How to Find Which Enrichment Tools Your Reps Use (a 50-Rep Survey and a 50-Contact Test)

We did not publish a 50-rep survey. Distrust any post that quotes one without a spreadsheet. This guide gives you the method instead. You run it on your own reps and get numbers you can defend.

TL;DR

• Survey 50 reps with five questions, tally the CSV in a short script, then enrich the same 50 contacts.
• It takes about 2 hours of your time plus a few days for replies.
• The API test spends at most 50 LeadOcean records (the free plan has 1,000). Sizing and counting spend none.

Prerequisites

• A LeadOcean account on the free plan, with the key exported as $LEADOCEAN_API_KEY.
• A form tool that exports CSV (any will do) and Python 3.
• A list of 50 reps. Your own team, or a community you belong to and may ask.
• 50 LinkedIn URLs of contacts your reps worked last quarter, for step 4.

Step 1: Write five questions that force a real answer

Ask what reps did last week, not what they like. Opinions about tools are noise. Usage is signal.

Q1. Which tools did you open in the last 7 days to find an email or phone? (tick all)
Q2. Which one did you open first?
Q3. How many contacts did you look up last week? (0 / 1-10 / 11-50 / 50+)
Q4. When a lookup failed, what did you do next? (try another tool / guess the email / skip)
Q5. Which tool do you pay for yourself, outside the company plan?

Q2 matters most. Q1 shows the sprawl, Q2 shows the default. Q5 exposes shadow spend.

Step 2: Size the pool so you know what 50 means

50 answers from one team is a sample of one team. Size the wider population before you claim anything about it. This call is free.

curl -s -X POST "https://api.leadocean.io/v1/people/search?count=true" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "title": ["Account Executive", "Sales Development Representative"],
    "jobFunction": ["Sales & Business Development"],
    "country": ["US"],
    "limit": 1
  }'

The count=true flag goes in the query string, not the body. The body is flat JSON and filters are arrays. meta.total is capped at 100,000, so a larger pool reads as the cap. The MCP tool leadocean_count_leads takes the same filters.

Step 3: Collect replies and tally them

Export the form to replies.csv. Q1 holds a semicolon-separated list. This script counts tools mentioned and tools named first.

import csv
from collections import Counter

mentioned, first = Counter(), Counter()
with open("replies.csv", newline="") as f:
    rows = list(csv.DictReader(f))

for r in rows:
    for tool in r["q1_tools"].split(";"):
        if tool.strip():
            mentioned[tool.strip().lower()] += 1
    first[r["q2_first"].strip().lower()] += 1

n = len(rows)
print(f"replies: {n}")
for tool, c in mentioned.most_common():
    print(f"{tool:<20} mentioned {c:>3} ({c/n:.0%})  first {first[tool]:>3}")

Look for the gap between "mentioned" and "first". A tool many reps mention but few name first is a fallback. The tool named first most often is the real default.

Step 4: Test the same 50 contacts

Take the 50 LinkedIn URLs, one per line in contacts.txt. Enrich each one. Enrich takes exactly one of linkedin_url, email, phone or person_id. A miss is a 404 and costs nothing.

while read -r url; do
  code=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
    "https://api.leadocean.io/v1/people/enrich" \
    -H "x-api-key: $LEADOCEAN_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"linkedin_url\": \"$url\", \"reveal_email\": true}")
  echo "$code $url"
  sleep 0.25
done < contacts.txt | tee enrich-log.txt

cut -d' ' -f1 enrich-log.txt | sort | uniq -c

The sleep 0.25 keeps you under the Free plan's 5 requests a second. Run the same 50 URLs through the tool your reps named first, then compare hit rates. Same inputs, same week. Anything else is not a comparison.

Third-party tools in your survey have their own export and API docs. Read those for their output shape before you build the comparison columns.

Step 5: Put the two results side by side

Join the survey tally with the hit rates in one table. Keep it plain.

tool        named first   hit rate on 50   cost for the 50
tool_a      (your number) (your number)    (your number)
tool_b      (your number) (your number)    (your number)
leadocean   n/a           (your number)    at most 50 records

If the tool named first has the lowest hit rate, you found the waste. If it has the highest, leave it alone and move on.

What you get

Step 2 returns a count. This is the result of the free leadocean_count_leads MCP tool on 2026-10-21, with the filters from step 2.

{
  "total": 289171,
  "totalIsExact": true,
  "totalAll": 766511,
  "distinctCompanies": 48903,
  "credits": 0
}

total counts US people with those titles and a verified, catch_all_valid or catch_all email, because the tool defaults to mailable addresses. totalAll is the same filters with no email condition. It spent 0 records.

Step 4 returns one JSON object per found person. This is the response shape from the API reference, with placeholder values:

{
  "success": true,
  "data": {
    "profile_data": { "profile_full_name": "Jane Doe" },
    "contact_data": {
      "has_email": true,
      "email_status": "verified",
      "contact_emails": [
        { "email": "jane.doe@acme.com", "type": "work", "status": "verified" }
      ],
      "contact_phones": [{ "phone": "+1 415 555 0133", "type": "mobile" }],
      "contact_current_experiences": [
        { "job_title": "Account Executive", "company_name": "Acme" }
      ]
    },
    "resume_data": { "experiences": [], "educations": [] }
  }
}

Past employers in resume_data carry no company_id yet. Your tally from step 3 prints one line per tool in the format shown in the script.

Troubleshooting

FAQ