Before you answer, run Kickbite's Trust Center check for [Market] (conversions and revenue, last 30 days). If it shows a tracking or processing issue, stop and tell me the affected days instead of analysing.

For [Market]: read contribution margin after product cost (CM2 %) from Kickbite for the last 90 days. If CM2 % shows 100%, no product cost is configured; tell me, and use a gross margin of [X]% that I give you instead.
Then work out, per campaign type, the metric and the bands I should use:
- Non-brand Search and Shopping: cost per new customer. Ceiling = new-customer AOV × margin, plus the extra margin a new customer brings back within [90] days from Kickbite's cohort reorder and CLTV data. Bands: scale ≤ 80% of ceiling, hold 80–110%, fix 110–150%, pause above 150% or ROAS below break-even.
- Performance Max: ROAS on new-customer revenue only. Break-even = 1 ÷ margin. Bands: scale ≥ 1.3× break-even, hold 1.0–1.3×, fix 0.75–1.0×, pause below.
- Demand Gen / YouTube and Meta prospecting: cost per new customer including first-click and assisted orders (AI Click & View), same bands as Search.
- Retargeting and catalog: cost per new customer with returning revenue counted at zero.
- Brand Search and Shopping: impression share, not ROAS; flag only if ROAS falls below 1.5× break-even.
Show the maths for each number and give me the block to paste into the other prompts.

For [Market], last 6 weeks, weekly, per paid channel: spend, clicks, Kickbite sessions, Kickbite orders and revenue, and the orders the platform reports (7-day click, 28-day click, 1-day view).
Flag any week where a channel has spend and clicks but zero or near-zero Kickbite revenue while the platform still reports orders: that is a tracking failure (template, UTM, click ID), not an attribution difference. Flag separately any campaign or ad set where the platform reports orders but Kickbite shows €0 for the whole period.
For the clean weeks, give the platform ÷ Kickbite ratio per channel. A ratio of 1.2–2× on Meta and 0.6–1.1× on Google is normal; outside that, say what to check.
Only tag campaigns on the weeks that pass this gate, and say which weeks you excluded.

For [Market], last 30 days, Google channels (Search Brand, Search Non-Brand, Shopping, PMax, Demand Gen): Kickbite orders and revenue next to Google-reported conversions and conversion value.
If Google reports fewer orders than Kickbite (ratio below 0.8) or almost nothing for Demand Gen, say so: Smart Bidding is then optimising on under-reported value and every tROAS target in Google is miscalibrated. Give the calibration factor per campaign type to use in prompt 1.4.

Using Kickbite for [Market], last 30 days, Google Ads only, Brand Search campaigns excluded:
Break performance down by campaign and ad group (and by the lowest level Kickbite has below ad group, if it exists). Show spend, Kickbite revenue, ROAS, new-customer ROAS, orders and the orders Google reports.
Apply my per-campaign-type targets [paste thresholds]. Then list:
1. The 10 ad groups furthest above target that have at least €300 spend.
2. The 10 ad groups furthest below target that have at least €300 spend.
3. Ad groups with €300+ spend and zero Kickbite orders. For each, say whether Google still reports orders.
4. Ad groups where Google reports at least 2x more orders than Kickbite, or fewer than half (see "Check Google's conversion tracking").
For each item, give one action (scale, cut budget/raise tROAS, pause, or check search terms in Google Ads) and the € involved. End with the 5 changes to make this week, ordered by € impact.

Kickbite has no search-term data, so use it to tell me where to look in Google Ads.
For [Market], last 30 days, non-brand Google Search and Shopping campaigns: show each ad group's spend, clicks, Kickbite sessions, conversion rate, Kickbite orders and the orders Google reports.
Flag ad groups that meet at least one of these, with €200+ spend:
- Kickbite orders are less than half of Google-reported orders.
- Conversion rate is below 50% of the campaign average.
- Clicks are more than 1.5x Kickbite sessions (paid clicks that never become a tracked visit).
Rank the flags by spend. For the top 5, tell me exactly which search-term report to open in Google Ads (campaign > ad group, date range), and what kind of query to look for, based on the ad group name.
End with an estimate of the € at risk across the flagged ad groups.

