---
title: Lead Scoring
description: Prioritize prospects by predicted value, not just firmographics.
---

## Beyond Firmographic Scoring

Traditional lead scoring: "Manufacturing company, 50-200 employees, São Paulo = 75 points"

The problem: Two companies matching that profile can have completely different outcomes. One becomes your best customer, the other churns in 3 months. Firmographics don't predict success.

## How It Works

You define the target outcome (conversion, high-LTV, retained after 12 months), we train a domain-specific model on top of our foundation. The result is a **probability score between 0 and 1** calibrated to your definition of success.

| You Provide | Avra Delivers |
|-------------|---------------|
| Historical outcomes (converted/not, high-LTV/not) | Trained model specific to your target |
| Your definition of "good" | Probability score (0-1) for each entity |
| Labeled seed data | Calibrated predictions across your universe |

This same pattern applies across use cases—credit risk, churn, fraud. Define the target, we train the downstream model.

## Scoring Your Pipeline

| Your CRM Lead | Avra Score Output |
|---------------|-------------------|
| `company: Acme Corp` | `conversion_probability: 0.87` |
| `cnpj: 12.345.678/0001-99` | `ltv_decile: 9` |
| `source: webinar` | `ltv_range: "R$ 80k-120k"` |

## Operationalizing Scores

**Routing**
- Probability > 0.8: Route to senior AE, fast-track onboarding
- Probability 0.5-0.8: Standard sales process
- Probability < 0.5: Nurture sequence, don't invest direct sales time

**Prioritization**
- Sort daily lead list by score
- SDRs work top-down, not first-in-first-out
- Result: Same team, more revenue

**Qualification**
- Replace subjective "gut feel" qualification
- Score provides objective baseline
- Sales adds context, doesn't start from zero

## Powered by two foundations

Lead scoring composes both Avra foundations. The **Graph Foundation Model** brings entity trajectories and network position from the broader economy — which entities are growing, which are showing stress, who counterparties cluster around. Your **Relational Foundation Model** brings the patterns specific to your business — what your converters look like, which signals predict your high-LTV customers. The downstream model is trained on both, and feeds signal back into your RFM with every retrain.

### Customer Data Needed

| Data | Purpose |
|------|---------|
| **Historical outcomes** | Converted/not, high-LTV/not, retained/churned — your definition of "good" |
| **CRM records** | Lead source, engagement history, firmographic attributes |
| **Transaction history** | Revenue patterns, product adoption, usage signals |

### Output Schema

| Field | Description |
|-------|-------------|
| `conversion_probability` | Probability (0-1) that the entity converts based on your definition |
| `ltv_decile` | Predicted lifetime value bucket (1-10) |
| `risk_factors` | Key signals driving the prediction (network growth, sector health, counterparty quality) |

### Evaluation Metrics

- **ROC-AUC** — Primary discrimination: how well the model separates converters from non-converters.
- **Lift at top deciles** — Operational metric: how much better the model's top-ranked leads perform vs. random selection.
- **Calibration** — Predicted probabilities should match observed conversion rates across score bands.

## Combining with Your Data

The best results come from combining Avra scores with your first-party signals:

| Input | Source |
|-------|--------|
| `avra_lead_score` | Avra API |
| `product_engagement` | Your product analytics |
| `recency` | Your CRM |
| `source_quality` | Your marketing data |

Use our embeddings as features in your model, or use our score directly. Either way, you're adding context your CRM can't see.