Leadforge Lead Scoring V1: Synthetic B2B CRM Data for Lead Scoring

A synthetic dataset family for teaching lead scoring at scale, created by Shay Palachy Affek using the open-source Leadforge framework. The dataset version is decoupled from the framework version and is published under an explicit v1 tag. Its last recorded update was on 2026-05-31.

Use Cases

• Training lead scoring models based on synthetic B2B CRM and funnel data.
• Benchmarking machine learning algorithms for customer relationship management tasks.
• Teaching data science concepts for marketing and sales analytics using reproducible, synthetic data.

Strengths

• Designed as a relational, three-tier dataset family for modeling complex CRM scenarios.
• Generated by a dedicated, open-source Python framework (Leadforge) for synthetic CRM/funnel data.
• Explicitly versioned (v1) and decoupled from the framework's release cycle for reproducibility.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Generated by the open-source Leadforge framework.

Collection Method

Synthetically generated for reproducibility and teaching purposes.

Freshness

Last updated 2026-05-31 20:09:48; freshness should be verified.