Lead Enrichment Confidence Scorer

Score enriched leads on data confidence (0-100) with signal-by-signal explanations, missing field detection, and recommended next actions. Works with any enriched lead dataset.

What does Lead Enrichment Confidence Scorer do?

Lead Enrichment Confidence Scorer takes enriched lead data and produces a confidence score (0-100) for each lead based on 10 weighted signals. It tells you not just how confident you should be in each lead's data, but why — which signals are strong, which data is missing, and what specific actions to take to improve each lead.

It works with output from Multi-Source Lead Enrichment or any enriched lead dataset that includes company, contact, and source metadata fields.

Use cases

• Sales teams — Prioritize outreach by data quality, not just lead score
• Marketing ops — Identify leads that need additional enrichment before campaigns
• Data quality audits — Score your entire CRM export to find data gaps
• Pipeline hygiene — Filter out low-confidence leads before they enter your pipeline
• Enrichment ROI — Measure how well your enrichment sources are performing

Why use Lead Enrichment Confidence Scorer?

• Transparent scoring — Every score comes with a full signal breakdown
• Actionable recommendations — Specific next steps to improve each lead's confidence
• Weighted signals — 10 signals with weights based on sales impact (verified email = 20pts, company ID = 15pts, etc.)
• Confidence levels — Critical, low, medium, high, very-high for quick triage
• Debug mode — Full per-signal weight breakdown for tuning and analysis
• Works with any data — Accepts loose JSON objects, not tied to a specific enrichment tool

Scoring signals

Signal	Weight	What it measures
Verified email	20	Email present and verification status
Company identified	15	Company name and domain known
Contact name	12	Full contact name available
Job title	10	Role/title or seniority level known
Multi-source	10	Data confirmed by 2+ independent sources
Industry known	8	Industry classification available
Phone available	7	Direct phone number present
LinkedIn profile	7	LinkedIn URL linked
Location data	6	Geography/location known
Company size	5	Headcount or company type known

Total: 100 points possible. Partial scores are awarded (e.g., unverified email = 8/20, one source = 0/10, two sources = 7/10).

Confidence levels

Level	Score range	Meaning
very-high	85-100	Comprehensive, cross-verified data — ready for outreach
high	65-84	Good quality, minor gaps — actionable
medium	45-64	Moderate confidence — enrich further before outreach
low	25-44	Significant gaps — needs additional enrichment
critical	0-24	Very little verified data — requires substantial work

Input parameters

Parameter	Type	Required	Default	Description
leads	object[]	Yes	—	Array of enriched lead objects with company, contact, and enrichment fields
minConfidence	integer	No	0	Only output leads at or above this confidence threshold
includeDebugWeights	boolean	No	true	Include per-signal weight breakdown in output

Output example

{
    "leadId": "jane@stripe.com",
    "companyName": "Stripe",
    "contactName": "Jane Smith",
    "confidenceScore": 97,
    "confidenceLevel": "very-high",
    "topSignals": [
        "Email is verified",
        "Company name and domain known",
        "Contact name available"
    ],
    "topSignalsSummary": "Email is verified; Company name and domain known; Contact name available",
    "missingSignals": [],
    "missingSignalsSummary": "None",
    "explanation": "Confidence score: 97/100 (very-high). Positive signals (9): Email is verified, Company name and domain known, Contact name available. This lead has comprehensive, cross-verified data and is ready for outreach.",
    "recommendedActions": [],
    "recommendedActionsSummary": "No actions needed",
    "debugWeights": [
        { "name": "verified-email", "weight": 20, "score": 1, "maxWeight": 20, "description": "Email is verified" },
        { "name": "company-identified", "weight": 15, "score": 1, "maxWeight": 15, "description": "Company name and domain known" }
    ],
    "scoredAt": "2026-03-01T20:00:00.000Z"
}

How much does it cost?

This actor uses pay-per-event pricing:

Event	Price	Description
Actor start	$0.035	Charged once per run
Lead scored	$0.001	Charged per lead scored

Example costs:

• 10 leads: $0.035 + (10 x $0.001) = $0.045
• 100 leads: $0.035 + (100 x $0.001) = $0.135
• 1,000 leads: $0.035 + (1,000 x $0.001) = $1.035

Using the Apify API

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('YOUR_ACTOR_ID').call({
    leads: [
        {
            companyName: 'Stripe',
            domain: 'stripe.com',
            contactName: 'Jane Smith',
            email: 'jane@stripe.com',
            emailStatus: 'verified',
            sources: ['linkedin', 'crunchbase'],
            sourceCount: 2,
        },
    ],
    minConfidence: 0,
    includeDebugWeights: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.log(`${item.leadId}: ${item.confidenceScore}/100 (${item.confidenceLevel})`);
    console.log(`  Actions: ${item.recommendedActions.join(', ')}`);
});

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("YOUR_ACTOR_ID").call(run_input={
    "leads": [
        {
            "companyName": "Stripe",
            "domain": "stripe.com",
            "contactName": "Jane Smith",
            "email": "jane@stripe.com",
            "emailStatus": "verified",
            "sources": ["linkedin", "crunchbase"],
            "sourceCount": 2,
        },
    ],
    "minConfidence": 50,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['leadId']}: {item['confidenceScore']}/100 ({item['confidenceLevel']})")

Integrations

• Multi-Source Lead Enrichment — Chain: enrich leads first, then score confidence
• Google Sheets — Export scored leads to spreadsheets for team review
• Webhooks — Trigger alerts when high-confidence leads are found
• Zapier / Make — Route leads to different CRM pipelines based on confidence level

Tips and best practices

• Use minConfidence: 50 to filter out leads that need too much work before outreach
• The debugWeights array shows exactly how each signal contributed to the score
• Chain with Multi-Source Lead Enrichment: run enrichment first, then pipe the output here
• Use the recommendedActions to build enrichment task lists for your data team
• Leads scoring 85+ are ready for direct outreach without further enrichment

Compliance

This actor performs pure data processing on user-provided input. No external APIs are called. No web scraping is performed. All data stays within the Apify platform.