For [Market], last 30 days, Google Ads excluding Brand Search: find campaigns and ad groups with €300+ spend where any of these is true:
- Zero Kickbite orders.
- Kickbite ROAS below 50% of my target for that campaign type [paste thresholds].
- Profit (POAS) below 1, i.e. the campaign loses money after product costs.
For each: spend, Kickbite revenue, ROAS, POAS, Google-reported orders, and the share of its orders that are new customers.
Before recommending a pause, look at the customer journeys through that campaign. If it often appears early in paths that convert later, mark it "assist, reduce" not "pause".
Sort by spend, give the total € flagged, and list this week's actions.

For [Market], last 60 days vs the 60 days before, Google Ads non-brand:
1. For each campaign: Kickbite orders ÷ Google-reported orders = my calibration factor. If Google says tROAS 5.0 and the factor is 0.6, the real ROAS is about 3.0.
2. Using that factor, give the tROAS (or tCPA) each campaign should run in Google Ads to hit my Kickbite targets [paste thresholds].
3. Flag campaigns where CPC rose more than 15% and Kickbite orders didn't rise.
4. Flag campaigns more than 20% above target that have less than 10% of Google spend (candidates to scale).
Output: campaign | current Google ROAS | Kickbite ROAS | factor | suggested tROAS/tCPA | action.

Quality Score is only in Google Ads. Use Kickbite to find where rising click costs are actually hurting results.
For [Market], Google Ads non-brand, weekly over the last 12 weeks, by campaign: CPC, conversion rate, Kickbite ROAS, spend.
Flag campaigns where CPC rose more than 20% over the 12 weeks while conversion rate or ROAS fell. Those are the campaigns to check for Quality Score and landing-page experience in Google Ads.
Rank them by € of extra cost: (current CPC − CPC 12 weeks ago) × current weekly clicks.
End with the 3 campaigns to look at first and what to check in each.

For [Market], last 30 days, paid Google and Meta traffic, by campaign and ad group (and by UTM content where you have it):
Show clicks, Kickbite sessions, the click-to-session rate, conversion rate, cost per session, AOV and ROAS.
Flag:
- Rows with 100+ clicks where sessions are under 70% of clicks (traffic lost between the ad and the site: slow page, redirect, broken tracking).
- Rows with 200+ sessions and a conversion rate below 50% of the channel average.
For each flag, name the likely landing page from the campaign/ad group naming, and the likely fix (speed, message match, offer, tracking).
Sort by spend and end with the 3 pages to fix first.

For [Market], last 30 days, by campaign (Brand Search excluded): CTR, click-to-session rate, conversion rate, cost per order (Kickbite), new-customer CAC.
Identify:
1. Campaigns with CTR above the channel median and conversion rate below it. The ad promises something the page doesn't deliver.
2. For each campaign, the reduction in new-customer CAC if conversion rate rose by 20%, in € per month.
Rank by € saved and say where the conversion work should go first.

Brand campaigns are excluded from the prompts above, but they still need a check: a brand campaign with a low ROAS is usually matching non-brand queries.
For [Market], last 30 days, Brand Search and Brand Shopping campaigns: spend, Kickbite ROAS, new-customer share of orders, returning-customer revenue share, and the 15-day trend.
Flag brand campaigns with ROAS below 1.5× break-even (from "Set my targets") or a new-customer share above 50%: those are taking generic queries and should be audited for search terms and negatives in Google Ads. Flag brand campaigns whose spend rose more than 30% without a rise in orders: impression share is already full.

For [Market], last 30 days, Meta creatives with at least €200 spend:
Show spend, Kickbite revenue, ROAS, new-customer ROAS, CAC, CTR, CPM and conversion rate. Show ROAS under both AI Click and AI Click & View.
List:
1. The top 5 by new-customer ROAS.
2. The bottom 5 by new-customer ROAS.
3. Creatives with €200+ spend and zero Kickbite orders.
4. Creatives that only look good when view-through is counted (Click & View ROAS more than 2x Click ROAS).
Use the creative name and body text to describe what the top 5 share (format, hook, offer) and what the bottom 5 share.
End with: which creatives to scale, which to cut, and 3 new variants to brief.

Kickbite has no frequency data. Use weekly trends instead.
For [Market], Meta creatives with €150+ spend in the last 7 days: show weekly CTR, CPM, new-customer ROAS and spend for the last 6 weeks.
Flag a creative as fatigued if, compared with its best 2-week stretch:
- CTR is down more than 25%, and
- new-customer ROAS is down more than 30%, while
- CPM is flat or rising.
Total the last-7-day spend on fatigued creatives. For each one: refresh (same angle, new hook), replace, or keep (still above my ROAS target of [X]).

