Build a prospect list from real data, not bought lists
Mine public records, maps data and your own files to find the organisations that actually fit, then enrich them with named decision-makers and verified emails.
The problem
How it works by hand
Bought data is stale the day you get it, and every competitor with a credit card has the same spreadsheet. Half the contacts have moved on, the firmographics are guesses, and you end up paying per row for people who were never going to buy. Meanwhile the organisations that genuinely fit you are sitting in public sources nobody bothers to mine.
A worked example
What a working version looks like
We start from where your ideal customers actually leave traces: public registries, maps and places data, contract award records, industry directories, your own past invoices and enquiry history. The system pulls candidate organisations from those sources, deduplicates them, and scores each one against your fit criteria so the list ranks itself. Qualifying companies then go through enrichment: named decision-makers found, job titles checked, email addresses verified before anything gets sent to them. The output is a living list in your CRM, not a CSV that rots, and the same pipeline re-runs to keep it topped up as new organisations appear in the source data.
The exact tools change per business. The shape does not.
What it needs
Honest inputs, nothing exotic
- 01A clear picture of your best customer (we will sharpen it with you)
- 02Which public sources matter in your sector, we know most of them
- 03Your CRM or a spreadsheet to receive the list
- 04Your existing customer list, so we exclude them and learn from them
The payoff
What you get back
A target list nobody else is working, built from sources your competitors ignore, with verified contacts instead of guesses. Because the pipeline keeps running, the list stays current instead of decaying like bought data does.
Do it yourself
How you would build this yourself
No course, no upsell. This is the order we would build it in, with the tools named, and a prompt to start from.
- 1
Write the fit test first: your ten best customers and what they share, sector, size, region, what they bought. Every later step scores against this.
- 2
Pick one public source and mine it properly before adding more. Companies House has a free API, Google Places covers anyone with premises, and contract award notices show who buys what.
- 3
Dedupe on company number or website domain, never on name. "Smith & Sons Ltd" and "Smith and Sons" are the same firm, and bought lists are full of this.
- 4
Score and rank against the fit test, then enrich only the top slice: find a named person and verify the email with a checker like NeverBounce or ZeroBounce before anything sends.
- 5
Before you contact anyone, write down your UK GDPR legitimate interest reasoning. It is a short honest paragraph, and you want it written before the first email, not after a complaint.
Build me a prospect list builder using the free Companies House API. I sell [what you sell] to [who buys it]. 1. Look up the right SIC codes for that sector and suggest them to me before pulling anything 2. Pull active companies matching those codes in [your region], incorporated more than two years ago 3. Dedupe against my existing customer list, which I will give you as a CSV, matching on company number and website domain rather than name 4. Score each company 1 to 10 against this description of my best customers: [describe your ten best: size, sector, what they had in common] 5. Output a ranked CSV with company name, number, address, incorporation date and the score reasoning Then suggest two other public data sources worth mining for this sector, and summarise how UK GDPR legitimate interest applies before I contact anyone on the list.
Copy it into Claude Code, fill the brackets, and it will plan the build with you before writing a line of code.
We would rather show you how than bill you. The whole ladder of free help, answers, guides and the weekly build-along, is on the do-it-yourself page.