (profit-adjusted)
2. Most over-credited channel (last-click vs. linear gap)
3. Attribution coverage rate and dark zone estimate
4. Recommended budget reallocation

Rules

1. 1. Always establish COGS and margin before computing profit-adjusted ROAS — reported ROAS without margin context is misleading
2. Never declare a channel unprofitable based on last-click attribution alone — always show multi-touch comparison
3. Flag UTM coverage rate prominently — if >25% of orders lack UTM data, all channel numbers are understated
4. Apply the correct break-even ROAS threshold for the store's margin — not a generic benchmark
5. Distinguish between revenue attribution and profit attribution — high-AOV channels may look great on revenue but poor on profit
6. Identify the Meta vs. Google credit-stealing dynamic by default — it is the most common misattribution pattern in Shopify stores
7. Save reports to ~/shopify-attribution/reports/ with month-year filename on every attribution save call