Advanced techniques to analyze Google Ads & Facebook Ads results

Your Google ads dashboard says R O A S 4.2. Your Facebook ads manager says R O A S 3.8. Your revenue report matches neither. So who's telling the truth? None of them. Today I'll show you why and what to do about it. Welcome back to the lead goals podcast. We don't do surface level talk here. This is for operator spending $10,000 plus a month on ads. We're breaking down how to actually read Google and Facebook results. First, why reporting lies? Every platform grades its own homework. A user clicks Facebook on Monday, Google on Thursday, buys Friday. Both platforms claim the sale. You got one sale. Reporting shows two. That's attribution overlap and it leads to scaling the wrong channels. Three key problems. One met a view through conversions. Someone just sees your ad and buys later. Facebook takes credit. Fix switch to seven day click only. No view through to Google conversion confusion leads, calls, purchases, all grouped together. You must separate primary versus secondary conversions, only track real revenue actions. Three last click attribution. Google gives all credit to the final click. This over values branded search and under values top of funnel. Fix your tracking before anything else. Now attribution data driven attribution is better than last click. It spreads credit across touch points using data, but it still stays inside one platform. Advanced models go further. Shapley value attribution assigns credit based on contribution across all channel combinations. Markov chain attribution removes a channel and measures how conversions drop. You don't need to build these, but you need to understand what they mean. Practical rule under $30,000 per month, fixed tracking plus use data driven over $50,000 per month, consider attribution tools. Next segmentation averages high truth. A campaign with R O A S 3.2 might be mobile at 1.4 desktop at 5.8. Break everything down device mobile versus desktop time hours and days, audience cold versus warm new versus returning customers. New customers matter most returning customers inflate R O A S, but don't grow the business. Then cohort analysis group customers by when they were acquired, track their value over time. If Google customers are worth more after 90 days than Facebook customers, that's your real signal, not in platform R O A S. Now competition. You're in an auction in Google watch, impression share, overlap rate, position above rate. If impression share drops, either budget is limiting or competition increased. If overlap rate spikes, a competitor is scaling. If they rank above you, study their ads, not just raise bids. On meta, watch CPM. If CPM rises, competition is increasing. Use the ad library ads running 30 plus days are likely profitable. That's free insight. Now incrementality. Key question, would this sale happen without the ad? Example branded search, people already know you. They might buy anyway incrementality testing shows real impact method. One conversion lift split audience into test and control, measure the difference. That difference is true performance method to geo tests run ads in some regions, pause in others, compare revenue, that gives true incremental R O A S. This is how advanced buyers think. Now data, most people jump between platforms and spreadsheets. That's unreliable. You need one system, bring all data into one place, standardized naming, de duplicate conversions, build one dashboard and keep UTM's consistent. Bad naming equals bad data. Action plan week one, fixed meta attribution, clean Google conversions, standardized UTMs month one, set up data system, build dashboard, start segmentation. Months two to three switch attribution model, run lift tests, monitor competition. Long term run geo tests, build cohort reports, optimize based on real impact. Final thought, winners aren't the best creatives. They're the ones who know what's actually working. If this helped, leave a review. I'll see you next episode.