Customer Discovery

Customer Discovery

Find all customers of a company by scanning multiple public data sources. Produces a deduplicated report with confidence scoring.

Quick Start

Find all customers of Datadog

Who are Notion's customers? Use deep mode.

Inputs

Procedure

Step 1: Gather Inputs

Ask the user for:

1. Company name (required)
2. Company website URL (optional — if not provided, WebSearch for it)
3. Depth tier — present these options, default to Standard:
   • Quick (~2-3 min): Website logos, case studies, G2 reviews, press search
   • Standard (~5-8 min): Quick + blog posts, Wayback Machine, LinkedIn, Twitter, Reddit, HN, job postings, YouTube
   • Deep (~10-15 min): Standard + SEC filings, podcasts, GitHub, integration directories, BuiltWith, Crunchbase

Step 2: Create Output Directory

mkdir -p customer-discovery-[company-slug]

Step 3: Run Sources for Selected Tier

Collect all results into a running list. For each customer found, record:

• name: Company name
• confidence: high / medium / low
• source_type: e.g., "logo_wall", "case_study", "g2_review", "press", "job_posting"
• evidence_url: URL where the evidence was found
• notes: Brief description of the evidence

Quick Sources

1. Website logo wall

Run the scrape_website_logos.py script:

python3 skills/capabilities/customer-discovery/scripts/scrape_website_logos.py \
  --url "[company-url]" --output json

Parse the JSON output and add each result to the customer list.

2. Case studies page

Use WebFetch on the company's case studies page (try /case-studies, /customers, /resources/case-studies). Extract customer names from page headings and content.

3. G2/Capterra reviews

If the review-site-scraper skill is available, use it to find reviewer companies:

python3 skills/capabilities/review-site-scraper/scripts/scrape_reviews.py \
  --platform g2 --url "[g2-product-url]" --max-reviews 50 --output json

First, WebSearch for the company's G2 page: site:g2.com "[company]". Extract reviewer company names from review author info.

4. Web search for press

WebSearch these queries and extract customer mentions from results:

• "[company]" customer OR "case study" OR partnership
• "[company]" "we use" OR "switched to" OR "chose"

Standard Sources (in addition to Quick)

5. Company blog posts

WebSearch: site:[company-domain] customer OR "case study" OR partnership OR "customer story"

6. Wayback Machine logos

Run the scrape_wayback_logos.py script:

python3 skills/capabilities/customer-discovery/scripts/scrape_wayback_logos.py \
  --url "[company-url]" --output json

Logos marked still_present: false are especially interesting — they indicate former customers.

7. Founder/exec LinkedIn posts

WebSearch: site:linkedin.com "[company]" customer OR "excited to announce" OR "welcome"

8. Twitter/X mentions

WebSearch: site:twitter.com "[company]" "we use" OR "just switched to" OR "loving"

9. Reddit/HN mentions

WebSearch these queries:

• site:reddit.com "we use [company]" OR "[company] customer"
• site:news.ycombinator.com "[company]" customer OR user

10. Job postings

WebSearch: "experience with [company]" site:linkedin.com/jobs OR site:greenhouse.io OR site:lever.co

Companies requiring experience with the product are likely customers.

11. YouTube testimonials

WebSearch: site:youtube.com "[company]" customer OR testimonial OR review

Deep Sources (in addition to Standard)

12. SEC filings

WebSearch: site:sec.gov "[company]" — Look for mentions in 10-K and 10-Q filings.

13. Podcast transcripts

WebSearch: "[company]" podcast customer OR transcript OR interview

14. GitHub usage signals

WebSearch: site:github.com "[company-package-name]" in dependency files, package.json, requirements.txt, etc.

15. Integration directories

WebFetch marketplace pages where the company lists integrations:

• Salesforce AppExchange
• Zapier integrations page
• Slack App Directory
• Any marketplace relevant to the company

16. BuiltWith detection

python3 skills/capabilities/customer-discovery/scripts/search_builtwith.py \
  --technology "[company-slug]" --max-results 50 --output json

17. Crunchbase

WebSearch: site:crunchbase.com "[company]" customers OR partners

Step 4: Deduplicate Results

Merge results by company name using fuzzy matching:

• Normalize: lowercase, strip suffixes (Inc, Corp, LLC, Ltd, Co., GmbH)
• Treat "Acme Inc" = "Acme" = "ACME Corp" = "acme.com" as the same company
• When merging, keep the highest confidence level and all evidence URLs

Step 5: Assign Confidence

Apply these rules:

High confidence:

• Logo on current website (from scrape_website_logos.py with confidence "high")
• Published case study or customer story
• Direct quote or testimonial on the company's site
• Official partnership page listing

Medium confidence:

• G2/Capterra review (reviewer's company)
• Press article mentioning customer relationship
• Job posting requiring experience with the product
• YouTube testimonial or video review
• Logo found only in Wayback Machine (was on site, now removed)

Low confidence:

• Single social media mention (tweet, Reddit post)
• Indirect reference ("heard good things about X")
• BuiltWith detection only (technology on site doesn't mean they're a paying customer)
• HN discussion mention

Step 6: Generate Report

Create two output files:

customer-discovery-[company]/report.md:

# Customer Discovery: [Company Name]

**Date:** YYYY-MM-DD
**Depth:** quick | standard | deep
**Total customers found:** N

## High Confidence (N)

| Customer | Source | Evidence |
|----------|--------|----------|
| Shopify | Case study | [link] |
| ... | ... | ... |

## Medium Confidence (N)

| Customer | Source | Evidence |
|----------|--------|----------|
| ... | ... | ... |

## Low Confidence (N)

| Customer | Source | Evidence |
|----------|--------|----------|
| ... | ... | ... |

## Sources Scanned

- Website logo wall: [url] — N customers found
- G2 reviews: N reviews analyzed — N companies identified
- Wayback Machine: N snapshots checked — N logos found (N removed)
- Web search: N queries — N mentions
- ...

## Methodology

This report was generated using the customer-discovery skill, which scans
public data sources to identify companies that use [Company Name]. Confidence
levels reflect the strength and directness of the evidence found.

customer-discovery-[company]/customers.csv:

CSV with columns: company_name,confidence,source_type,evidence_url,notes

Write the CSV using a code block or Python script.

Scripts Reference

All scripts require requests: pip3 install requests

External skill scripts (use if available):

• skills/capabilities/review-site-scraper/scripts/scrape_reviews.py — G2/Capterra/Trustpilot reviews (requires Apify token)
• skills/capabilities/linkedin-post-research/scripts/search_posts.py — LinkedIn post search (requires Apify token)

Cost

• Quick / Standard: Free (uses WebSearch + free APIs like Wayback Machine CDX)
• Deep: Mostly free. BuiltWith paid API is optional (--api-key flag); free scraping is used by default.
• External skills (review-site-scraper, linkedin-post-research) may require paid API tokens.