Prospeo alternative: U.S. mobiles and emails as one free file

Credits come at the start of each billing cycle, all of a year's at once billed yearly, and whatever is left expires at renewal. Prospeo charges nothing for a person it doesn't find, or for the same record enriched again within 90 days, its Enrich Person docs say. If it found every contact in the list below, that would be 217,422 credits: more than a year of Pro billed yearly, 180,000 credits for $2,241.

Prospeo's Job Change filter, from Growth, finds the people who just changed jobs. The file gives each person the month their current job started, CURRENT_JOB_START_DATE: 862,532 of its 1,129,058 directors and above have one. Unzip the file into us_10m/, pip install duckdb, and:

import duckdb

duckdb.sql("create view people as select * from 'us_10m/us_smb_mid_market_persons.parquet'")
duckdb.sql("create view companies as select * from 'us_10m/us_smb_mid_market_companies.parquet'")

# A title's level: 1 an owner, a founder, a CEO or a president; 2 another chief officer; 3 a VP; 4 a director or a head of; 5 a manager; 6 anyone else
duckdb.sql(r"""
  create macro level(title) as case
    when regexp_matches(title, '\bassistant\b.*\b(to|for)\b|\bexecutive assistant\b', 'i') then 6
    when regexp_matches(title, '\b(owner|co-owner|founder|co-founder|cofounder|ceo|chief executive officer)\b', 'i')
      and not regexp_matches(title, '\b(product|process|system|data|service|application|platform) owner\b|\bowner.?s rep', 'i') then 1
    when regexp_matches(regexp_replace(title, 'vice[ -]president', 'vp', 'gi'), '\bpresident\b', 'i') then 1
    when regexp_matches(title, '\b(chief\b.*\bofficer|cfo|coo|cto|cmo|cro|cio|ciso|chro)\b', 'i') then 2
    when regexp_matches(title, '\b(vp|svp|evp|vice[ -]president)\b', 'i') then 3
    when regexp_matches(title, '\b(director|head of)\b', 'i') then 4
    when regexp_matches(title, '\bmanager\b', 'i') then 5
    else 6
  end
""")

# Directors and above by the month they started their current job, since October 2025
duckdb.sql("""
  select strftime(CURRENT_JOB_START_DATE, '%Y-%m') as started, count(*) as people, count(MOBILE_PHONE) as with_a_mobile, count(EMAIL) as with_an_email
  from people
  where level(CURRENT_JOB_TITLE) <= 4 and CURRENT_JOB_START_DATE >= '2025-10-01'
  group by started order by started desc
""").show()

That's 32,248 people at 25,126 companies. A start date is what the person's profile said when their record was read, and 30,690 of them were read between April and September 2026, so the months since April count only the people read after they started: a floor, not a slowdown. Then the ones you can reach:

# The ones with a mobile or an email, newest in the job first, with their company, to a CSV
duckdb.sql("""
  copy (
    select p.FULL_NAME, p.CURRENT_JOB_TITLE, p.CURRENT_JOB_START_DATE, c.NAME as COMPANY, c.EMPLOYEE_COUNT_RANGE, c.LINKEDIN_INDUSTRY,
           p.EMAIL, p.MOBILE_PHONE, p.STATE_CODE, p.LINKEDIN_URL, p.UPDATED_AT
    from people p
    left join companies c on c.LINKEDIN_ID = p.CURRENT_JOB_COMPANY_LINKEDIN_ID
    where level(p.CURRENT_JOB_TITLE) <= 4 and p.CURRENT_JOB_START_DATE >= '2025-10-01'
      and (p.EMAIL is not null or p.MOBILE_PHONE is not null)
    order by p.CURRENT_JOB_START_DATE desc, COMPANY
  ) to 'new_decision_makers.csv'
""")

It writes 26,316 people, each with their company and the date their record was read: 21,234 with a mobile, 18,158 with an email, 13,076 with both. 8,284 are at companies of 11 to 50 employees, 11,027 at 51 to 200 and 7,005 at 201 to 500, and the most common titles are Chief Financial Officer (531), Vice President (466) and Director of Operations (460). Change '2025-10-01' for a later date, or level(...) <= 4 to = 1 for owners, founders and CEOs only. The laws on reaching them are yours to follow: in the U.S., CAN-SPAM for email, the TCPA and the National Do Not Call Registry for calls.

A new job in the file is a few months old by the time you read it. Before you write, the API reads the person's LinkedIn profile behind the URL in the CSV, live: Live. Each request goes to the provider and gets the profile as it is today. $1.25 per 1,000 through Up2Data (a profile it can't find is free), $3.70 per 1,000 through HarvestAPI. Up2Data takes $1 a day per account (800 profiles), with a shared daily limit for all customers, then answers 429 until 00:00 UTC; HarvestAPI has no daily limit.

So the list's 26,316 people cost $38.57 over 30 days, 800 a day through Up2Data and the rest through HarvestAPI, or $97.37 at once through HarvestAPI. The same check every week, on a list you keep: track LinkedIn job changes.

• Emails it verifies and mobiles it finds when you ask: the file's emails carry the date each was last verified.
• Contacts outside the U.S., and companies of every size in one search.
• Search with AI and 30+ filters, intent topics and its Chrome extension.
• Job Change Tracking on your CRM, and HubSpot and Salesforce sync from Growth.

Other tools' credits next to the file: Hunter.io alternative, Snov.io alternative, Kaspr alternative, Cognism alternative; what each tool's free plan gives: free B2B database; lists by job title: email lists.

The free file is on your dashboard once you sign up with your work email, with the $5 credit and your API key.