How to Generate Leads

How do you define an Ideal Customer Profile?

Take your best current customer — profitable, renewing, not painful to serve — and describe them in four lines: what they sell, who buys from them, roughly how big they are, and where they operate. Add one line on the problem you solve for them. That is a usable Ideal Customer Profile, and it beats any framework you have not written down.

Keep it narrow on purpose. A profile broad enough to include everyone gives you no way to reject a company, which means every list you build will be long and every outreach round will be diluted.

How do you build a list of companies that match?

You need companies plus a recorded reason for each. Manually that means searching directories and industry associations and writing notes as you go; it works and it is slow. Automated, it means starting from a domain that already fits and letting the matching happen from that evidence — with Leads10 you paste the seed company's homepage and get matching companies back with a fit score from 70 to 100 and a three-part match reason.

Either way, stop at the point where you have a list you could defend in a pipeline review. Then reach the right people at those companies, which is where a contact data tool belongs.

• One row, one reason — no unexplained entries.
• Cut anything you would not want a rep to spend an hour on.
• Keep the list short enough to personalise every message.
• Log which reasons produced replies, so the next list is better.

Which lead generation channels are worth your time?

Four carry most B2B results: search and content, which compounds slowly but keeps working; outbound email, which is fast to start and unforgiving of a bad list; LinkedIn, which suits considered, low-volume approaches; and referrals, which convert best and scale worst. Pick two you can sustain rather than four you cannot.

None of them rescue a poorly chosen list. A precise list with an ordinary message outperforms a large list with an excellent one, which is why qualification comes before channel choice.

What should you measure?

Track replies per hundred companies contacted, meetings per hundred, and the reasons that appeared on the rows that replied. Those three tell you whether your profile is right. Contacts added, emails sent and open rates tell you how busy you were.

Give any change four to six weeks before judging it. B2B cycles are slow enough that a week of data mostly measures noise.

How does turning a domain into business prospects actually work?

You enter your company website address. Leads10 analyzes the business and builds an Ideal Customer Profile before you choose how many prospect companies to identify. No step asks you to guess filter settings.

When verification finishes, Leads10 automatically opens Review Analysis and Prospects, your saved results page. Each company includes its name, industry, website, company LinkedIn page, fit score, and a written match reason. You can search by company name, industry, website, or match reason; filter and sort the list; and export selected companies to CSV.

• Step 1 — Enter your domain. Your own homepage, or an existing customer who already fits well.
• Step 2 — Review the business analysis. It shows what you actually sell, your industry, company size, and the pain point you solve.
• Step 3 — Review your Ideal Customer Profile. It includes target industries, company size, suggested decision-maker roles, and pain points.
• Step 4 — Scroll down on the same page and choose 1–5 prospects for the batch.
• Step 5 — Stay on the page while Leads10 checks each company's details and fit, with live progress. Please do not refresh.
• Step 6 — Review Analysis and Prospects opens automatically. Each company has its name, industry, website, company LinkedIn page, fit score (70–100), and match reason. Search, filter, sort, and export selected companies to CSV.

What is a fit score, and what does the number actually mean?

A fit score is a number from 70 to 100 that says how closely one company's situation matches what you sell. It is a judgement about fit, not a prediction that the company will buy. Companies scoring below 70 are not returned at all, so every company on your list has already cleared a bar.

The score always arrives with a written match reason, so you can read the reasoning and disagree with it. A score with no explanation is impossible to check; that is why every score here comes with one.

• A score goes up when the company's own website shows the problem you solve, when its size and market resemble customers you already serve well, and when what it sells implies a need for your kind of product.
• A score goes down when the fit rests on the industry label alone, when the company looks much larger or smaller than your usual customer, or when the evidence on its site is thin.
• 90 to 100 means a close match on several independent grounds. 80 to 89 means a solid match with one clear gap. 70 to 79 means a plausible match worth a look, not a priority.
• The score cannot know a company's budget, its timing, who holds the decision internally, or whether it already bought from a competitor last month.

What is pain-point matching, and why is it different from filtering?

Pain-point matching means checking whether a company appears to have the specific problem you solve before it goes on your list — instead of accepting it because its industry label and headcount fit a filter. It is the step that decides whether the rest of your outreach has any chance.

Filtering by industry and job title tells you a company exists and someone there has a plausible title. It does not tell you that the company needs what you sell. Two companies can share an industry, a size and a country and have completely different problems, so a list built from filters alone contains a large share of companies that were never going to buy — no matter how good the email is.

The matching here works in the other direction. It starts from your own website to establish what you sell, then reads each candidate company's public site for evidence of the matching pain point, and writes down the reason. You can read that reason and reject it, which is the point.

• What it uses: what your site says you sell, what a candidate company's own public site says about its business, and how the two line up.
• What it produces: a written, per-company reason tied to your specific offer — not a filter result you have to interpret yourself.
• What it cannot see: internal budgets, purchase timing, politics, contracts already signed, or anything a company does not publish.
• Worth being clear about: contact databases are not simply dumb filters. Apollo sells buying-intent signals and ZoomInfo sells lead scoring. Those signals are behavioural and firmographic — what a company browsed, how big it is. None of them start by reading your website to learn what you sell.

How can a Leads10 match reason help with personalized outreach?

A Leads10 match reason gives you company-specific context you can check and adapt into a relevant subject line, email opening, LinkedIn message, call plan, or account brief. It explains how the prospect's apparent pain point connects to what you sell, instead of leaving you with only a company name and broad industry label.

For example, a generic opening such as ‘Can we help with your operations?’ can become a specific starting point such as ‘Your public site describes expansion across several service regions; our platform helps teams coordinate that kind of multi-location operation.’ The fictional example shows the method, not a claim that Leads10 knows private facts or guarantees a reply.

Leads10 does not write or send outreach and does not provide a named person's contact details. Review the match reason, confirm the evidence on the company website, use the suggested decision-maker roles to decide whom to research, and use the company LinkedIn page to continue that research.

Does Leads10 give you email addresses and phone numbers?

No. Leads10 returns companies, not individual people's contact details. You get the company name, its website, its company LinkedIn page, the pain point it appears to have, the match reason and the fit score. You do not get a named person's email address, direct dial or mobile number.

That is deliberate, and it is where Leads10 sits in a normal stack: pair it with a contact database such as Apollo, ZoomInfo, Cognism or Lusha to find the right people at the companies it identifies. Leads10 decides which companies are worth contacting; those tools tell you who to contact there.