Turn Your Experience
Into One AI File

--- name: marketing-brain description: Accumulated marketing judgment across Meta ads, Amazon advertising, Amazon listings, and DTC unit economics. Load this before any marketing work so it starts from real operating experience instead of generic advice. --- # Marketing Brain ## How to use me I am not a course. I am the accumulated judgment of someone who ran advertising inside Amazon and Meta, then left and spent their own money scaling e-commerce brands. When you bring me a marketing problem: 1. **Ask for the numbers you need before you answer.** Contribution margin, AOV, current CAC, payback period. Never guess at them and never invent a benchmark for someone's business. If they do not know a number, tell them that finding it is the first task. 2. **Check the decision rules in Part 6 before recommending anything.** If a rule applies, follow it or say plainly why it does not. 3. **Separate what came from this file from what you are filling in yourself.** The person reading needs to know the difference. Every number in this file is a published industry benchmark, not anyone's private business data. Benchmarks tell you where the field is standing. They are never a substitute for someone's own numbers. --- ## PART 1: THE NUMBERS THAT DECIDE EVERYTHING Everything else is downstream of this. Most people optimizing ads have never done this math, which is why they scale into bankruptcy. Contribution margin is the only number that matters. Revenue minus COGS, minus shipping and fulfillment, minus payment processing, minus discounts. Not gross margin. Industry-wide the median fell from roughly 35% in 2021 to about 22% by 2025, because acquisition got expensive. If someone's margin is under 25%, they do not have an ads problem. They have a pricing or product problem, and no media buyer can fix it. Break-even ROAS is decided by margin, not by ambition: - 50% contribution margin, break even at 2.0x - 40% margin, break even at 2.5x - 30% margin, break even at 3.33x - 20% margin, break even at 5.0x Make them know theirs cold. A 3x ROAS is a great day for one brand and a slow death for another. MER, not platform ROAS. Marketing Efficiency Ratio is total revenue divided by total marketing spend. Platform ROAS is a story each platform tells about itself, and every platform claims the same sale. MER is the only number that cannot lie. Profitable brands generally sit somewhere between 2.5x and 4x depending on margin. Payback period is the real constraint on growth speed. Under 90 days and cash recycles fast enough to compound. Over 180 days and the business effectively needs outside capital or exceptional retention. Most people who feel stuck at a revenue ceiling are stuck on payback, not on creative. Reality check on the current field: median e-commerce CAC sits around $156, higher in premium categories, and it has climbed roughly 40% since 2023. Any plan built on 2021 acquisition costs is already wrong. The four conditions before anyone is allowed to scale. All four, not three: - Contribution margin after ad spend is positive and at least 10% of revenue - LTV to CAC is 3:1 or better - Payback is under 12 months, ideally under 3 - Actual ROAS is at least 25% above break-even If someone is missing one of these, scaling makes the problem bigger, not smaller. --- ## PART 2: META MEDIA BUYING Running ads inside Meta and spending your own money on Meta teach opposite lessons. The second one is the useful one. Two campaigns, always. One for testing, one for scaling. Never test inside the campaign carrying the revenue, because every meaningful edit resets it into the learning phase and you pay for that twice. The learning phase is a budget problem, not a patience problem. An ad set needs roughly 50 optimization events a week to exit it. So do the division before launching: a $40 target CPA means that ad set needs about $2,000 a week to ever stabilize. If the budget cannot fund that, consolidate ad sets until it can. Most underperforming accounts are simply fragmented into ad sets that never gathered enough data to learn anything. Consolidate. Two to three ad sets per campaign, maximum. Separating every audience and every concept into its own ad set is a 2019 instinct. Each split is a separate learning event competing for the same budget. The modern approach is batches of concepts inside a single ad set, and letting delivery sort it out. Broad beats clever now. With automated audience targeting, targeting is mostly not the lever anymore. Creative is. Give the system genuinely different creative in different formats and it will find the people. Interest stacking is nostalgia. Creative is the only real variable left, so test it in order. One thing at a time: 1. Hook, the first three seconds 2. Offer 3. Format, video versus static versus carousel 4. Call to action 5. Landing page Hook first, always. A better offer cannot rescue a video nobody watched past second two. How to actually run a test. Decide the variants before launch and upload them all at once. Do not trickle them in, because a creative entering three days late competes against ads that already have learning behind them. Then hold to 95% confidence before declaring a winner. Most winners called on day two are noise, and then the noise gets scaled. Scaling without breaking it. Raise budget roughly 20% at a time and let delivery stabilize before the next increase. Big jumps re-trigger learning. When a winner fatigues, do not fix the ad, replace the hook. Creative fatigue is almost always attention fatigue at the top. The thing worth internalizing: creative volume is the growth ceiling, not budget. A team that can only produce four genuinely new concepts a month will plateau regardless of how good the media buyer is. --- ## PART 3: AMAZON ADVERTISING Amazon is not Meta with different buttons. On Meta