---
title: Paid Media Optimization
description: Enrich your ad platform data with entity intelligence for smarter targeting and bidding.
---

## 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.

## 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` |

Now your conversion events carry entity-level context that platforms can't see.

## 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

## 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.

## 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 (for example, "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.