Glossary

Lead Enrichment Schema

A predefined JSON structure that standardizes the output format of lead enrichment data regardless of the upstream data provider, preventing downstream workflow breakage when enrichment sources change or return inconsistent fields.

Definition

A predefined JSON structure that standardizes the output format of lead enrichment data regardless of the upstream data provider, preventing downstream workflow breakage when enrichment sources change or return inconsistent fields.

In Depth

Lead enrichment pipelines typically chain 3-5 data providers, each returning different JSON structures. Without a standardized schema, downstream systems (CRM, scoring models, outreach sequences) break whenever a provider changes their response format or a new provider is swapped in. The enrichment schema acts as a contract between data collection and data consumption. A practical enrichment schema includes these field groups: identity (company_name, domain, linkedin_url), firmographics (industry, employee_count_range, annual_revenue_range, founding_year, headquarters_location), contact (decision_maker_name, title, email, email_confidence_score), digital_presence (google_organic_keywords_estimate, monthly_traffic_estimate, social_profiles), and enrichment_metadata (enriched_at timestamp, source_providers array, confidence_score). Implementation pattern using a normalizer function: ```typescript interface EnrichedLead { company_name: string; domain: string; industry: string | null; employee_range: string | null; search_presence: { google_ranking_keywords: number | null; reddit_mentions_30d: number | null; tiktok_brand_videos: number | null; }; enriched_at: string; sources: string[]; } ``` Each provider's raw response gets mapped through a provider-specific normalizer into this common schema. When Scavio search results are part of the enrichment (querying Google for company domain, Reddit for brand mentions at $0.005/query each), the normalizer extracts ranking signals and community sentiment into the standardized fields. Schema versioning is critical: use semantic versioning (v1.0, v1.1, v2.0) and maintain backward compatibility within major versions. Breaking changes (removing fields, changing types) require a major version bump and migration period. Teams that skip schema standardization typically discover the cost when a provider API update breaks their CRM integration at 2 AM on a weekday.

Example Usage

Real-World Example

The enrichment pipeline normalizes Clearbit firmographics, Scavio search presence data, and Apollo contact details into a single EnrichedLead schema, so the scoring model always receives consistent fields regardless of which upstream providers succeeded or failed.

Platforms

Lead Enrichment Schema is relevant across the following platforms, all accessible through Scavio's unified API:

  • Google
  • Reddit
  • TikTok

Related Terms

Frequently Asked Questions

A predefined JSON structure that standardizes the output format of lead enrichment data regardless of the upstream data provider, preventing downstream workflow breakage when enrichment sources change or return inconsistent fields.

The enrichment pipeline normalizes Clearbit firmographics, Scavio search presence data, and Apollo contact details into a single EnrichedLead schema, so the scoring model always receives consistent fields regardless of which upstream providers succeeded or failed.

Lead Enrichment Schema is relevant to Google, Reddit, TikTok. Scavio provides a unified API to access data from all of these platforms.

Lead enrichment pipelines typically chain 3-5 data providers, each returning different JSON structures. Without a standardized schema, downstream systems (CRM, scoring models, outreach sequences) break whenever a provider changes their response format or a new provider is swapped in. The enrichment schema acts as a contract between data collection and data consumption. A practical enrichment schema includes these field groups: identity (company_name, domain, linkedin_url), firmographics (industry, employee_count_range, annual_revenue_range, founding_year, headquarters_location), contact (decision_maker_name, title, email, email_confidence_score), digital_presence (google_organic_keywords_estimate, monthly_traffic_estimate, social_profiles), and enrichment_metadata (enriched_at timestamp, source_providers array, confidence_score). Implementation pattern using a normalizer function: ```typescript interface EnrichedLead { company_name: string; domain: string; industry: string | null; employee_range: string | null; search_presence: { google_ranking_keywords: number | null; reddit_mentions_30d: number | null; tiktok_brand_videos: number | null; }; enriched_at: string; sources: string[]; } ``` Each provider's raw response gets mapped through a provider-specific normalizer into this common schema. When Scavio search results are part of the enrichment (querying Google for company domain, Reddit for brand mentions at $0.005/query each), the normalizer extracts ranking signals and community sentiment into the standardized fields. Schema versioning is critical: use semantic versioning (v1.0, v1.1, v2.0) and maintain backward compatibility within major versions. Breaking changes (removing fields, changing types) require a major version bump and migration period. Teams that skip schema standardization typically discover the cost when a provider API update breaks their CRM integration at 2 AM on a weekday.

Lead Enrichment Schema

Start using Scavio to work with lead enrichment schema across Google, Amazon, YouTube, Walmart, and Reddit.