What Leadz Category Snapshots Show About Contact Coverage
A business-record total is not an email-list total. This study compares stored primary-email and primary-phone availability across six Leadz categories so buyers can see the difference between the size of a category and the contact fields present in it. The figures are historical inventory aggregates, not current mailbox tests, unique-person counts or measurements of outreach performance.
The coverage study CSV contains the source counts and dates. The article was reviewed on September 23, 2026; that review does not change the age of the underlying data.
Read the three dates before reading the percentages
The national public-page snapshots were saved on July 18, 2026 and retrieved for this study on September 6, 2026. Their embedded aggregate update timestamps are December 14, 2025. Those dates describe different events: when the aggregate says it was updated, when a page snapshot was saved and when the study retrieved it.
None of those dates establishes when every individual business record was checked. They also do not tell us when an email was last tested for delivery. A buyer comparing this study with a current search should therefore keep the two sets of observations separate. Differences can reflect changed inventory, different filters, record corrections or changes in how a segment is represented.
We selected architects, dentists, electricians, plumbers, restaurants and roofing contractors because they provide useful contrasts across practices, trades and local businesses. The selection is purposeful. It is not a random sample of all Leadz categories or all United States businesses, and the results should not be generalized to categories that were not studied.
The method reads existing stored aggregates. It does not recollect business information, independently resolve every company identity or validate mailboxes. That boundary is important: the study can accurately describe what these saved counts say without claiming to establish the present quality of every underlying contact.
Compare availability within each category's denominator
Each percentage divides the stored count of records with a primary contact field by that category's stored business-record total. The table rounds percentages to one decimal place. Phone and email columns are independent measures; a business can appear in both.
Category | Business records | Primary email present | Email share | Primary phone present | Phone share |
|---|---|---|---|---|---|
68,809 | 25,681 | 37.3% | 66,566 | 96.7% | |
246,533 | 37,060 | 15.0% | 240,767 | 97.7% | |
52,804 | 8,145 | 15.4% | 50,748 | 96.1% | |
50,981 | 7,683 | 15.1% | 49,512 | 97.1% | |
499,115 | 60,650 | 12.2% | 464,113 | 93.0% | |
60,707 | 13,321 | 21.9% | 58,909 | 97.0% |
For architects, the calculation is 25,681 divided by 68,809, multiplied by 100: approximately 37.3%. For restaurants it is 60,650 divided by 499,115, multiplied by 100: approximately 12.2%. The restaurant category has a smaller proportion with email but a larger absolute count with email than the architecture category.
Those two observations are compatible. A percentage describes the composition of a category; a count describes the volume recorded in it. Neither tells an agency which market to enter. The right category still depends on the offer, delivery territory, account criteria and economics of serving the businesses in question.
Understand what these counts cannot identify
Business records are not necessarily unique companies. A company may operate several locations, appear under related categories or have name variations that require review. Adding all six category totals would therefore not establish a combined universe of unique businesses. Adding state totals to national totals would also mix overlapping populations.
A useful comparison is the distinction in the BLS explanation of firms and establishments: one firm can have multiple establishments. Leadz records are not being claimed to follow the BLS statistical methodology. The point is conceptual: locations and companies answer different questions, so a counting method needs to name its unit.
The saved contact counts also do not establish the intersection of email and phone availability. Separate totals can tell us how many records have each field, but not exactly how many have both. A stacked chart implying mutually exclusive email and phone groups would be misleading. That is why the diagram uses independent email bars rather than stacking channels into a supposed total.
Additional contact fields introduce another distinction. A business can have a primary email and additional addresses, potentially with names or roles associated with saved profiles. Those addresses are not automatically separate people or separate buyer accounts. The CSV field guide explains how to retain those relationships without multiplying the apparent number of prospects.
Separate a present field from a usable contact route
The study uses the historical aggregates as stored. It does not reconstruct the complete generation history of each snapshot or independently validate every value. A field can be populated while still requiring cleaning, identity review or a current check before use. Treat a saved count as an inventory observation, not a promise about deliverability.
For a buyer, there are several separate questions. Does the business match the offer? Does the listed contact route belong to that business? Is a named person or function appropriate for the subject? Is the address currently usable for the intended channel? What rules and preferences govern contacting it? A single “has email” flag cannot answer all of them.
The availability versus verification guide explains those distinctions. In a working sheet, preserve each question separately. That makes it possible to diagnose whether a disappointing sample has a category problem, a contact problem or an unresolved role-research problem.
Phone availability deserves the same restraint. A primary phone value can be present without establishing that the number reaches the right location, that a person will answer or that calling is appropriate. This study contains no channel comparison of replies, appointments, revenue or customer acquisition cost. Higher stored phone coverage does not prove a better sales channel.
Use a sample to connect inventory with your actual workflow
Suppose a fictional supplier serves commercial roofing businesses in one state. The national roofing email share gives it a question to investigate, but the supplier needs a current sample matching its trade and territory. Residential-only firms, existing customers and businesses outside delivery coverage may not belong in the campaign even when their contact fields are populated.
In an illustrative review of 50 records, imagine 36 match the business criteria, 30 represent distinct target accounts and 18 have an appropriate contact route after research. The resulting rates are 72% relevant records, 60% distinct target accounts and 36% accounts with the required route, each using the original 50-row denominator. These are hypothetical numbers, not findings from the historical study.
If the question instead concerns contact-route availability among the 30 distinct target accounts, the rate is 18 divided by 30, or 60%. Both 36% and 60% can be correct, but they answer different questions. Put the denominator beside the metric so a later reader cannot mistake one for the other.
Use the sample evaluation workflow to document this review. Preserve exclusions and unresolved cases rather than silently removing them before reporting a favorable percentage. A sample that contains uncertainty can still inform a decision when the uncertainty is visible.
Reproduce the study without creating false freshness
Download the CSV and preserve an unchanged copy. Check the category label, total, primary-email count, primary-phone count and the associated dates before calculating anything. A spreadsheet formula for email share is email count divided by total; format the result as a percentage and round only for presentation.
For a simple arithmetic check, confirm that each count is nonnegative and does not exceed its own total. Then independently recompute one row by hand or with a calculator. These checks catch transcription and formula errors. They do not validate the underlying businesses, so keep the validation note appropriately narrow.
Keep unrounded calculations in the working file. Repeatedly rounding intermediate values can create small discrepancies when a table is reused, especially if another analyst tries to reconstruct a count from the displayed percentage.
If you repeat the exercise with new data, create a separate dated table. Record the collection method and any changed filters or definitions. Do not overwrite a historical aggregate with a current count while retaining the old title or methodology. A time comparison is only meaningful when differences in coverage and counting are understood.
The multi-state territory guide applies the same discipline to separate state snapshots. Those snapshots have their own save dates and should not be blended into this national table as if every number were collected simultaneously.
Turn the findings into better purchasing questions
Within these six stored categories, primary-phone availability exceeds primary-email availability in every row. Architects have the highest primary-email proportion, while restaurants have the largest primary-email count. Those are bounded observations about the selected historical inventory. They do not establish a best industry, a current export yield or a likely campaign result.
Use the findings to ask a provider better questions: What does a record represent? Which field is being counted? Can I inspect a relevant segment? How are multiple locations and additional contacts represented? Which dates describe the records, the aggregate and the published page? What happens when a required field is absent?
Then test the exact segment your team would use. Keep the historical study as background, your sample findings as current working evidence and the commercial decision as a separate judgment. This separation makes the purchase review understandable even if the inventory changes next week.