Lead Quality Analyzer
Score and rank your leads by quality signals so your sales team spends time on the prospects most likely to convert.
How to Score Lead Quality
Not all leads are equal. Sales teams waste enormous time chasing low-fit prospects while hot leads go cold. Lead scoring solves this by ranking prospects based on signals that predict conversion — so reps focus where it matters.
This Lead Quality Analyzer evaluates key B2B signals: whether the email is a business domain (versus a free provider like Gmail), the seniority of the job title (decision-makers score higher), and company size. It produces a 0–100 score, a letter grade, and a temperature tier (Hot/Warm/Cool/Cold).
For production use: real lead scoring platforms combine dozens of data points — firmographics, intent data, technographics, and behavioral signals — pulled from enrichment APIs. This tool applies the core fit signals and gives you a useful quick assessment for individual leads.
A Worked Example: Scoring a 2,000-Row Export
A typical input is a lead export with columns for name, title, company, email, phone, country and source. The analyzer checks each row for completeness (how many of the important fields are filled), validity (does the email have a plausible shape, does the phone have enough digits for its country, is the title a real job title or a placeholder) and consistency (does the email domain match the company name, is the same person present twice under slightly different spellings). Each row gets a score and a reason list; the summary at the top shows how the whole file is distributed.
On a realistic 2,000-row export, the pattern is usually a large block of high-scoring rows, a tail of rows with a missing title or phone, and a small cluster of rows that are clearly junk: test entries, personal email addresses on company leads, names in the email field. That cluster is the first thing to remove, the tail is what to enrich or deprioritise, and the block is what goes to sales.
How to set your own thresholds
- Decide which fields are required for your process. If your sales team will not call without a title, a missing title should cost more points than a missing phone.
- Decide what score means “ready”. A common choice is: 80 and above goes straight to outreach, 50 to 79 goes to enrichment, below 50 is reviewed by a person or dropped.
- Re-run after enrichment. The point of the score is to measure the improvement, not to grade the file once.
Limits
The analyzer judges the shape of the data, not the truth of it. A perfectly formatted row about a person who left the company two years ago scores well. It cannot verify that an email address is deliverable, only that it is well-formed; the deliverability checklist covers the next step. And it does not know your market, so a “low-quality” row by the formatting rules may be your best prospect. Use the score to order your attention, not to replace it. For a methodology that weights business fit rather than data completeness, see Lead Scoring 101.