--- name: meta-ads-reported-vs-real-roas description: "Every Monday: each Meta and Google Ads campaign's claimed purchases next to the orders Shopify or Stripe recorded with its UTM, and the campaign inflating most. Use when the user asks for this, or when it runs on a schedule." --- # Reported vs real ROAS: Meta and Google Ads against Stripe or Shopify Every Monday: each Meta and Google Ads campaign's claimed purchases next to the orders Shopify or Stripe recorded with its UTM, and the campaign inflating most. Ads Manager says a campaign returned 3.4 times its spend. The store says something smaller, but only as one total, so nobody knows which campaign is padding its numbers. For ecommerce marketers and founders who move budget between campaigns every week, that missing per-campaign answer is the expensive part. Every Monday the agent lines up each campaign's platform-reported purchases and value with the orders that actually carried that campaign's UTM in Shopify or Stripe, on the same conversion dates. You see reported ROAS and real ROAS side by side, the view-through share Meta folds in, and the one campaign where the gap is widest. ## Two ways to get the data Use the API or MCP route when you have an MCP server, a CLI or an API token for the app. Otherwise, if you can drive a browser the user is signed in to (Claude in Chrome, a Playwright or Chrome DevTools MCP server), use the browser route. Say which one you used. ## Route A: API or MCP server ### What you need - UTM tags on every ad: Meta URL parameters such as `utm_source=facebook&utm_medium=paid&utm_campaign={{campaign.id}}`, and a Google Ads final URL suffix such as `utm_source=google&utm_medium=cpc&utm_campaign={campaignid}`. - Read access to both ad accounts (`ads_read` on the Meta ad account, a Read only Google Ads user or the Google Ads MCP server). - Shopify Admin API `read_orders`, or a Stripe restricted key with Read on Checkout Sessions and Customers plus a checkout that copies the visitor's UTM into Checkout Session or customer metadata. If something is missing, say what and stop; do not invent numbers. ### Steps 1. **Pull Meta's claim per campaign** (Meta Ads): Call `GET /act_/insights` with `level=campaign`, last Monday to Sunday as `time_range`, `fields=campaign_id,campaign_name,spend,actions,action_values`, `action_attribution_windows=["7d_click","1d_view"]` and `action_report_time=conversion`, so purchases sit on the day they happened. Read one action type only, `omni_purchase` (the Purchases column in Ads Manager), and keep its `7d_click` and `1d_view` values apart. 2. **Pull Google's claim per campaign** (Google Ads): Run `SELECT campaign.id, campaign.name, metrics.cost_micros, metrics.conversions_by_conversion_date, metrics.conversions_value_by_conversion_date FROM campaign WHERE segments.date BETWEEN '' AND ''`. The plain `metrics.conversions` sits on the click date, which would not line up with store orders. 3. **Collect real orders with their campaign** (Shopify): From Shopify (whichever store you use), page through `orders` for the same dates (`test:false`, not cancelled) with `currentTotalPriceSet`, `currentTotalTaxSet` and `customerJourneySummary { ready lastVisit { utmParameters { source medium campaign } } }`. From Stripe instead, list Checkout Sessions with `status=complete` and that `created` range and read `metadata.utm_campaign`, or the customer's metadata for subscription first payments. 4. **Join orders to campaigns on utm_campaign**: Normalize both sides: lowercase, trim, URL-decode (`+` and `%20` to spaces), then match `utm_campaign` to the campaign ID, or to the name when the person tags with names. Orders whose campaign value matches no campaign go in an "unknown campaign" row (renamed or paused campaigns); orders with no UTM stay unattributed and are never shared out; campaigns with spend and zero matched orders are flagged. Orders where `ready` is false are rechecked next run. 5. **Work out reported and real ROAS**: Per campaign: reported ROAS is platform value over spend, real ROAS is matched order revenue (after refunds, without tax) over spend, and the gap is the difference in revenue. Rank campaigns by gap in dollars, and say how many of Meta's purchases were `1d_view`, since no UTM can ever confirm a view. 6. **Post the card**: Title the card with the campaign that has the widest gap, both ROAS figures in the summary, the full table in the widget, and the unattributed and unknown-campaign totals in a callout. Use item_key `insight-meta-ads-reported-vs-real-roas` every week. ## Route B: the browser ### What you need - Signed in to Meta with at least Analyst access on the ad account, to Google Ads with Read only access, and to Shopify with Reports permission or to Stripe with a role that can view payments. If something is missing, say what and stop; do not invent numbers. ### Steps Start at https://adsmanager.facebook.com/. The user is signed in already; never type passwords or codes, and ask if a sign-in page appears. 