Produce a brief media buying health check based on the last 7 days data
Focus:
* show the current overall marketing health in contrast to previous 7 days and 365 day average
* highlight any campaigns and creatives with significant spend but concerning performance, as defined by
* use Polar Pixel metrics as your primary attributed conversions, revenue, CAC and ROAS measure
* for awareness campaigns, do not judge based on conversion metrics, instead find Platform metrics that measure reach, frequency, engagement, fatigue etc
* do not recommend increasing investment in branded search, it may just cannibalize organic traffic

## Report Structure

Media Buying Health Check
Period:

1. Headline metrics

* New vs returning customers and new customer % 
* Blended ROAS (MER)
* Total Ad spend
* Include trends (WoW and vs 365d average). Note material shifts.

2. Acquisition efficiency 

* Commentary focuses on factual shifts in spend efficiency, and balance of acquisition sources. Avoid winner/loser framing.
* One table by "Default channel grouping"
* One table by "Channel", filtered only to paid ad channels, include spend
* In each table report:
  - `shopify_sales_main.raw.polar_pixel_conversions`
  - `shopify_sales_main.computed.pixel_gross_sales`
* In the default channel groping include
  - AOV
* In the paid channel table also include
  - `pixel_roas`
  - Platform revenue and ROAS

3. Conversion campaign highlights (Polar Pixel attribution)

* Top 3-5 and bottom 3-5 campaigns by ROAS/CAC (significant spend only).

4. Awareness campaign highlights (Facebook ads, platform attribution)

* Top 2–3 and bottom 2–3 campaigns by reach, engagement, CPM, CPC etc (significant spend only).
* Use Facebook Ads metrics

5. Ad Creative Performance (Facebook ads, platform attribution)

* Query for all creatives with significant spend, generate an hidden leaderboard with:
	* Outcome metrics
		* Spend
		* Purchases Conversion Value
		* ROAS
		* Revenue per Impression
		* Purchases (Click Attribution)
		* Purchases (View Attribution)
	* Funnel metrics 
		* CTR (All)
		* CPC (All)
		* Cost per Add to Cart
		* Cost per Checkout Initiated
		* Cost per Purchase
		* Average Purchase Conversion Value
	* Meta only (broken by `ad_name`)
	* Use Facebook Ads metrics
* Comment about (referring to content / geography / position / format)
	1. patterns in outcome - what closes the deal
	2. patterns in funnel - what engages interest
* illustrate with top and bottom 5 performing

9. Recommendations / Next Steps

* short, medium and long-term fixes/experiments based on strenghts and weaknesses found above.

## Style & Output Rules

* For each section, include commentary before tables
* No alarmist language (banned word examples: crisis, critical, immediate)
* Keep the main report concise (<1 page equivalent).
* Use tables for number-heavy sections; text for commentary.
* No placeholders.
* No estimated / projected numbers.
* Always label attribution basis for advertising metrics — Pixel vs Platform vs GA.
* Double-check:
  - Positive % means current > previous, negative % means current < previous.
  - Percentage changes in tables match the raw numbers shown.
  - Levels vs changes are clearly separated.
  - Root causes are quantified and add up to the observed effect.
  - Magnitude is understandable without seeing the raw data.
  
## Expert knowledge you have:

* You can compare marketing channels or campaigns in one request by fetching blended metrics such as:
  * platform-reported:
    * `total_marketing_spend`
    * `total_conversions_from_pixels`
    * `total_conversion_value_from_pixels`
    * `paid_roas`
  * polar pixel attributed:
    * `shopify_sales_main.computed.pixel_gross_sales`
    * `shopify_sales_main.raw.polar_pixel_conversions`
    * `pixel_roas`
    * `pixel_cac`
  * ...and then breaking down by `channel` or `campaign`.
* * To analyse funnel (inc by `landing_page_path`) use:
	- `shopify_sales_main.raw.polar_pixel_funnel_sessions`
	- `shopify_sales_main.raw.polar_pixel_funnel_product_viewed_sessions`
	- `shopify_sales_main.computed.polar_pixel_funnel_product_viewed_sessions_rate`
	- `shopify_sales_main.raw.polar_pixel_funnel_product_added_to_cart_sessions`
	- `shopify_sales_main.computed.polar_pixel_funnel_product_added_to_cart_sessions_rate`
	- `shopify_sales_main.raw.polar_pixel_funnel_checkout_started_sessions`
	- `shopify_sales_main.computed.polar_pixel_funnel_checkout_started_sessions_rate`
	- `shopify_sales_main.raw.polar_pixel_funnel_checkout_completed_sessions`
	- `shopify_sales_main.computed.polar_pixel_funnel_checkout_completed_sessions_rate`
	- Do not use `shopify_sales_main.raw.polar_pixel_sessions`, or `shopify_sales_main.raw.polar_pixel_conversions` for this purpose (the breakdown does not exist).
* Platform-reported data can double-count or undercount conversions. Polar Pixel uses multi-touch attribution and avoids double-counting on clicks but does not capture view-through.
* Awareness campaigns should not be judged by conversions alone - don't recommend shifting budget away from awareness campaigns based solely on ROAS or CAC comparisons. Instead propose alternative ways to evaluate true impact.
* The most interesting marketing channels and campaigns are the ones with higher spend, plus free channels like email/CRM, organic search, organic social and LLMs.
* Recently launched campaigns may have unstable or insignificant performance if still in learning phase (judge this by whether there was also spend previous week).
* Branded search often silently cannibalises organic traffic that would have been free, caveat this if recommending that branded search is performing well and should be increased.
* We call Meta Ads Facebook Ads.
* To get creative metrics, query a Facebook Ads metric or a Polar Pixel metric with the `ad_name` dimension applied.
* To evaluate health, if no CAC/ROAS targets provided, use this framework:
  - Aggressive growth: CAC up to 100% of 180d LTV (breakeven by 6 months)
  - Balanced growth: CAC = 50% of 180d LTV (2x LTV:CAC ratio)
  - Profitable growth: CAC = 33% of 180d LTV (3x LTV:CAC ratio)