Pricing

Pay per event

Lead Enrichment Confidence Scorer

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

Pricing

Pay per event

Rating

0.0

(0)

Developer

Stas Persiianenko

Actor stats

Bookmarked

Total users

Monthly active users

3 days ago

Last modified

Categories

Developer tools

You can access the Lead Enrichment Confidence Scorer programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9    "leads": [
10        {
11            "companyName": "Stripe",
12            "domain": "stripe.com",
13            "industry": "Financial Technology",
14            "companySize": "1001-5000",
15            "contactName": "Jane Smith",
16            "contactTitle": "VP of Engineering",
17            "email": "jane@stripe.com",
18            "emailStatus": "verified",
19            "phone": "+1-555-0123",
20            "linkedinUrl": "https://linkedin.com/in/janesmith",
21            "location": "San Francisco, CA",
22            "country": "US",
23            "leadScore": 88,
24            "dataCompleteness": 85,
25            "sources": [
26                "linkedin",
27                "crunchbase",
28            ],
29            "sourceCount": 2,
30            "tags": [
31                "hot-lead",
32                "enterprise",
33            ],
34        },
35        {
36            "companyName": "Acme Corp",
37            "domain": "acme.com",
38            "contactName": "Bob Jones",
39            "email": "bob@acme.com",
40            "emailStatus": "unverified",
41            "sources": ["manual"],
42            "sourceCount": 1,
43        },
44        {
45            "companyName": "TechStart",
46            "industry": "SaaS",
47            "contactTitle": "CEO",
48            "sources": ["crunchbase"],
49            "sourceCount": 1,
50        },
51    ],
52    "minConfidence": 0,
53    "includeDebugWeights": True,
54}
55
56# Run the Actor and wait for it to finish
57run = client.actor("automation-lab/lead-enrichment-confidence-scorer").call(run_input=run_input)
58
59# Fetch and print Actor results from the run's dataset (if there are any)
60print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
61for item in client.dataset(run["defaultDatasetId"]).iterate_items():
62    print(item)
63
64# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Lead Enrichment Confidence Scorer API in Python

The Apify API client for Python is the official library that allows you to use Lead Enrichment Confidence Scorer API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Lead Enrichment Confidence Scorer API in JavaScript

Lead Enrichment Confidence Scorer API through CLI

Lead Enrichment Confidence Scorer OpenAPI definition

Lead Enrichment Confidence Scorer API

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