Kickbite has no demographic data. Use ad sets as the audience.
For [Market], last 30 days, Meta and TikTok, by ad set (sub-campaign): spend, new-customer share of orders, new-customer CAC, ROAS, new-customer ROAS, and returning-customer revenue.
Split ad sets into prospecting and retargeting by name. For each group:
- The top 3 and bottom 3 ad sets by new-customer CAC.
- Prospecting ad sets where more than 40% of orders come from existing customers (they're retargeting in disguise).
- Retargeting ad sets that take more than 20% of spend.
End with: audiences to scale, to cut, and to exclude existing customers from.

For [Market], last 30 days, Meta ad sets with €300+ spend: show spend, Kickbite orders, Meta-reported orders (7-day click and 1-day view), ROAS (AI Click), ROAS (AI Click & View) and POAS.
Flag:
- Zero Kickbite orders.
- Meta reports at least 3x more orders than Kickbite, and most of the gap is 1-day view.
- POAS below 1.
Sort by spend. Give the € total and a pause / cut / keep call for each, with one line of reasoning.

For [Market], last 60 days, Meta, by campaign and ad set: spend, new-customer CAC, new-customer ROAS, reorder rate of the customers acquired.
1. Rank ad sets by new-customer CAC (€300+ spend only).
2. Look at customer journeys that include Paid Social. How often does a converting customer touch two or more Meta campaigns? If that's above 30%, flag likely audience overlap.
3. Find the ad sets whose new customers reorder most, from the customer value data.
Recommend: 2 ad sets to scale, 2 to merge or narrow, 1 targeting test.

For [Market], last 90 days, Meta creatives with at least €500 spend:
Group by ad body text, then by creative name. Show spend, new-customer ROAS, CAC, CTR and conversion rate.
Compare the top 10 and bottom 10 by new-customer ROAS. What hooks, offers, formats (video/static/UGC, from the naming) and message angles does each group share?
Check whether the winners still win under AI Click only (not just AI Click & View).
Give 3 angles to keep, 2 to retire, and 5 concrete new variants (hook + format + offer).

Write my monthly marketing report for [Market]: last full calendar month vs the month before, and vs the same month last year.
First check Kickbite's data health for the month and mention any issue. If POAS equals ROAS (no product cost configured), say so and use the margin from "Set my targets".
Per channel (Brand Search and Direct shown separately, not counted as wins): spend, Kickbite revenue, ROAS, new customers, new-customer CAC, POAS, returns rate.
Compare actuals with my Kickbite targets for revenue and spend for the month.
Structure:
- 3-sentence summary: on or off target, why, what we do next.
- Channel table.
- Top 3 wins, top 2 problems (with €).
- 3 actions for next month, each with an owner and the € expected.
Plain language for a CEO. No jargon.

For [Market], last 7 days vs the previous 7: total spend, Kickbite revenue, ROAS, new customers, new-customer CAC, plus the same by channel.
Also show month-to-date spend and revenue vs this month's targets, and the pace needed to hit them.
Write under 150 words for a non-specialist: one win, one issue, one action, each with a number. Then a simple table.

Daily check for [Market]. Compare yesterday and the last 7 days against the previous 7 days.
1. Data health first: is Kickbite tracking or order processing broken for any day? If yes, that is the only Critical item.
2. Then per channel and per campaign with €100+ spend in the last 7 days, flag:
- Spend up more than 30% with Kickbite orders flat or down.
- Cost per order or new-customer CAC up more than 25%.
- ROAS below my target for that campaign type [paste thresholds].
- Zero Kickbite orders on two days in a row with spend.
Give a severity for each (Critical / High / Watch) and one action. Keep it readable in 5 minutes.

For [Market], this month to date: spend and Kickbite revenue by channel, and month-end projections at the last 7 days' daily rate.
Compare with this month's spend and revenue targets in Kickbite.
Flag:
- A projected month-end spend more than 10% over or under target.
- Channels whose daily spend in the last 7 days is 40%+ above their month-to-date average.
- Channels whose spend dropped to near zero (possible pause or broken cost import).
Show the daily spend needed for the rest of the month to land on target, and whether revenue is projected to hit target at the current ROAS.

