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The Problem with Platform-Native Targeting

Ad platforms know clicks, impressions, and on-site behavior. They don’t know:
  • Is this CNPJ a growing company or about to go bankrupt?
  • What’s their actual lifetime value potential?
  • Are they connected to your best customers’ networks?
You’re bidding blind on entity quality.

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Enrich Your Pixel Data

Match your pixel events to Avra’s entity intelligence: Now your conversion events carry entity-level context that platforms can’t see.

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Use Cases

Smarter Lookalikes
  • Seed with CNPJs of your highest-LTV customers (not just converters)
  • Platform finds users similar to your best customers, not just any customers
  • Result: Higher-quality traffic from day one
Value-Based Bidding
  • Pass LTV forecasts as conversion values to Google/Meta
  • Algorithms optimize for revenue, not just conversions
  • Bid more for entities predicted to be worth 10x
Suppression & Exclusion
  • Exclude high churn-risk entities from acquisition campaigns
  • Suppress low-score leads from retargeting
  • Stop wasting spend on entities unlikely to convert or retain
Retargeting Prioritization
  • Rank your retargeting pool by lead score
  • Show premium creative to high-value prospects
  • Reduce frequency for low-score visitors

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Example: LTV-Optimized Meta Campaign

  1. Export your customer list with Avra LTV forecasts
  2. Upload as custom audience with value column
  3. Create value-based lookalike (Meta optimizes for predicted LTV, not just match)
  4. Set campaign to optimize for “Value” not “Conversions”
The result: Meta’s algorithm learns what high-LTV entities look like and finds more of them.

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Powered by two foundations

Paid media optimization enriches ad-platform data with signals invisible to pixel tracking. The Graph Foundation Model brings entity growth trajectories, network health, and sector dynamics. Your Relational Foundation Model brings the definition of a high-value customer specific to your business. The downstream model trained on both calibrates predictions to your LTV definition — and every training run feeds signal back into your RFM.

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Customer Data Needed

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Output Schema

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Evaluation Metrics

  • Incremental ROAS — Primary metric: return on ad spend improvement vs. platform-native targeting alone.
  • Cost per qualified lead — Measures targeting efficiency when using Avra scores for bid adjustments.
  • Lift in LTV — Compares average LTV of customers acquired with vs. without Avra enrichment.