1. **Read Meta per campaign**: Pick the ad account, open the **Campaigns** tab and set the date to last Monday to Sunday. Under **Columns > Customize columns**, add **Purchases** and **Purchases conversion value**, tick **Compare attribution settings** and choose 7-day click and 1-day view, then note spend, purchases and value per campaign with the click and view split. 2. **Read Google Ads per campaign**: Open `https://ads.google.com/`, go to **Campaigns** for the same dates, and add the **Cost**, **Conversions (by conv. time)** and **Conv. value (by conv. time)** columns. Note each campaign's ID too (add the **Campaign ID** column) if your UTMs use IDs. 3. **Read orders by UTM campaign**: Open `https://admin.shopify.com/`, pick the store, then **Analytics > Reports** and the **Sales attributed to marketing** report for the same dates, grouped by UTM campaign. For Stripe, open **Payments**, filter to the week and read the `utm_campaign` metadata on each succeeded payment (your checkout must copy it onto the payment, or open the linked Checkout Session), or export the list with metadata columns. 4. **Match and compare**: Match each UTM campaign value to a campaign ID or name, lowercased. For every campaign, divide the matched revenue by spend for real ROAS, set it beside the platform figure, and list UTM values that match nothing and the orders with no UTM at all. Note: Shopify's marketing report applies its own attribution model, so its campaign totals can differ a little from a per-order count through the API. Ads Manager shows conversions on the impression date unless you change the reporting setting, so a few purchases can shift between weeks. Over a dozen campaigns, the API route is the better one. ## The report - **The worst gap**: the campaign whose reported revenue the store backs up least, with both ROAS figures. - **Per campaign**: spend, platform purchases and value, matched orders and revenue, reported and real ROAS. - **View-through share** of Meta's purchases. - **Unmatched**: orders with an unknown campaign value, and orders with no UTM. - One thing to do first, for example "Cut Prospecting LAL's budget by a third until real ROAS clears 2.5". ## Post it to Briefkin When `BRIEFKIN_TOKEN` is set, or `~/.config/briefkin/env` exists (the installer from https://briefkin.dev/docs writes it), also send the result to the user's Briefkin board as one card. Keep the `item_key` the same on every run so the card updates in place, use a new `event_id` per run, and put today's real numbers in place of the example ones. If the request fails, say so once and carry on. ```bash [ -n "${BRIEFKIN_TOKEN:-}" ] || . ~/.config/briefkin/env curl -sS -X POST "${BRIEFKIN_URL:-https://briefkin.dev}/v1/events" \ -H "authorization: Bearer $BRIEFKIN_TOKEN" \ -H "content-type: application/json" \ -d @- <<'JSON' { "v": 1, "event_id": "meta-ads-reported-vs-real-roas/", "item_key": "meta-ads-reported-vs-real-roas", "project": "Acme Store", "source": "Reported vs real ROAS: Meta and Google Ads against Stripe or Shopify skill", "kind": "update", "title": "Prospecting LAL: real ROAS 1.9, Meta says 3.4", "summary": "Meta reports **41 purchases** for Prospecting LAL last week; Shopify shows **23 orders** with its UTM. Real ROAS **1.9**, reported **3.4**. 10 of the 41 were 1-day view-throughs.", "widget": [ { "type": "Table", "props": { "columns": [ "Campaign", "Spend", "Platform says", "Shopify shows", "Reported", "Real" ], "align": [ "left", "right", "right", "right", "right", "right" ], "rows": [ [ "Meta: Prospecting LAL", "$2,400", "41 / $8,160", "23 / $4,560", "3.4x", "1.9x" ], [ "Google: PMax all products", "$1,800", "38 / $6,840", "26 / $4,680", "3.8x", "2.6x" ], [ "Meta: Retargeting 30d", "$900", "28 / $5,040", "24 / $4,320", "5.6x", "4.8x" ], [ "Google: Search brand", "$600", "52 / $9,360", "49 / $8,820", "15.6x", "14.7x" ], [ "All four", "$5,700", "159 / $29,400", "122 / $22,380", "5.2x", "3.9x" ] ] } }, { "type": "Callout", "props": { "text": "Prospecting LAL claims $3,600 more than the store can trace to it, the largest gap of the four. Not counted: 9 orders ($1,530) with a utm_campaign that matches no live campaign, and 64 orders with no UTM.", "tone": "warn" } } ] } JSON ``` Widgets come from the Briefkin catalog (https://briefkin.dev/v1/catalog). Without a token, just give the user the report. ## Run it on a schedule Suggested: Mondays at 9:00 (`0 9 * * 1`). From cron, with the agent signed in on that machine: ```bash 0 9 * * 1 claude -p "Run the meta-ads-reported-vs-real-roas skill" # Codex: codex exec, Gemini CLI: gemini -p, Copilot CLI: copilot -p, OpenCode: opencode run ``` Or use the agent's own scheduler (Claude Code routines, Codex automations, Claude or ChatGPT scheduled tasks): https://briefkin.dev/skills/meta-ads-reported-vs-real-roas#schedule Source: https://briefkin.dev/skills/meta-ads-reported-vs-real-roas