For markets [list, e.g. DE, AT, CH], this month to date: spend, Kickbite revenue and ROAS per market, the month's spend and revenue targets, and projected month-end values at the last 7 days' rate.
One table sorted by projected spend deviation, with each market's currency shown. Flag markets more than 10% off target on spend or revenue, and say for each whether to slow down, push, or leave it.

For [Market], last 30 days, all paid channels: spend, Kickbite revenue, ROAS, orders, cost per order, new customers, new-customer CAC, and the orders each platform reports itself.
Add a column: platform-reported orders ÷ Kickbite orders (how much each platform over- or under-claims).
Show Brand Search and Direct on separate rows and don't rank them.
Summarise in 3 sentences which channel is the most efficient at bringing in new customers, which platform over-claims most, and whether any platform under-reports (ratio below 0.8).

For [Market], last 60 days, paid channels except Brand Search:
Rank channels by new-customer CAC and by POAS. Show both AI Click and AI Click & View results.
For the best and worst channel, look at weekly spend vs weekly new-customer ROAS. Does the best channel's return drop in its higher-spend weeks? If so, say so.
Then estimate what moving 15% of the worst channel's budget to the best would do to new customers and profit. Discount the gain if the best channel shows saturation.
End with a recommended split in €.

For [Market], last 30 days, all campaigns whose name contains "[keyword]", grouped by channel:
spend, share of spend, Kickbite revenue, share of revenue, new customers, CAC.
Does each channel's share of spend match its share of Kickbite revenue and new customers? Look at customer journeys for these orders: which channel usually comes first and which closes?
Give a two-sentence recommendation on the split.

For [Market], last 30 days, all paid campaigns with €500+ spend, excluding Brand Search:
Score each against my target for its campaign type [paste thresholds]: Kickbite ROAS, new-customer ROAS, POAS.
Take the bottom 3 and top 3 by distance to target. Model moving 20% of the bottom 3's budget to the top 3, discounting the top 3's return by 20% to allow for diminishing returns.
Show the expected change in revenue, new customers and profit. End with the exact € moves per campaign.

For [Market], last 12 weeks, paid campaigns excluding Brand Search:
Find campaigns that are 20%+ above my target [paste thresholds] and have less than 10% of channel spend.
For each, show weekly spend vs weekly new-customer ROAS. Did ROAS hold in the weeks with higher spend? Only call a campaign "ready to scale" if it held.
For those campaigns: suggest a budget increase (+20% or +30%), the expected extra new customers, and where the money should come from (the furthest-below-target campaign in the same channel).

For [Market], customers acquired in the last 12 months: by channel and by campaign, show new customers, new-customer CAC, average customer lifetime value, reorder rate, and returns rate.
Calculate CLTV ÷ CAC per campaign (€300+ spend only).
Which campaigns bring customers worth the most over time, and which bring one-off buyers or high returners?
Recommend which campaigns deserve a higher CAC ceiling, and where to build a lookalike or retargeting audience from.

For [Market], last 90 days, by channel: Kickbite orders, lifetime-click orders, first-click revenue, last-click revenue, and the orders each platform reports (1-day view, 7-day click, 28-day click).
Which channels get more credit with lifetime click tracking than any platform window gives them? Which get less?
Tell me which channels I'm undervaluing if I judge them in their ad platform, and by roughly how much in €.

For [Market], last 60 days: show the 15 most common conversion paths, average touchpoints, and average days to conversion. Show the split between single-touch and multi-touch, and between click and view.
Per channel, compare first-click revenue, last-click revenue and Kickbite revenue. Label each channel opener, closer or both.
Name one channel I'd wrongly cut if I only looked at last click, and how many euros of revenue it starts.

For [Market], last 6 months by month and channel (Brand Search and Direct excluded): new customers, new-customer CAC, first-order AOV.
Using cohort CLTV and reorder data, estimate how many months each channel's CAC takes to pay back.
Flag channels where CAC rose more than 20% over the 6 months. Tell me the maximum CAC I can afford per channel to pay back within [6] months.

