10% more opportunities in month one from better lead data
LinkedIn URL or domain → research, clean, dedupe → a profile a rep can sell from
This firm sells into enterprise accounts, and off-the-shelf tools only gave them surface-level data. We built a pipeline that turns a LinkedIn URL or domain into a full profile: hiring signals, exec appearances, org insights, clean job titles. It runs off a Google Sheet the team already used. Opportunities went up 10% in the first month, on about $1,000 a month of running cost.
What it's worth
New opportunities
- Monthly opportunity value before
- $250,000
- Lift in the first month
- +10%
- Extra opportunity value
- $25,000
- first month, measured
- +$25,000
What it costs to run
- Leads enriched per day
- 500–1,000
- Leads enriched per month
- ~16,500
- Cost per lead
- $0.06
- Cost per month
- ~$990
- per month
- ~$990
How this was calculated — every assumption
- The 10% lift is the client's own first-month result, against a reported monthly base of about $250,000.
- We do not annualise this. One month is one month, and a first month gets more attention than a fourth. If it held all year it would be worth far more, but we have not measured that, so we do not claim it.
- This is opportunity value, not closed revenue.
- Lead volume is the midpoint of the reported 500–1,000 a day, over 22 working days.
- The $0.06 per lead is measured, not estimated. Getting the same depth from an off-the-shelf platform cost several times more.
Results at a glance
- +$25,000 of new opportunities in the first month
- About $990 a month to run, at 500–1,000 leads a day
- Under 3% false positives
- Research time down to under two minutes a lead
- Delivered in two weeks, half the planned timeline
The problem
Platforms like Clay and Apollo gave surface-level data that was not good enough for enterprise selling. Research was slow and manual. Good prospects went unnoticed because nobody had the context to spot them. The client's own words: they tried the existing platforms, but costs were too high and the answers often were not good enough.
What was built
Input
A LinkedIn URL or company domain pasted into a Google Sheet. No new tool to learn.
Research
Apify scrapers, targeted AI research, and public data in one pass. The mix is what gets past basic firmographics.
Output
Hiring signals, media appearances, org insights, clean job titles, no duplicates. The context enterprise outreach actually needs.
Operations
Retries with backoff, full logging, and Slack alerts on failure. Self-hostable and sheet-driven. The team deployed it same-day.
What keeps it from breaking
False positives kept under 3%
Bad data is worse than no data. It sends a rep into a call with the wrong story. Accuracy was measured against a held-out set, not assumed.
Every source retries
Scrapers fail sometimes. Failures retry with backoff and reach Slack if they persist, so a bad source degrades the run instead of quietly cutting it short.
How the approach worked
Off-the-shelf tools answer the easy questions cheaply. The value is in the hard ones. A custom pipeline that mixes scraping, AI research, and public data beats a generic platform on both cost and depth, especially when it runs from a sheet the team already lives in. About $12,000 a year of running cost sits under a $300,000 movement.
The results
Opportunities rose 10% in the first month against a $250,000 monthly base. That is about $25,000 more a month, or roughly $300,000 a year. Under 3% false positives, 500 to 1,000 leads a day at $0.06 each, and research time down to under two minutes a lead. Delivered in two weeks, half the planned timeline.
Noah made sense of rough ideas, built exactly what we needed, and delivered more than expected.
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