🎁 Get the FREE AI Skills Starter Guide — Subscribe →
BytesAgainBytesAgain
BytesAgain › Skills › Shopify Ad Attribution
🦀 ClawHub

Shopify Ad Attribution

by @mguozhen

Shopify ad attribution agent. Calculates true ROAS per channel by correlating Shopify order UTM data with ad spend — reveals which channels actually drive pr...

Versionv1.0.0
Installs1
#advertising#ecommerce#legal
TERMINAL
clawhub install shopify-ad-attribution

📖 About This Skill

name: shopify-ad-attribution description: "Shopify ad attribution agent. Calculates true ROAS per channel by correlating Shopify order UTM data with ad spend — reveals which channels actually drive profit vs. which ones just get credit. Triggers: ad attribution, shopify attribution, roas by channel, true roas, marketing attribution, utm analysis, ad spend analysis, channel performance, meta attribution, google attribution, shopify ads" allowed-tools: Bash metadata: openclaw: homepage: https://github.com/mguozhen/shopify-ad-attribution

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

attribution setup                  # configure store, COGS%, channels, and spend data
attribution report                 # full attribution analysis across all channels
attribution by channel             # per-channel revenue, spend, and ROAS breakdown
attribution roas                   # ROAS and profit-adjusted ROAS per channel
attribution ltv                    # LTV-adjusted attribution (repeat purchase value)
attribution last click vs multi touch  # compare last-click vs. linear vs. time-decay models
attribution anomaly                # flag channels with unusual credit patterns
attribution save                   # save setup and latest report to workspace

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
  • reports/ — monthly attribution reports
  • spend-log.md — historical ad spend by channel
  • anomalies.md — 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 × days_before_purchase), 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
    • - If avg customer makes 1.4 purchases in first year, LTV ROAS = Reported ROAS × 1.4
  • 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

    attribution report 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

    | Channel | Last-Click | Linear | Time-Decay | Difference | |---------|-----------|--------|------------|------------|

    Key Findings

    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. 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

    🔒 Constraints

    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