For [Market], last 16 weeks, top 5 paid campaigns by spend (Brand Search excluded): weekly spend, new-customer ROAS, CAC and CPM.
For each campaign, describe how new-customer ROAS changes as weekly spend goes up. Is it flat, slowly falling, or falling fast?
Estimate the weekly spend level where CAC goes above €[target CAC]. Rank campaigns by headroom in €/week.

For [Market], my revenue targets are: [YYYY-MM: €X, YYYY-MM: €Y, YYYY-MM: €Z].
Using Kickbite's projection based on my cohort reorder behaviour, show for each month: revenue from returning customers, revenue needed from new customers, new customers needed, and media budget at current CAC.
Compare that media budget with the spend targets I have in Kickbite. Report the model's backtest error. Then rerun with CAC 15% higher to show the risk.

For [Market], last 30 days, by channel and by top 20 campaigns: ROAS, ROAS after returns, POAS, contribution margin 2, returns rate.
Which campaigns look fine on ROAS but lose money after product costs and returns? Rank them by € of lost margin and give one action each.

For [Market], last 90 days: the top 15 products by new-customer revenue, broken down by channel. Show each product's share of new-customer revenue and new-customer AOV.
Which products bring in new customers on Meta versus Google? Which products should lead the creatives and the Shopping/PMax feed?

For [Market]: run the Kickbite coupon data-quality check. List duplicate coupon rules, influencer codes without a name, and codes outside their active window.
Then show last 90 days' Influencer channel revenue, new customers, and CAC including the extra costs logged for influencers.
Tell me which coupon issues to fix first and which influencers are worth renewing.

For [Market] (or [list of markets]), check Kickbite's Trust Center for the last 30 days on conversions, revenue and costs, by day.
List the days and markets with a tracking issue (Kickbite tracking far below the shop backend) or a processing issue (backend near zero). Say which numbers in this week's reports are affected and whether they can be reprocessed.

For [Market], last 60 days, weekly and by channel: new-customer orders and revenue, returning-customer orders and revenue, new-customer share of orders, AOV for new vs returning customers.
Flag channels where more than 70% of orders come from returning customers (retention channel, don't judge it on acquisition) and channels where more than 60% are new (acquisition channels to protect).
Is the store-wide new-customer share growing, flat or shrinking week over week?
Then, from Kickbite's reorder data for the cohorts acquired 2 months ago: what % placed a second order, and what are the median days to the second order?
End with which channels should be judged on new-customer ROAS and which on blended ROAS. If returning customers are more than 60% of store revenue, say that acquisition must be judged on customer value ("Set my targets"), and report whether recent cohorts' second-order rate is above or below cohorts from a year ago.

For [Market], last 7 days vs the 7 before:
1. Store totals: revenue, orders, new customers, returning customers, AOV, sessions, conversion rate, revenue per session (revenue ÷ sessions), spend, ROAS, new-customer CAC, returns rate.
2. Flag if: conversion rate fell while sessions rose; orders rose but AOV fell; returning share rose while new customers fell; revenue growth came mainly from more sessions rather than conversion or AOV.
3. Products: top 10 by revenue, the 5 biggest risers and fallers (week over week), and each one's new-customer revenue share.
4. Check data health for the week first and mention any issue.
Write a 3-sentence summary (stronger or weaker week, the main driver, one risk), then the tables, then 3 actions for next week.

For [Market], last 90 days, by product (then by SKU for the top 20): revenue, net revenue, items sold, orders, AOV of orders containing it, new-customer revenue share, and new vs returning items sold.
Flag:
- Products in the top 20 by revenue where net revenue is more than 15% below revenue.
- Products with low volume but a high share of returning-customer items (loyalty drivers).
- Products bought mainly by new customers (good ad and feed heroes).
Kickbite has no product-level ad spend, discount amounts or per-product refund counts. Say where shop data is needed for those.
End with 3 products to push in ads and 3 to review.

For [Market]: by SKU, units sold per day averaged over the last 14 days and over the last 30 days, and whether velocity is speeding up (14-day average more than 20% above the 30-day average). Top 30 SKUs by units sold.
I'll give you current stock per SKU: [paste SKU, stock]. Calculate days of stock left and label Critical (<7 days), High (7–14) or Medium (14–21).
For Critical SKUs, list the campaigns and channels selling them most, so I can pull ad spend before they sell out.