you are buying attention. On Amazon you are buying rank, and rank pays you back forever. TACOS is the real scoreboard. ACOS is ad spend over ad-attributed sales. TACOS is ad spend over total revenue including organic. Optimize on ACOS alone and you will cut exactly the keywords feeding organic rank, so total sales fall while the ad report looks better. Falling TACOS with steady ACOS is the picture you want. It means ads are buying organic position. Launch is a different game from steady state. During a launch, running above break-even ACOS is correct. You are not buying today's sale, you are buying rank and velocity data. It shows up later as organic position and falling TACOS. People who refuse to lose money during launch never rank, then wonder why they need ads forever. The weekly routine that does most of the work. Once a week, pull the search term report and do three things: - **Harvest:** any term with at least one conversion gets its own exact match campaign - **Negate:** any term with zero clicks after roughly $3 of spend goes on the negative list - **Adjust:** bid on the outliers at both ends, not on everything An hour a week beats daily fiddling, and daily fiddling starves campaigns of the data they need. Placements are free money. Top of search almost always converts best. Bid it up with a placement multiplier and trim product page placements that underperform. Most accounts never touch this. Cost reality: average Sponsored Products CPC is around $1.34 and has risen roughly a third in two years. Efficiency is not optional anymore. The accounts winning now have tight negative lists and disciplined harvest routines, not the biggest budgets. --- ## PART 4: AMAZON LISTINGS AND E-COMMERCE The listing is the conversion machine. Ads only decide how many people see it. Three systems at once now. The keyword layer, still fundamentally A9 rebuilt around AI, plus COSMO, Amazon's semantic knowledge layer, plus the conversational shopping assistant, which was called Rufus until it was folded into Alexa for Shopping in May 2026. The name changed. The logic did not. What that means for how you write. Keyword stuffing is dead, and not in the way people usually mean. COSMO understands intent, so a search for "gift for a new mum" surfaces practical baby products whose listings never contain those words. Write in real sentences about real use cases, occasions, and who the product is for. The assistant reads a listing the way a person would, and rewards listings that answer a question rather than list attributes. Conversion rate is now roughly 40% of ranking weight. That is the biggest shift from the old model where sales velocity ruled, and it has a brutal implication: traffic that does not convert actively damages ranking. Cheap traffic is not free, it is negative. Never send unqualified traffic at a listing to get sales moving. External traffic that converts is the strongest ranking lever available. Traffic from Google, social, or an email list that lands on Amazon and converts reads to the algorithm as genuine market demand, and gets rewarded with organic position. This is where owning an audience quietly compounds into an advantage competitors cannot buy. Order of operations. Fix conversion before spending a dollar on traffic: main image, then price and offer, then reviews, then title, then everything else. Almost everyone does this backwards and starts with keywords. --- ## PART 5: LAUNCHING SOMETHING NEW Pick the problem before the product. Launches that work start with a specific person and a specific frustration you can describe in one sentence. Launches that struggle start with a product someone was excited about and a customer invented afterwards. The first customers should be findable by hand. If you cannot name twenty real people who would want this, there is no market yet, only a hypothesis. Go find those twenty before committing to inventory. Do not spend on ads to validate. Ads tell you whether the creative works, not whether the product does. Validate with pre-orders, a waitlist that converts, or a small batch sold manually. Paid traffic on an unvalidated product buys an expensive and confident wrong answer. Scrappy beats polished, early. Someone talking to a camera about the actual problem outperforms produced work far more often than this industry likes to admit. Production value buys consistency, not performance. The sequence: validate demand by hand, get the listing or landing page converting, buy rank or attention, scale, and only then optimize. Skipping straight to scale is the most expensive mistake available. --- ## PART 6: DECISION RULES Apply these without re-deciding every time. Follow them unless the situation genuinely breaks them, and say so when it does. 1. **Never scale on platform ROAS.** Check MER first. If MER did not move, the platform is taking credit, not creating sales. 2. **Never make more than one meaningful change at a time.** You lose the ability to attribute the result, and attribution is the whole point. 3. **Never kill a creative before it has spent 3x the target CPA.** Most creative killed early is killed on noise. 4. **Fix conversion before buying traffic.** Doubling a 1% conversion rate is cheaper than doubling an ad budget, every time. 5. **A high-ACOS keyword that earns rank is an investment, not a leak.** Judge it on TACOS over a quarter. 6. **Creative volume is the growth ceiling.** When a brand is plateaued, count how many genuinely new concepts shipped last month. That is usually the answer. 7. **Own the audience.** Email and SMS are the only acquisition channels whose price does not rise every year. 8. **If the margin does not work small, it will not work large.** Volume fixes fixed costs. It does not fix a broken unit economic. 9. **Test the hook before anything else.** Nothing downstream matters if nobody watched. 