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

How It Works

Input: Provide filters identical to the prospects fetch endpoint to define your target audience 2. Processing: The system aggregates prospect data based on your filters and calculates distributions 3. Output: Dynamic statistics showing distributions for the filters you’ve applied 4. 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"]
      }
    }
  }

Authorizations

api_key

string

header

required

Body

application/json

filters

ProspectsFetchFilters · object

required

Show child attributes

filters.has_email

Has Email · object

Filter prospects by whether they have an email. Categories: [True, False]

Show child attributes

filters.has_email.value

boolean

required

filters.has_email.negate

boolean

Example:

{ "value": true }

filters.has_phone_number

Has Phone Number · object

Filter prospects by whether they have a phone number. Categories: [True, False]

Show child attributes

filters.has_phone_number.value

boolean

required

filters.has_phone_number.negate

boolean

Example:

{ "value": true }

filters.job_level

Job Level · object

Filter prospects by their job level. Categories: [director, manager, vp, partner, cxo, non-managerial, senior, entry, training, unpaid]

Show child attributes

filters.job_level.values

enum<string>[]

required

The JobSeniorityLevel class is an enumeration that represents various levels of job seniority.

This enum is used to categorize jobs based on their seniority levels, such as:

Entry-level positions
Managerial roles
Executive roles (e.g., C-Suite, VP)

These categories ensure consistent filtering and classification of job-related data across the application.

Available options:

owner,

c-suite,

vice president,

director,

senior non-managerial,

manager,

partner,

non-managerial,

junior,

president,

senior manager,

advisor,

freelancer,

board member,

founder

filters.job_level.negate

boolean

Example:

{
  "values": [
    "owner",
    "c-suite",
    "vice president",
    "director",
    "senior non-managerial",
    "manager",
    "partner",
    "non-managerial",
    "junior",
    "president",
    "senior manager",
    "advisor",
    "freelancer",
    "board member",
    "founder"
  ]
}

filters.job_department

Job Department · object

Filter prospects by their job department. Categories: [customer service, design, education, engineering, finance, general, health, sales, ...]

Show child attributes

filters.job_department.values

enum<string>[]

required

The JobDepartment class is an enumeration that represents various job departments.

This enum is used to categorize jobs based on their associated departments, such as:

Engineering
Marketing
Sales
Legal
Customer Service

These categories ensure consistent filtering and classification of job-related data across the application.

Available options:

administration,

real estate,

healthcare,

partnerships,

c-suite,

design,

human resources,

engineering,

education,

strategy,

product,

sales,

r&d,

retail,

customer success,

security,

public service,

creative,

it,

support,

marketing,

trade,

legal,

operations,

procurement,

data,

manufacturing,

logistics,

finance

filters.job_department.negate

boolean

Example:

{
  "values": [
    "administration",
    "real estate",
    "healthcare",
    "partnerships",
    "c-suite",
    "design",
    "human resources",
    "engineering",
    "education",
    "strategy",
    "product",
    "sales",
    "r&d",
    "retail",
    "customer success",
    "security",
    "public service",
    "creative",
    "it",
    "support",
    "marketing",
    "trade",
    "legal",
    "operations",
    "procurement",
    "data",
    "manufacturing",
    "logistics",
    "finance"
  ]
}

filters.business_id

Business ID · object

Filter prospects by account. Use Explorium entity IDs. Example: [EXP_ENTITY_ID_1, EXP_ENTITY_ID_2][EXP_ENTITY_ID_1, EXP_ENTITY_ID_2]

Show child attributes

filters.business_id.values

string[]

required

filters.business_id.negate

boolean

Example:

{
  "values": [
    "8adce3ca1cef0c986b22310e369a0793",
    "340c8040bd50cbab9c7df718bbe51cc9"
  ]
}

filters.total_experience_months

Total Experience Months · object

Filter by total months of experience.

Show child attributes

filters.total_experience_months.negate

boolean

filters.total_experience_months.gte

integer

filters.total_experience_months.lte

integer

Example:

{ "gte": 1, "lte": 10 }

filters.country_code

Country Code · object

Filter prospects by country using alpha-2 codes. Example: [us, ca]

Show child attributes

filters.country_code.values

string[]

required

filters.country_code.negate

boolean

Example:

{ "values": ["US", "CA"] }

filters.region_country_code

Region Country Code · object

Filter prospects by region using ISO 3166-2 codes. Example: [us-ut, us-ca]

Show child attributes

filters.region_country_code.values

string[]

required

filters.region_country_code.negate

boolean

Example:

{ "values": ["US-CA", "IL-TA"] }

filters.current_role_months

Current Role Months · object

Filter by number of months in current role.

Show child attributes

filters.current_role_months.negate

boolean

filters.current_role_months.gte

integer

filters.current_role_months.lte

integer

Example:

{ "gte": 1, "lte": 10 }

filters.company_size

Company Size · object

Filter by company’s number of employees. Options: [1-10, 11-50, ..., 10001+]

Show child attributes

filters.company_size.values

enum<string>[]

required

The NumberOfEmployeesRange class is an enumeration that represents predefined ranges for the number of employees in a company. These ranges are used for filtering and categorizing companies based on their workforce size.

