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?
Enrich Your Pixel Data
Match your pixel events to Avra’s entity intelligence:| Your Pixel Event | Avra Enrichment |
|---|---|
visitor_id: abc123 | cnpj: 12.345.678/0001-99 |
event: form_submit | lead_score: 847 |
page: /pricing | ltv_forecast: R$ 45,000 |
segment: "high-growth-tech" | |
churn_risk: 0.12 |
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
- 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
- Exclude high churn-risk entities from acquisition campaigns
- Suppress low-score leads from retargeting
- Stop wasting spend on entities unlikely to convert or retain
- Rank your retargeting pool by lead score
- Show premium creative to high-value prospects
- Reduce frequency for low-score visitors
Example: LTV-Optimized Meta Campaign
- Export your customer list with Avra LTV forecasts
- Upload as custom audience with value column
- Create value-based lookalike (Meta optimizes for predicted LTV, not just match)
- Set campaign to optimize for “Value” not “Conversions”
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.Customer Data Needed
| Data | Purpose |
|---|---|
| Customer list with LTV | Actual or estimated lifetime value per customer for value-based optimization |
| Conversion events | Which leads became customers, and when |
| Pixel/CRM match keys | CNPJ or identifiers that allow matching ad platform visitors to Avra entities |
Output Schema
| Field | Description |
|---|---|
lead_score | Predicted conversion probability (0-1) |
ltv_forecast | Estimated lifetime value in BRL |
segment | Behavioral cluster label (e.g., “high-growth-tech”) |
churn_risk | Predicted probability of early churn |
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.