Saleshandy Lead Finder: The Credit Rules Behind the Coverage Headline

Saleshandy Lead Finder advertises 852 million contacts and 42 million companies. That figure tells you almost nothing useful about whether the tool will build the segment you need, and the numbers that would tell you are further down the same pricing page: how many credits a plan carries, how many filters that tier unlocks, and what a reveal costs when it includes a phone number.

Coverage is the number every data vendor leads with because it is cheap to compute and impossible to falsify from outside. The credit arithmetic is the number that decides what the tool costs per usable row.

Everything below is verified against saleshandy.com and its machine-readable pricing document, fetched 15 August 2026. We run Email Bison for sending and HeyReach for LinkedIn, so this is a documentation read rather than an operator's account.

The published plans, and the filter count that moves with them

Saleshandy publishes Lead Finder as its own plan line, separate from the email outreach subscriptions. It also publishes a structured pricing summary at saleshandy.com/pricing.md, explicitly written for AI agents and procurement tools, and its figures match the rendered page.

The filter count moving between tiers is unusual enough to flag, because it changes what the tool can express rather than just how much of it you get. A segment definition that needs a filter your plan does not carry cannot be built at any credit volume. Anyone evaluating on the basis of a specific targeting requirement should confirm that the filter exists on the tier they intend to buy, not merely that the vendor's marketing lists it somewhere.

There is one internal disagreement worth disclosing rather than resolving. Saleshandy's Lead Finder product page describes 75 or more people and company filters, while the pricing document assigns 60 to Starter and 70 to Pro. Both are live on the vendor's own surfaces at the same time. The plausible reading is that 75 is the total catalogue and the tier figures are what each plan unlocks, but the pages do not say so, and the honest handling of a vendor contradicting itself is to attribute each figure to the page it appears on and ask before committing.

The credit rules, which decide the real cost per row

Three published rules govern the economics, and the second one is the one that surprises people.

One credit reveals a verified email address. A verified email plus a direct phone number costs seven credits in total. Half of any unused monthly balance rolls into the next cycle.

Run that through the Starter plan and the difference is stark. At 2,500 credits a month, email-only reveals produce up to 2,500 contacts. The same allowance with phone numbers attached produces roughly 357. A team that leaves phone reveals on by default is buying a seventh of the list it thinks it is buying, and will conclude the database is thin when what actually happened is that the credits went somewhere else.

Saleshandy describes its data as a waterfall across nine providers and states that you pay only for verified results. The waterfall shape is the standard architecture for this problem and the reasoning behind it is set out in waterfall enrichment: chain sources cheapest-first, escalate only on a genuine miss, and the savings come from the ordering rather than from any single provider being better.

852M+contacts claimed

Coverage, which is the cheap number to publish

42M+companies claimed

Company-level records behind the contacts

1 vs 7credits, email vs email plus phone

The ratio that actually sizes a plan

9providers in the waterfall

The vendor's description of its supply chain

Coverage is quoted, accuracy is not

The gap between those two words is where most database disappointment lives, and it is worth being precise about it because no vendor will be.

Coverage is the share of submitted records for which a source returns a value. It takes minutes to compute, it is comparable across vendors on a spreadsheet, and it improves whenever a provider guesses more freely. Accuracy is the share of returned values that are true today, and measuring it requires a human checking rows one at a time against a current source. A provider returning plausible but eighteen-month-old job titles scores identically to one returning correct ones.

The practical consequence for a lead database specifically is that a stale record is worse than a missing one. A missing address announces itself and gets checked. A confidently wrong address gets emailed, bounces, and takes a small piece of the sending domain's reputation with it. That is the mechanism by which a data decision becomes a deliverability problem two weeks later, and it is why email verification tools belong in the workflow even when the source claims verification. Data decay covers why the underlying facts move regardless of who supplied them.

The only accuracy figure worth acting on is the one you compute yourself. Pull a hundred rows, check them by hand against public sources, and use that result rather than any published claim. It costs an afternoon and it is the sole accuracy measurement that will ever exist for your specific segment.

1. Step 1Write the segment query first

   List the filters the definition genuinely needs before opening any pricing page

2. Step 2Confirm those filters exist on the tier

   Filter counts move between plans, so a definition can be unbuyable at any credit volume

3. Step 3Decide about phone numbers deliberately

   Seven credits against one changes the plan you need by a factor of seven

4. Step 4Sample a hundred rows by hand

   The only accuracy measurement that will ever apply to your segment

5. Step 5Re-verify before sending

   A verification status from the source is a claim about then, not about now

Saved filters are the feature that decides whether this compounds

Filter counts get compared and saved filters get ignored, which is backwards. Saleshandy lets you name and save a filter set, and that capability is what separates a database from a search box.

The reason is repeatability. A segment you can re-run is a segment you can measure. Run it in January, work the list, and in April re-run the identical definition: the new rows are companies that entered your criteria since, which is a genuinely useful signal rather than a re-export. Run a slightly different definition instead and the difference between the two lists tells you nothing, because you changed the ruler and the thing being measured at the same time.

That discipline also protects the work when people change. A saved filter is a written definition that a new joiner can read, question and inherit. A definition that lived in whoever built the list is lost the week they move teams, and the replacement rebuilds it from memory, slightly differently, and the campaign results across the two quarters stop being comparable without anybody noticing why.

Two habits make saved filters worth what they cost. Name them for the decision rather than the criteria, so the reason survives alongside the mechanics. And record the row count each time the filter is run, because a segment whose size moves sharply between quarters is telling you either that your market shifted or that the database's coverage of it did, and both are worth knowing before the next campaign is sized on the assumption of a stable pool.

Where a database beats an extension, and where it does not

Saleshandy sells two ways to get a contact, and they suit genuinely different work.

The database is the right tool when the requirement is a segment: a repeatable definition of who to reach, expressible in filters, that can be re-run next quarter and produce a comparable list. Saved filters make that repeatability real, and it is what separates a targeting decision from a browsing session. The discipline that makes it work is writing the ideal customer profile in terms the filters can actually express, which is a harder exercise than it sounds and is where most segments quietly go wrong.

The extension is the right tool when the requirement is a shortlist: named accounts, warm referrals, a conference attendee list, anything where the people are already identified and the missing piece is contact detail. Saleshandy Connect covers that path, including the same credit rules applied one profile at a time.

The failure mode worth naming is using the extension for segment work. The resulting list is selected by whatever the search interface happened to surface rather than by the ICP, and nothing downstream can recover the definition afterwards, because there was never a definition to recover.

The house position on the list once you have it

A verified list is an input, not a campaign. Our own practice at RevenueFlow is one message per campaign, with no bumps and no thread replies, so a second contact is a fresh campaign with a genuinely different angle rather than another step under the first. Saleshandy's outreach product is built around multi-step sequences and Lead Finder pushes into them directly; that is a description of the tool rather than a recommendation, and a single-message programme will draw on its credits far more slowly than the vendor's own examples assume.

For where this vendor sits in the wider market, cold email software is the category view and best Saleshandy alternatives is the comparison. If a built segment and a live campaign would answer the question faster than another evaluation, see what a first campaign looks like.

Pricing and features verified as of August 2026. Verify current terms with the vendor before relying on them.