For [Market]: revenue and items sold by month for the last 24 months, by product category (product attribute 1) and for the top 10 products.
For each category: YoY growth over the last 3 months, the trend over the last 6 weeks, and the seasonal index for [upcoming months] from last year.
Flag categories growing more than 25% YoY (raise stock and ad spend), negative 2+ months in a row (review range or pricing), or more than 15% above their 6-week average (early trend).
Then run Kickbite's revenue projection for the next 3 months at targets [YYYY-MM: €X, ...]. Show the new customers and media budget needed, and the model's backtest error.

For [Market], last 60 days vs the 60 days before:
1. The top 20 products by net revenue, and the top 10 by revenue growth (minimum 50 orders).
2. By category (product attribute 1): revenue, growth, items sold, and revenue per item (to show whether growth came from volume or price).
3. Emerging products: under €100 in the earlier period, more than €500 now.
For each riser, show which channel drives it and its new-customer revenue share.
End with the 3 products to feature in creatives and the feed next.

For [Market], customer cohorts by acquisition month over the last 24 months:
- Reorder depth: what % reach a 2nd, 3rd, 4th and 5th order?
- CLTV, average orders and AOV per cohort.
- Median and average days between 1st→2nd and 2nd→3rd order.
Which channels and campaigns acquire the customers who reach 3+ orders (use CLTV and reorder rate by campaign)?
Define "at risk" for my store as no order after 1.5× the median repurchase gap, and give me that number of days so I can build the segment in my shop or email tool.

For [Market]: list the coupon codes in Kickbite with the channel and UTM campaign each one maps to. Run the coupon data-quality check.
Then, last 90 days, for the channels and campaigns those codes map to (e.g. Influencer): revenue, orders, new-customer share, AOV, returns rate, and CAC including logged extra costs.
Flag campaigns where more than 70% of orders are from returning customers (discounts given to people who'd buy anyway) and where the returns rate is 5+ points above the store average.
Kickbite has no discount € per order. Say where shop data is needed for that.

For [Market], last 90 days: returned orders, returned revenue and returns rate overall, for new vs returning customers, by channel and by the top 20 campaigns.
Flag channels and campaigns where the returns rate is more than 5 points above the store average. Show their ROAS before and after returns, and their POAS.
Which campaigns would drop below my ROAS target [X] once returns are counted?
Product-level return rates aren't in Kickbite. Say if shop data is needed.

For all my markets [list], last 60 days, by market: revenue, orders, AOV, returns rate, new-customer share, reorder rate, spend, ROAS, new-customer CAC, POAS.
Flag markets with ROAS 30%+ above average (scale), 30%+ below with more than €2,000 spend (cut), and markets with above-average AOV and reorder rate but low spend (test paid).
Show revenue and spend vs this month's targets per market.

For [Market]: median and average days between orders for the 1st→2nd, 2nd→3rd and 3rd→4th order. Show the reorder-depth curve for cohorts from the last 12 months.
Are recent cohorts reordering less than cohorts from a year ago at the same age? If so, by how many points?
Give me the "overdue" cut-off in days per order number (1.5× median) to build win-back segments in my email tool. Estimate the revenue at stake if the reorder rate fell by 5 points.

Scan all my clients' markets [list, or "all markets I have access to"], last 7 days vs the 7 before.
1. Data health per market: any tracking or processing issue in the last 7 days? List those first.
2. Per market and channel with €500+ spend in the last 7 days, flag: spend up 30%+ with Kickbite orders flat or down; cost per order or new-customer CAC 50%+ above the 30-day average; spend dropped to near zero (paused or broken cost import); ROAS below that client's target.
Output one table: client | market | channel | issue | change | severity (Critical/High/Watch) | next step. Sort by severity, then spend.

For [Client market], last 60 days: list my saved creative segment groups. If there's one for format (image / carousel / video / UGC), use it. If not, group creatives by the format tag in their names.
Per format and platform: spend, share of spend, new-customer ROAS, CAC, CTR, CPM, conversion rate. Show ROAS under AI Click and AI Click & View.
Which formats get more budget share than their share of new-customer revenue? Give a creative-mix recommendation in % of spend.

For [Client market], last month: top 15 products by revenue, broken down by channel.
For each product: share of revenue from paid channels vs organic/direct/email, new-customer revenue share.
Flag products with strong revenue mostly from organic channels and little paid support (test in ads), and products relying mostly on paid channels with a low new-customer share.
Kickbite has no ad spend per product, so there's no product ROAS. If you have a product segment group or product-named campaigns, use campaign ROAS as a proxy.