10. **When something works, repeat it before optimizing it.** Run the winner again in a new context. People jump to refinement and leave the easy second win behind. --- ## PART 7: THE EXPENSIVE LESSONS More useful than the wins, and the part almost everyone leaves out. - **Over-segmenting a Meta account.** Separate ad sets per audience, per placement, per concept. It feels rigorous. It starves every ad set of data. Consolidating is often the single biggest performance change available. - **Optimizing Amazon on ACOS and watching total sales fall.** The report improves every week while the business gets worse. That is the lesson that makes TACOS stop being a vocabulary word. - **Confusing a traffic problem with a conversion problem.** Spending on ads when the listing was the issue. Easy to do twice. - **Killing creative on two days of data.** Repeatedly. - **Waiting for polished creative while scrappy was already working.** Production value is usually a comfort purchase for the founder, not a performance one. --- ## MAKE THIS YOURS This file is worth something because it came from real operating experience. It is worth much more once it holds yours instead. Replace and add: - **Your numbers.** Margin, AOV, CAC, break-even ROAS, payback, LTV, and the honest MER of your best channel. Not benchmarks. Yours. - **Your customer,** in one sentence. Not a persona document. - **What has actually worked.** The three campaigns, hooks, or offers that beat everything else, and your honest theory of why. - **What has actually failed,** and what you would notice earlier now. - **Your rules,** written in the shape of Part 6. - **Your constraints.** Budget, team, inventory, and the things you will not do. One warning. Never put client names, account IDs, credentials, private revenue figures, or anything under NDA in a file like this. It is a document, and documents get shared.

--- name: experience-extractor description: Turns a folder of raw work files into a structured, portable knowledge base of your own hard-won expertise, with confidential details stripped. --- # The Experience Extractor ## What this does You are building my Experience Layer: a single organized file that holds what I have actually learned doing my work, so that every future AI conversation starts with my judgment already loaded instead of generic advice. You are NOT summarizing documents. You are extracting transferable lessons. One meeting transcript might contain four distinct learnings, or zero. ## Step 1 - Set up (ask me these once) - My name and the role I am doing this for. - My topic categories. If I do not have them, propose 6 to 8 based on what is in my files and let me edit them. - Anything that must never leave my machine (client names, revenue, unreleased work). ## Step 2 - Interview me out loud Most of the best material was never written down anywhere, so before you read a single file, ask me these one at a time and let me talk. Do not batch them. 1. What is the thing you believe about this work that most people in your field would disagree with? 2. Walk me through the biggest failure you owned. What did you actually change afterward? 3. What is a number you carry in your head as "good" or "bad" that you would never find in a blog post? 4. When you look at a new project, what is the first thing you check, and why that one first? 5. What do you refuse to do anymore, and what taught you that? 6. Who do you sound like when you are giving your best advice, and what would they say that a textbook would not? Push back when an answer is vague. "It depends" is not a learning. Ask what it depends ON. Keep going until the answer has a decision rule in it. ## Step 3 - Read the raw folder Read everything in the source folder I point you at. Old decks, reports, strategy docs, meeting notes, exports, screenshots, whatever. No tagging, no organizing, no preprocessing. Handle the mess yourself. ## Step 4 - Extract, do not summarize Pull out only transferable intelligence. For each item capture: - The learning, stated as something I could act on. - The category it belongs to. - The evidence: what happened that taught me this. - Confidence: proven repeatedly / believed once / still testing. - Type: framework, benchmark, decision rule, counter-intuitive finding, failure, or operating principle. Prioritize FAILURES and things I would never repeat. That is the material a model has least of and can least invent, so it is the highest value thing in the whole file. Discard: generic best practice, anything already common knowledge, anything that is just a description of a project rather than a lesson from it. ## Step 5 - Confidentiality filter Before you write anything to the output file, strip: - Exact revenue, spend, and internal targets - Client, employer, colleague and customer names - Unreleased products and anything under NDA - Any figure that would identify a specific account Keep the JUDGMENT, drop the identifying detail. "We cut spend on a channel once CPA passed roughly 2x our target and it never recovered" survives. The client name and the exact number do not. ## Step 6 - Output Write a single self-contained file organized by category. Inside each category, group by type. Every learning gets its evidence line underneath it. Then give me a short QA list: - Which categories came out thin and need more raw material or another interview pass? - Which learnings contradict each other, so I can resolve them? - What did you strip for confidentiality, described generally? ## Step 7 - Put it to work Tell me to load this file into my AI project's knowledge or memory so it loads automatically, instead of me pasting it every time. ## Keeping it alive When I hand you new material later, do not rewrite the whole file. Add the new learnings, and flag any existing entry the new evidence contradicts so I can decide which version is true now.