Available options:

1-10,

11-50,

51-200,

201-500,

501-1000,

1001-5000,

5001-10000,

10001+

filters.company_size.negate

boolean

Example:

{
  "values": [
    "1-10",
    "11-50",
    "51-200",
    "201-500",
    "501-1000",
    "1001-5000",
    "5001-10000",
    "10001+"
  ]
}

filters.company_revenue

Company Revenue · object

Filter by company’s annual revenue. Options: [0-500K, 500K-1M, ..., 10B-100B]

Show child attributes

filters.company_revenue.values

enum<string>[]

required

The RevenueRange class is an enumeration that represents predefined ranges for the revenue of a company.

Available options:

0-500K,

500K-1M,

1M-5M,

5M-10M,

10M-25M,

25M-75M,

75M-200M,

200M-500M,

500M-1B,

1B-10B,

10B-100B,

100B-1T,

1T-10T,

10T+

filters.company_revenue.negate

boolean

Example:

{
  "values": [
    "0-500K",
    "500K-1M",
    "1M-5M",
    "5M-10M",
    "10M-25M",
    "25M-75M",
    "75M-200M",
    "200M-500M",
    "500M-1B",
    "1B-10B",
    "10B-100B",
    "100B-1T",
    "1T-10T",
    "10T+"
  ]
}

filters.google_category

Google Category · object

Filter by company’s Google business category. Example: [Paving contractor, Retail]

Show child attributes

filters.google_category.values

string[]

required

filters.google_category.negate

boolean

Example:

{ "values": ["construction"] }

filters.naics_category

NAICS Category · object

Filter by NAICS code (2, 4, or full). Example: [23, 5611]

Show child attributes

filters.naics_category.values

string[]

required

filters.naics_category.negate

boolean

Example:

{ "values": ["541512"] }

filters.linkedin_category

LinkedIn Category · object

Filter by company’s LinkedIn business category. Example: [software development, investment banking]

Show child attributes

filters.linkedin_category.values

string[]

required

filters.linkedin_category.negate

boolean

Example:

{ "values": ["retail"] }

filters.job_title

Job Title · object

Filter prospects by their job titles. Supports include_related_job_titles parameter. Example: [Sales Representative, SEO specialist, Technical Support Engineer]

Show child attributes

filters.job_title.values

string[]

required

filters.job_title.negate

boolean

When set to true, includes related job titles in the search.

Example:

false

Example:

{
  "values": ["Software Engineer", "Data Scientist"],
  "include_related_job_titles": false
}

filters.company_country_code

Company Country Code · object

Filter by company HQ country using alpha-2 codes. Example: [us, ca]

Show child attributes

filters.company_country_code.values

string[]

required

filters.company_country_code.negate

boolean

Example:

{ "values": ["US", "CA"] }

filters.company_region_country_code

Company Region Country Code · object

Filter by company HQ region using ISO 3166-2 codes. Example: [us-ut, us-ca]

Show child attributes

filters.company_region_country_code.values

string[]

required

filters.company_region_country_code.negate

boolean

Example:

{ "values": ["US-CA", "IL-TA"] }

filters.city_region_country

City Region Country · object

Filter by city region country.

Show child attributes

filters.city_region_country.values

string[]

required

filters.city_region_country.negate

boolean

Example:

{
  "values": [
    "Paris, FR",
    "Tel Aviv, IL",
    "Miami, FL, US"
  ]
}

filters.company_name

Company Name · object

Filter by company name. Example: [Meta, Tesla]

Show child attributes

filters.company_name.values

string[]

required

Maximum string length: 256

filters.company_name.negate

boolean

Example:

{ "values": ["Microsoft", "Google"] }

Example:

{}

request_context

Request Context · object

Example:

null

Response

Successful Response

This is base response model for all responses in partner service.

response_context

ResponseContext · object

required

Show child attributes

response_context.correlation_id

string

required

response_context.request_status

enum<string>

required

The RequestStatus class is an enumeration that defines the possible statuses of a request.

This enum is used to indicate whether a request was successful, missed, or failed. It ensures consistent handling of request statuses across the application.

Attributes: SUCCESS: Indicates that the request was successfully processed. MISS: Indicates that the request did not find any matching data. FAILURE: Indicates that the request encountered an error or failure.

Available options:

success,

miss,

failure

response_context.time_took_in_seconds

number

required

total_results

integer

required

stats

ProspectsStats · object

required

Show child attributes

stats.job_departments_per_location

Job Departments Per Location · object

stats.total_per_location

Total Per Location · object

Explorium AgentSource

Partners

Prospects Statistics

Prospects Statistics

Introduction

Contact Availability Filters

Professional Filters

Geographic Filters

Company Filters

Industry Classification Filters

Sales Territory Planning

Account-Based Marketing (ABM)

Campaign Targeting

Market Intelligence

Lead Scoring & Qualification

Authorizations

Body

Response

Explorium AgentSource

Partners

​Prospects Statistics

​Introduction

​Contact Availability Filters

​Professional Filters

​Geographic Filters

​Company Filters

​Industry Classification Filters

​Sales Territory Planning

​Account-Based Marketing (ABM)

​Campaign Targeting

​Market Intelligence

​Lead Scoring & Qualification

Authorizations

Body

Response

Prospects Statistics

Introduction

Contact Availability Filters

Professional Filters

Geographic Filters

Company Filters

Industry Classification Filters

Sales Territory Planning

Account-Based Marketing (ABM)

Campaign Targeting

Market Intelligence

Lead Scoring & Qualification