How to Measure Ad Performance

Your Ad Spend 30 Days Ago Probably has MORE Impact on Today's Sales, than Today's Spend. What most Premium/Luxury brands forget... People can take weeks, months or years to purchase from you— So how can you judge a new ads performance so quickly? You're killing profitable campaigns before they even have a chance to work. The culprit? Attribution lag and the Ad Stock Effect. 𝐀𝐝 𝐒𝐭𝐨𝐜𝐤 𝐄𝐟𝐟𝐞𝐜𝐭: The lingering impact of advertising that builds cumulatively over time before triggering a purchase. Real industry data reveals: • 97% of retail conversions happen within 10 days of first ad exposure • B2B buyers need an average of 31 touchpoints across 6-12 month cycles • High-ticket items ($500+) require 30+ day attribution windows • Cart abandonment averages 71.3% across all categories - but many of these "lost" sales convert later Most founders judge campaign success on daily metrics, missing the full revenue picture. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐒𝐡𝐨𝐩𝐢𝐟𝐲 𝐱 𝐌𝐞𝐭𝐚 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩: 1️⃣ Today's ad spend often drives sales that show up days later 2️⃣ Post-iOS 14.5, Meta lost 93.5% of its attribution accuracy - only 6.5% of baseline tracking remains 3️⃣ If someone clicks Monday and buys Thursday, Thursday's sales partly came from Monday's spend 4️⃣ Single-day ROAS metrics miss this natural delay in the customer journey 𝐇𝐨𝐰 𝐭𝐨 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐀𝐝 𝐒𝐭𝐨𝐜𝐤 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐞: → Attribution Analysis: • Compare 1-day vs. 7-day vs. 28-day attribution windows to see your delay • Meta's Andromeda update (launched Dec 2024) improved Advantage+ ROAS by 22% through better AI matching → Shopify Tools: • Check "Time to Purchase" in your analytics dashboard • Use GA4's "Time Lag" report to visualize your purchase delay distribution → Framework: • Product category matters: Personal care (6.8% conversion rate) vs. home decor (1.4%) • Price point impact: Under $50 = 1-day windows, $500+ = 30+ day windows 𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠: ✅ Stop using daily 1DC ROAS as your north star metric ✅ Evaluate campaigns using 7-day minimum windows ✅ Implement MER/CMERs (Marketing Efficiency Ratios) ✅ Use server-side and 1st party tracking via Meta's Conversions API/Elevar ✅ Remember: Mobile users browse (77.2% abandonment) but often buy on desktop later The bottom line: What looks like a "failing campaign" on day 1-3 might be your highest-performing revenue driver when measured properly over 14-30 days. Are you making decisions on incomplete data and killing campaigns right when they're about to deliver results? P.S. The chart shows an example ad stock curve, every brand's timing is unique based on product price, category, and customer behavior. Track YOUR specific patterns, don't rely on industry averages.

After 10+ years of analyzing marketing performance data, I've noticed a (very!) common optimization pitfall. Teams focus solely on Cost Per Acquisition (CPA) while missing the bigger revenue (ROAS) picture. 𝐖𝐡𝐲 𝐂𝐏𝐀 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 👉 Different user segments show varying behaviors post-conversion (retention rates, seats per account, cancellation patterns, upselling potential) 👉 Low CPA campaigns might actually generate less revenue than higher CPA initiatives with better ARPU 👉 Subscription products have multiple revenue-generating actions beyond initial conversion 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐰𝐢𝐭𝐡 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐎𝐀𝐒 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 👉 Multiple revenue events (renewals, plan changes, seat additions) can't be cleanly attributed to original campaigns 👉 Attribution windows often misassign later revenue events to organic or CRM campaigns 👉 Conversion events alone don't capture the full revenue story 𝐓𝐡𝐞 4-𝐒𝐭𝐞𝐩 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐑𝐎𝐀𝐒 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 1️⃣ 𝘚𝘵𝘰𝘳𝘦 𝘈𝘥 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘚𝘱𝘦𝘯𝘥 - Implement ETL tools (Fivetran, Funnel etc) to store spend data - Create unified view across platforms with daily campaign-level granularity 2️⃣ 𝘊𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦 𝘙𝘦𝘷𝘦𝘯𝘶𝘦 𝘗𝘦𝘳 𝘜𝘴𝘦𝘳 - Aggregate all revenue events (subscriptions, renewals, upgrades) - Create comprehensive user lifetime value view - Store in same warehouse as ad spend data 3️⃣ 𝘛𝘳𝘢𝘤𝘬 𝘐𝘯𝘪𝘵𝘪𝘢𝘭 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 - Ensure conversion events link to single touchpoint - Maintain consistent unique identifiers (user_id, campaign_id) - Connect conversion data to revenue tracking 4️⃣ 𝘑𝘰𝘪𝘯 𝘋𝘢𝘵𝘢 𝘚𝘦𝘵𝘴 𝘧𝘰𝘳 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴 - Combine spend, revenue, and conversion data - Create segmented views by market, strategy, audience, keyword - Enable granular ROAS calculation per campaign With this as basis, you can calculate granular ROAS and payback period for your individual campaigns, ads or keywords. Full detailed guide with implementation steps in comments.

