Lead Scoring

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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.

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

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Scoring Your Pipeline

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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

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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.

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Customer Data Needed

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Output Schema

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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.

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Combining with Your Data

The best results come from combining Avra scores with your first-party signals: Use our embeddings as features in your model, or use our score directly. Either way, you’re adding context your CRM can’t see.