How to Test Ad Copy for Better Results

I just audited an account spending $573k/mo on Meta This one mistake loses them $100k+/mo: Killing ads too early. Let me explain: Being profitable at scale is the goal of most DTC brands. And all brands understand that creative is the biggest lever they can pull to achieve this scale. This means they produce ad creative in high quantities. But are testing them completely wrong. For many DTC brands, simply testing 200 ads per month is the goal. → Creative production is rushed → Corners at cut to meet delivery deadlines → Statistical significance in the creative testing process → $100k+ of testing budget is lit on fire only to find ads that have a 0.9x ROAS at scale This suboptimal creative testing means winning ads are killed early. And bad ads are moved to scaling campaigns based off 'early signals'. Which eventually grinds the ad account to a halt. With no new winning ad being seen in the account for months. However, this is all be solved with a robust creative testing process. So here’s the exact creative testing process we will be implementing for the brand I audited: 1. Pre-Launch: → ABO campaign → Broad targeting → 1 concept per Ad Set, with 4-5 variations of each concept 2. After Launch: → Monitor for 5-7 days, or until ads spend more than 3 times AOV → A wining ad will have a CPA below the target → A losing ad will have a CPA above the target → Kill any losing ads → Increase budget on winning ads by 20-40% every 48 hours 3. Scaling: → When winners are found, duplicate winners to the scaling campaign → DO NOT turn off the winning ads in the testing campaign ↳ These ads are making you money, it would be foolish to turn them off But here's where most stop. And go blindly on to the next round of creatives without diving into the data. You need to take to take the time to understand WHY each ad is a winner or loser. To inform your creative strategy moving forward. Look at every element of the ad: → The creator → The angle → The pacing → The creative format (Video, GIF, Static) → The video length → The hook → The messaging → The delivery medium (brand page, whitelisted page etc.) Come up with a hypothesis as to why each ad won/lost based on the above variables. Test that hypothesis. Extract key learnings. And use these learnings as the North Star in your creative strategy moving forward. Rinse and repeat this iterative process. And profitable scale will be the new reality for your brand.

Why Your LinkedIn Ads Are Flatlining 📉 You’re not 𝙩𝙚𝙨𝙩𝙞𝙣𝙜. You’re 𝙜𝙪𝙚𝙨𝙨𝙞𝙣𝙜. I’ve reviewed hundreds of campaigns. And I can tell within 60 seconds whether it’s being run by someone who builds systems… Or someone throwing things at the wall hoping for pipeline. Here’s what I mean: ❌ Running one campaign with one audience and one ad isn’t a test. ❌ Launching a “lead gen” campaign without retargeting isn’t a strategy. ❌ Optimizing based on CTR without knowing lead quality is a trap. Want to actually test? Do this instead: ✅ Launch separate campaigns by objective (TOFU, MOFU, BOFU) ✅ Segment audiences by seniority, role, or geo ✅ Run multiple ad variations: different formats, tones, lengths, designs ✅ Create matched audiences + build 30/60/90 day retargeting buckets ✅ Analyze results beyond CTR: look at CPL, demo rate, deal value When I run campaigns, I don’t just test ads. I test messaging frameworks. Audience behavior. Funnel architecture. Every campaign is an experiment. And experiments produce insights, not just metrics. Stop launching “ads.” Start launching hypotheses. #linkedinads #b2bmarketing #linkedincowboy

How to set up a creative diversity testing structure in 45 minutes. (this is the exact process we use while managing $10M+ in Meta spend) 1. Set up your testing structure First, create your CBO campaign: - Budget: $300-500/day minimum - Objective: Purchases or NC-Purchases - Targeting: Broad 2. Build your ad set structure Create 3 themed ad sets, 1 for each angle: - Value angle ($100-150/day) - Efficacy angle ($100-150/day) - Versatility angle ($100-150/day) 3. Create your naming convention Format: [Product] - [Persona] - [Audience] - [Angle] - [Format] - [Creative] Example: Cotton Crew Shirt - Millennial Guys - Comfort - Podcast - EnemyPodcast_01 This matters because: → Easier performance analysis → Clear testing structure → Unlocks faster optimization 4. Set up your creative analytics system I use Motion to analyze ads by - Product - Persona - Audience - Angle - Format - Creative Comparison reports make it incredibly easy to drill down by each element within the ads to see how different combinations are performing. 5. Launch sequence Week 1: → Launch 5 ads per ad set → Monitor first 72 hours → Kill bottom 40% performers Week 2: → Scale winners by 20% → Add 5 new variations → Keep testing new angles Week 3: → Graduate proven winners → Move to scaling campaign → Start new test cycle Some ballpark numbers you’re looking for: - 40%+ hook rate - 7+ second avg watch time - 20%+ better CPM’s - 20%+ better CPA’s in first 72 hours --- How you organize your creative testing system. Is actually everything.

I’m still not convinced we’re doing creative strategy right…🫣 We launch ads. Test everything. Let the algorithm sort it out. More tests = more winners. More shots = more chances. More data = better decisions. …doesn’t it? I watched brands burn $50K+ a month on high-volume testing and come out with nothing but noise. Not better ads. Not scalable insights. Just… more confusion. 𝗬𝗼𝘂𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗲𝗮𝘁𝘀. If you keep feeding your brand junk data, your ad account will eventually start to feel like crap. I’ve seen this play out over and over: •Brands launch 100+ ads •90% fail instantly •Meta optimizes for the cheap clicks •You chase ghosts in the data •You spend another $50K next month We call this “testing”. But most of the time, we’re just making more expensive guesses. If you throw 100 random darts at a wall without understanding what actually makes people buy, what are you really learning? I’ve learned that scaling isn’t about running more ads—it’s about running smarter, faster, more controlled experiments. Instead of feeding the algorithm junk, the brands that actually scale are the ones running 10-15 deeply researched ads: ✔ Subconscious identity triggers (what actually makes people buy) ✔ Emotional motivators (tied to your best customers) ✔ Psychological friction points (the real reason people don’t convert) When you test like this, you don’t just get a winning ad. You get a repeatable strategy. One that scales for 𝘮𝘰𝘯𝘵𝘩𝘴, not just weeks. So yeah, I get it. Volume feels like the way forward. But I also think we did this to ourselves in an attempt to make ourselves feel good about all the things we couldn’t control because “at least we’re doing something.” But it’s time to rethink the game, here. ❓ “But doesn’t high-volume testing work?” → If you test 100 ads and 90% fail, what did you actually learn? That most of your ideas suck? That Meta prefers cheap clicks over quality? Testing should be about learning, not just filtering out losers. A research-backed system gives you better inputs, so you’re not just playing Whac-A-Mole with bad ads. ❓ “We don’t have time to overthink ads—we need to move fast.” → Moving fast is good. But wasting $50K a month on random ideas isn’t speed, it’s friction. Slowing down to test better ads actually makes your scaling process faster in the long run. ❓ “Meta rewards volume testing. It’s how the algorithm works.” → Meta rewards strong signals—not randomness. A high volume of bad ads just tells the platform you don’t know what you’re doing. When you start with better strategy, you get stronger signals faster—without burning through cash. ❓ “Okay, but what if we test both? Volume AND research-backed ads?” → If you have unlimited budget, sure. But most brands don’t. Would you rather spend $50K testing 100 random ads or $50K testing 15 highly informed ones? One approach gives you a real growth system. The other just makes your media buyer look busy.