For [Market], last 30 days, Email channel, by UTM campaign: sessions, conversion rate, Kickbite revenue, orders, new vs returning orders, assisted orders, and revenue under first click vs last click.
Rank campaigns by Kickbite revenue. Flag campaigns with 500+ sessions and a conversion rate below half the email average.
Look at customer journeys that include Email. How often is Email the last touch after a paid channel opened the journey?
Recommend which campaign types to send more of and which to restructure.

For [Market], last 30 days, all channels: sessions, revenue, revenue per session, conversion rate, share of sessions vs share of revenue, new-customer share, assisted orders.
Is Email's share of revenue above or below its share of sessions? Is email revenue mostly from returning customers (expected) or does it also convert first-time buyers?
Show Direct and Brand Search separately, and don't treat them as performance.

For [Market], last 90 days, Email channel by UTM campaign: find the flows/automations (names containing welcome, abandon, post-purchase, winback, or [your naming]).
Per flow: revenue, orders, new-customer share, conversion rate, and the monthly trend.
Flag flows whose revenue fell more than 20% vs the previous 90 days, and abandon-cart flows that have a lower conversion rate than newsletters.

For [Market], last 60 days: show the most common journey paths that start with a paid channel and end with Email, and how many days they take.
By Google Ads and Meta campaign (Brand Search excluded): sessions, conversion rate, new-customer share.
Flag campaigns with 1,000+ sessions and a conversion rate below half the channel average. These are the visitors worth capturing for email.
For the top 3, suggest a capture offer and a 3-email follow-up based on what the campaign sells.

For [Market], last month vs the month before and vs the same month last year, Email channel: revenue, orders, share of total revenue, new vs returning orders, assisted orders, conversion rate, top 5 campaigns by revenue.
3-sentence summary, a table, the top 3 campaigns and 3 actions. Note that opens, clicks and list growth come from the email tool, not Kickbite.

For [Market], last 90 days, weekly: Organic Social and Paid Social sessions, conversion rate, revenue, new-customer share, assisted orders, and revenue under first click vs Kickbite attribution.
How often does Organic Social open a journey that converts later through another channel? Use customer journeys that include it.
Which weeks had a jump in Organic Social sessions? I'll match them to posts: [paste post dates/topics].
End with whether organic social should be judged on revenue it closes, or on journeys it starts.

For [Market], last 60 days: Organic Social vs Paid Social sessions, revenue, new customers, assisted orders, conversion rate. For Paid Social, add spend, ROAS, new-customer ROAS and CAC.
In journeys with both, which usually comes first?
Then list the 10 Meta creatives with the highest new-customer ROAS (€200+ spend) with their Instagram permalinks. Are boosted organic posts beating purpose-made ads?

For [Market], last 30 days, Meta campaigns with €300+ spend: CPM, CTR, CPC, Kickbite ROAS, new-customer ROAS, CAC.
Flag campaigns with CPM above €15 and CTR below 1%, and campaigns where CTR fell 25%+ vs the previous 30 days.
Then check: do the "expensive" campaigns by CPM/CPC still have good new-customer ROAS? Only flag them for cutting if they don't.

For [Market], last month vs the month before: per social channel (Organic Social, Paid Social by platform): sessions, revenue, new customers, assisted orders; plus spend, ROAS and CAC for paid.
3-sentence summary for a CEO, a table, and one win, one issue and one action per channel. Note that reach, engagement and followers come from the platforms, not Kickbite.

For [Market], last 90 days, Meta creatives with €200+ spend: new-customer ROAS, CAC, CTR, CPM, and the Instagram permalink.
Split them into boosted organic posts and purpose-made ads, using the creative name or permalink. Compare spend share and new-customer ROAS for the two groups.
Tell me whether boosting organic posts is worth more budget. List the 5 boosted posts to keep spending on and the 5 to stop.

For [Market], last 30 days: Organic Search, Google Ads non-brand, Organic Social, Paid Social. Show sessions, conversion rate, revenue, new customers, new-customer share, assisted orders, revenue per session. Add spend, ROAS and CAC for the paid ones.
Which channel converts best? Which brings the most new customers per session? How often does Organic Search start journeys that a paid channel closes, and the other way round?

