How to Preview Lead Counts Before Spending Credits

A lead count preview helps you test whether a filtered audience is worth investigating before retrieving contact records. Start with the companies and roles you need, check the matching total, then review a bounded set of records when the segment looks workable.

Keep the operations separate: previewing counts is free; retrieving leads uses the applicable credits; downloading an already saved list adds no search-credit charge.

Use the market coverage guide when you need to connect database coverage with wider market assumptions.

• Exact count: the number of database records matching your current filters when the query ran. An exact zero means the query found no matches.
• Estimated count: an approximate total for planning. Use it to judge the rough size of an audience. Keep the estimate label in your notes and review a small set of actual records before setting an export target.
• Count unavailable: the service could not produce a total. Keep the same filters for one retry. If it persists, try a smaller country, company-size band or set of company domains.

If the count looks plausible, choose a small retrieval limit and define the acceptance rules before you run the search. For a role list, check the full title and company on each returned row. For a work-email campaign, check the work email column specifically. Record how many rows meet both requirements before expanding the list.

A count is useful only when you know which question it answers. Record the campaign purpose, target account criteria, relevant roles and exclusions before adjusting filters.

• Account criteria: industry, company size, relevant location and known company domains.
• Contact criteria: title variants, department or seniority where supported and relevant.
• Additional stored criteria: company revenue or funding dates when they genuinely affect the target definition.
• Research criteria: technology context, a current investment round or actual buying responsibility that still needs supporting evidence.

Distinguish contact location from company location. A person working remotely in one country does not establish that the employer is headquartered there. Stored revenue or funding dates also need a recency and meaning check.

Open the lead count preview with the minimum criteria that define a relevant audience. Keep mandatory constraints in place, including markets you can serve and roles the offer concerns.

Add or change one criterion, then compare the matching total. This isolates the effect of that edit. It does not prove that the excluded records were poor fits.

Check title variants, field definitions, location choice and query logic before changing the customer profile. An empty result can reflect limited database coverage or missing fields, not an empty market.

Record each filter definition, the observed count and the date in your own review process. Label what changed. Comparing two different definitions without a change log makes later counts difficult to interpret.

When the audience looks plausible, retrieve only the records needed for an initial review. Check company fit, current role and required contact fields before expanding the retrieval.

There is no universal number of leads that makes a segment viable. A focused account-based project and a broad research campaign have different needs. Judge the count against the work the team can actually perform.

A total does not show whether the returned companies fit the offer or whether the contacts still hold the relevant responsibility. Use explicit acceptance rules for the initial review.

• Does the company match the account definition rather than only a broad keyword?
• Does the full title and current company support the intended buying role?
• Are the required contact fields present, with unresolved values kept visible?
• Have duplicates and existing-customer or campaign exclusions been handled?
• Have email validation, preferences and applicable outreach requirements been reviewed separately?

The data quality checklist provides a broader framework for evaluating returned data.

Dashboard lead searches use one search credit per returned lead; an empty search returns no contacts and uses no search credits. Repeating a search can spend credits again, including when a person has appeared in an earlier result.

A download of an already saved list uses no additional search credits. Public API usage has a separate pay-as-you-go credit balance. Check the current pricing and credit rules for the operation you intend to run.

For planning, distinguish the number of retrieved records from the number accepted for the campaign. Review and validation effort belong in the workflow cost even when the final download itself is free.

Keep the filter definition, count observation date, accepted list version, exclusions and next-action owner together. Revisit the audience before reuse when the campaign scope or important account assumptions change.

Test the matching count first. When the definition looks workable, retrieve a filtered list, review the records and download the accepted saved list for the next step.