The days of manual targeting, csv uploads, and pixel-based optimization are gone.
Top D2C brands have upgraded to a new signal-first, first-party data playbook and their ROAS shows it.
From Broad Lookalikes → To Precise New Customer Acquisition
| Old Playbook | New Playbook |
|---|---|
| Marketers used to build lookalike audiences from pixel data, hoping Meta would automatically find the right buyers. But overlapping with already engaged users and existing customers — plus poor signal quality — led to higher CPAs and diluted performance. | Using synthetic / signal engineering “new_customer_purchase” events to train Meta on first-time buyer intent. Meta learns to optimize for actual new customers, not just similar profiles. Brand saw 45% lift in the new customers by training the Meta algorithm using the synthetic event “new customer purchase” at very controlled CPP. |
From Generic “Purchase” → To “High-Value Customer” purchase event Optimization
| Old Playbook | New Playbook |
|---|---|
| Marketers now and used to optimized for the default “Purchase” event and treating all sales equally. Meta learned from every low-ticket order and did not find more high values due to non-segmentation. That led to more sales, but lower margin. | With signal engineering, using a custom “high_AOV_purchase” event to help Meta prioritize profitable customers. Meta learns better from linear high-value conversions and brings in people who are likely to buy high value products. Here is a brand story of optimizing the campaign with high AOV purchases and saw 37% ROAS lift. |
From ad account optimized for one hero product → To Category-Level Scaling
| Old Playbook | New Playbook |
|---|---|
| The Ad account often gets skewed for one product which gets high sales. Campaigns collapsed when one product fatigued. We even add multiple adsets / ads for other products .. the campaign doesn’t train to sell the non-hero products. Unstable performance across seasons | Using signal engineering, built category-based custom events (shoes_purchase, bags_purchase, etc.) for diversified scaling. Category-level optimized campaigns allow scaling multiple verticals in parallel and each campaign becomes an AI algorithm itself. Brand scaled all the category of the business and showed a 200% ROAS lift with consistent multi-category growth. |
From Pixel Tracking → To Server-Side First-Party Signals
| Old Playbook | New Playbook |
|---|---|
| Marketers used to send the broken signal data of their users without the extra parameters .. Hence which the attribution on Meta was always not right. This happened because marketers relied on Meta pixel tracking however, Meta deletes them in 28days and attribution is lost completely. Depended on browser pixels — lost accuracy to ad blockers and cookies.Leaky, short-lived data. | Now, marketers use server-side first-party (1P) tracking for accurate, privacy-compliant event data. First-party signals never expire — enabling long-term optimization and attribution beyond 28, 45, or even 90 days. Every purchase is tied back to the right campaign, ensuring crystal-clear attribution. Already, 1500+ brands have switched to 1P Signals to power their entire marketing ops — and their attribution is now rock solid. |
From health and wellness blocked from running ads —> Freely running meta ads privacy complaint
| Old Playbook | New Playbook |
|---|---|
| In the old way, brands and marketers used to run ads by sending all visitor data — including personal and sensitive health information — directly to Meta. But this approach doesn’t work anymore. Meta blocked such accounts to comply with privacy laws, as sharing sensitive data violates HIPAA and GDPR The performance didn’t drop but stopped totally to bankruptcy | Now, marketers using first-party data can send all web and offline conversions — including CRM events — to Meta safely. Personal and health data are hashed, keeping everything fully HIPAA and GDPR compliant. Your performance is back on track — accurate, compliant, and unstoppable |
Want to see how these brands scaled profitably?