For [Market], last 60 days: of the orders where non-brand Google Ads gets credit, what share also had an Organic Search touch in the journey? By campaign.
Campaigns with a high organic overlap are the ones to check in Google Ads for queries you already rank on. List the top 5 and the € spent.

For [Market], last 7 days vs the 7 before and vs the same week last year, Organic Search: sessions, conversion rate, revenue, new customers, assisted orders, share of total revenue.
3-sentence summary, a table, one win, one issue. Rankings, queries and pages come from Search Console, not Kickbite. Mark where they're needed.
For a monthly stakeholder version, ask for 30 days and 200 words.

For [Market], last 6 months, by channel and source: new customers, revenue from them, AOV, CLTV, reorder rate, average days from first touch to first order (from journeys).
Rank by revenue from new customers. Flag sources with many new customers but CLTV 25%+ below average, and sources with high CLTV but few customers (worth scaling).

For [Market], year [YYYY]: per month, revenue and spend targets in Kickbite vs actuals, plus ROAS and new-customer share vs target. Year to date: % of the annual spend and revenue target used, and year-end projections at the last 3 months' run rate.
Rate each month and the year On Track / At Risk / Off Track (more than 10% variance). For the year-end gap, give the monthly spend and revenue needed from now on to land on plan.
Extra Costs logged in Kickbite (agency fees, influencers) count in spend. List them.

For [Market], each of the last 4 quarters and the same quarters a year earlier: revenue, net revenue (after returns), cost of goods, contribution margin 1, 2 and 3 (and %), marketing spend, POAS.
Flag quarters where CM3 % fell more than 5 points quarter on quarter. Say which driver caused it (returns, product margin, marketing cost share) and which channels' cost share rose.

For [Market], last 30 days by day: Kickbite-tracked conversions and revenue vs the shop backend (Trust Center), and spend in Kickbite vs ad platforms.
List days where tracked revenue is more than 5% below the backend, with the likely cause (tracking loss vs processing delay) and whether it can be reprocessed.

For [Market], last month vs the month before and vs last year: revenue, net revenue, new customers, new-customer CAC, marketing spend and cost share, ROAS, POAS, CM3 %, returns rate, revenue and spend vs target.
Flag any KPI that moved more than 15% and give a one-sentence cause. 3-sentence summary for the board. Cash, receivables and opex are outside Kickbite.
For an investor-style version, add sessions and conversion rate.

For [Market], last 13 weeks, weekly: total spend, revenue, ROAS, new customers, new-customer CAC, and the same by paid channel.
Mark weeks where CAC rose more than 20% vs the 4-week average and say which channel caused it. Did any week have a data-health issue?

For [Market], last 14 days vs the 14 before: ROAS and revenue share by channel; top 5 and bottom 5 campaigns by distance to my ROAS targets [paste thresholds] (Brand Search excluded, €300+ spend); revenue by category and the top 10 products with their main channel.
Flag anomalies (any metric ±25%) and give 5 actions to improve ROAS, each with the € involved.

For [Market]: Meta (Paid Social) monthly spend, Kickbite revenue, new-customer ROAS and CAC for the last 12 months.
Run Kickbite's revenue projection for my total revenue targets for the next 3 months [YYYY-MM: €X, ...] and show the media budget and new customers needed.
Assuming Meta keeps its current share of new customers, what Meta spend does that imply? Is it above any spend level where Meta's CAC rose in the past?

For [Market], last 30 days: list all utm_source, utm_medium and utm_source/medium values with their sessions and revenue. Also list my Kickbite channel rules.
Find: traffic that falls into a catch-all or unassigned channel, inconsistent spellings (facebook vs fb vs Facebook), paid traffic without utm_medium=cpc/paid, and campaigns with no utm_campaign.
Estimate the revenue affected. Propose a naming convention and the exact channel-rule changes to make. Don't change anything; give me the list to review.

For [Market], last 90 days, all paid campaigns with €500+ spend (Brand Search excluded): spend, new-customer ROAS, CAC, POAS, assisted orders, and the weekly spend-vs-return trend.
List campaigns where cutting 20–30% would cost the fewest new customers: low new-customer ROAS, POAS below 1, few assists, and no sign of starting journeys.
Show the € saved and the estimated new customers lost. Keep total new customers within 5% of the current level.