Too many advertisers obsess over one metric in isolation: ROAS, CTR, MER, etc. But Facebook ads don’t work in silos. To find and fix performance issues, you need to look at all the metrics together. Here’s how I do it. These are the 9 metrics I track, why they matter, and when they matter: Top 3 for General Business Performance: 💥 Cost Per Purchase This has to fit within your unit economics. If it’s too high, you’re losing money on every sale. A good campaign is profitable, period. 💥 ROAS (Return on Ad Spend) This shows whether your clicks are converting into valuable paying customers. It’s your direct line to revenue performance. 💥 Click-to-Purchase Conversion Rate (CVR) This metric bridges your ads and your website. Both should convert in the 1-2% range. If one is off, you know where to start optimizing. Top 3 for Ad Scalability: 🚀 Reach and Frequency For ads that have been live for a few weeks, these metrics highlight fatigue. A rising frequency means your ad is hitting the same people over and over instead of finding new ones. 🚀 Ad Set Purchase Volume Meta’s algorithm thrives on data. Hitting 7+ purchases per day per ad set (50+ per week) is critical for exiting the learning phase and unlocking better performance. 🚀 Cost Per New Customer Purchase Popsixle sends a separate bonus event for new customer purchases. This is a key metric for effectively running prospecting ads to scale up a business. Top 3 for Monitoring New Ad Creatives: 💣 CTR (Click-Through Rate) This is especially important for new ads in the learning phase. A CTR over 1.5% tells me an ad is doing its job of driving curiosity and clicks. 💣 Ad Quality, Engagement, and Conversion Rankings These rankings tell you how your creative performs across campaigns. Above average on all three dimensions is the standard of excellence. 💣 Incremental Reach Filter your campaigns to show only your new campaign and your previous top performing, top scaled campaign. Compare the reach of each campaign to the unduplicated reach in the summary to see if the new campaign is reaching incremental people. Summary: There’s no one metric that tells the full story. The magic happens when you connect the dots. What metrics do you rely on most to evaluate your Facebook ads?

Most performance marketers are optimizing for fake numbers. Does this sound familiar? Facebook says your ads are crushing it. Your P&L says you're bleeding money. Your agency team shrugs and blames "market conditions." The problem isn't that Facebook's lying. They're just measuring a completely different thing than you think they are. FB attribution only captures what happens inside their walled garden. Someone sees your ad, goes to your website, maybe bookmarks it, then buys three weeks later after getting your email? Facebook sees none of that. The actual customer journey looks nothing like what any single platform can track. So you end up optimizing for impulse buyers only. Everyone who needs more than five minutes to think about your product? Your algorithm never learns they exist. Or killing campaigns that are actually driving long-term sales and scaling campaigns that only work for impulse buyers. So performance marketers get really good at finding people who buy stuff immediately, while completely ignoring everyone who actually thinks before they purchase. Which explains why so many "profitable" campaigns mysteriously stop working when you scale them, or why clients see great ROAS but flat revenue growth. You don't need fancier attribution tools. You need to accept that single-touch attribution only works for impulse purchases. The smart move is to stop living inside Facebook's dashboard. Track what matters: total revenue, customer lifetime value, how much you're spending to acquire customers across everything. Turn off those 1-day view attribution windows in your reports. They're basically fake conversions anyway. Start tracking Marketing Efficiency Ratio - total marketing spend divided by total revenue. Simple math that can't lie to you. Do post-purchase surveys. Ask customers "How did you first hear about us?" You might be surprised how many say "Google search" or "friend recommendation" when Facebook is taking credit for the sale. The reality is Facebook wants to sell you more ads. Your business wants to make more money. Sometimes those goals align. But not always. And when they don't, the platform metrics will completely mislead you. -- Want growth hacks like this that can catapult your business forward?    Sign up to my weekly growth hacks newsletter for easy to implement hacks every Sunday:  <https://lnkd.in/eGMgpwUA>