Paid Media Optimization

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

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”

The result: Meta’s algorithm learns what high-LTV entities look like and finds more of them.

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.