Prospects Statistics

Introduction

The Prospects Statistics API provides aggregated insights and distributions for prospects based on your search criteria. This powerful analytics endpoint helps you understand the composition of your prospect universe, enabling data-driven decisions for sales territory planning, market analysis, and campaign targeting.

📌 Key Benefits:

Get instant insights into prospect distributions without fetching individual records
Understand market composition by department, seniority, location, and more
Receive dynamic statistics that adapt to your filter criteria
Make data-driven decisions for territory planning and resource allocation
No credit charges for statistical analysis

Endpoint: `POST /v1/prospects/stats`

How It Works

Input: Provide filters identical to the prospects fetch endpoint to define your target audience
Processing: The system aggregates prospect data based on your filters and calculates distributions
Output: Dynamic statistics showing distributions for the filters you've applied
Intelligence: The response structure adapts based on which filters are used in the request

Request Schema

Field	Type	Description	Required
`filters`	Object	Filter criteria to define the prospect universe. Supports all filters from the `/v1/prospects` endpoint	No

Note: See the "Supported Filters" section below for the complete list of available filters and their usage.

Supported Filters

This endpoint supports ALL filters available in the /v1/prospects fetch endpoint:

Contact Availability Filters

Filter	Description	Example
`has_email`	Filter by email availability	`{"value": true}`
`has_phone_number`	Filter by phone availability	`{"value": true}`

Professional Filters

Filter	Description	Example
`job_level`	Filter by seniority level	`{"values": ["director", "vp", "cxo"]}`
`job_department`	Filter by department	`{"values": ["sales", "marketing", "engineering"]}`
`job_title`	Filter by job title keywords	`{"value": "Sales Representative"}`
`total_experience_months`	Filter by total experience	`{"gte": 60, "lte": 120}`
`current_role_months`	Filter by time in current role	`{"gte": 12}`

Geographic Filters

Filter	Description	Example
`country_code`	Filter by prospect's country	`{"values": ["us", "gb", "de"]}`
`region_country_code`	Filter by prospect's region	`{"values": ["us-ca", "us-ny"]}`
`city_region_country`	Filter by city, region, country	`{"values": ["New York, NY, US"]}`

Company Filters

Filter	Description	Example
`business_id`	Filter by specific business IDs	`{"values": ["exp_id_1", "exp_id_2"]}`
`company_name`	Filter by company names	`{"values": ["Meta", "Tesla"]}`
`company_size`	Filter by employee count ranges	`{"values": ["51-200", "201-500"]}`
`company_revenue`	Filter by revenue ranges	`{"values": ["10M-50M", "50M-100M"]}`
`company_country_code`	Filter by company HQ country	`{"values": ["us", "ca"]}`
`company_region_country_code`	Filter by company HQ region	`{"values": ["us-ca", "us-tx"]}`

Industry Classification Filters

Filter	Description	Example
`google_category`	Filter by Google business category	`{"values": ["Software Development", "Retail"]}`
`naics_category`	Filter by NAICS industry codes	`{"values": ["5611", "23"]}`
`linkedin_category`	Filter by LinkedIn business category	`{"values": ["software development", "investment banking"]}`

Filter Structure:

Use {"values": [...]} for multi-value filters
Use {"value": ...} for single-value or boolean filters
Use {"gte": ..., "lte": ...} for numeric range filters

Dynamic Response Structure

The response intelligently adapts based on the filters used in your request:

Filters Used	Statistics Returned	Use Case
`job_department` + `region_country_code`	Distribution of departments across regions	Territory planning by function
`job_department` + `country_code`	Distribution of departments across countries	Executive targeting analysis
Single filter only	Simple count distribution	Quick market sizing

Always Included:

total_results: Total count of matching prospects
response_context: Request metadata and performance metrics

Request Examples

{
  "filters": {
    "job_department": {
      "values": ["engineering", "sales", "marketing"]
    },
    "region_country_code": {
      "values": ["us-ca", "us-ny", "us-tx"]
    },
    "company_size": {
      "values": ["51-200", "201-500"]
    }
  }
}

Response Examples

Department by Region Response:

{
  "response_context": {
    "correlation_id": "812719a515474144a8cac019d7776b14",
    "request_status": "success",
    "time_took_in_seconds": 0.75
  },
  "total_results": 4500,
  "stats": {
    "job_departments_per_location": {
      "engineering": {
        "California, US": 850,
        "New York, US": 620,
        "Texas, US": 480,
        "total": 1950
      },
      "sales": {
        "California, US": 560,
        "New York, US": 410,
        "Texas, US": 380,
        "total": 1350
      },
      "marketing": {
        "California, US": 420,
        "New York, US": 350,
        "Texas, US": 430,
        "total": 1200
      },
      "total_per_location": {
        "California, US": 1830,
        "New York, US": 1380,
        "Texas, US": 1290,
        "total": 4500
      }
    }
  }
}

Use Cases

Sales Territory Planning

Analyze prospect distribution across regions using region_country_code and job_department
Balance territories based on prospect density with company_size and job_level filters
Identify underserved markets by comparing prospect counts across geographic areas

Account-Based Marketing (ABM)

Target specific companies using company_name or business_id filters
Analyze decision-maker distribution with job_level and job_department
Segment by company characteristics using company_revenue and company_size

Campaign Targeting

Size your total addressable market using industry filters (naics_category, linkedin_category)
Validate contact availability with has_email and has_phone_number
Focus on new decision-makers using current_role_months filter

Market Intelligence

Compare prospect density across industries using classification filters
Analyze experience levels with total_experience_months
Track job mobility trends with role duration filters

Lead Scoring & Qualification

Identify high-value segments combining job_level, company_revenue, and job_department
Find prospects in growth companies using company size and revenue filters
Target specific buyer personas with precise filter combinations

Best Practices

Start broad, then narrow: Begin with 1-2 filters to understand market size, then add filters progressively
Validate before fetching: Always run stats before expensive fetch operations to ensure sufficient results
Use classification filters: Leverage naics_category or linkedin_category for accurate industry targeting
Combine geographic levels: Use country → region → city filters for precise location targeting
Monitor regularly: Schedule weekly/monthly stats queries to track market changes
Cross-reference filters: Combine professional (job_level) with company (company_size) filters for better segmentation
Experience targeting: Use experience filters to identify senior professionals or recent role changes
Industry-specific analysis: Combine industry classifications with other filters for vertical-specific insights

Important Notes

No credit charges: Statistical queries are free and don't consume any credits
Real-time data: Statistics reflect the current state of the prospect database
Dynamic response: Response structure automatically adapts to your filter combination
All filters supported: Every filter from the /v1/prospects endpoint works here

Try Me Example:

  {
    "filters": {
      "job_department": {
        "values": ["engineering", "sales", "marketing"]
      },
      "region_country_code": {
        "values": ["us-ca", "us-ny", "us-tx"]
      },
      "company_size": {
        "values": ["51-200", "201-500"]
      }
    }
  }