Melissa publishes its pricing, which already puts it ahead of most of the data category. The difficulty is not finding the numbers — it is that the published unit is a credit, the unit you care about is a usable record, and the conversion between them is not one to one. Teams size a tier off the headline per-credit rate, then discover their actual monthly consumption is a multiple of what they modelled. This guide is about doing that conversion properly before you commit.
Disclosure: Scout Data is our product; treat that entry as a maker’s pitch and the rest as our honest read.
Credits are the billing unit, not the work unit
A credit rate card tells you what a credit costs at each volume tier. It does not tell you how many credits your work consumes, because that depends on which operations you run. Verifying an address, verifying a phone, appending a name and running a fuller identity or property operation are not equivalent, and a single operation can spend several credits at once. The headline rate is therefore a floor, not an estimate.
This is not a criticism of the model — per-credit pricing is transparent and scales cleanly, which is more than the credentialed investigative platforms offer. It just has to be read in two steps rather than one.
How to read the rate card in four steps
- List the operations you will actually run, not the product names. “Verify the mailing address and confirm the phone is live” is two operations on every record.
- Find the credit cost of each one. This is published per operation; it is the number most buyers skip.
- Multiply by monthly record count to get monthly credit consumption, then pick the tier that consumption lands in — not the tier your record count lands in.
- Divide the tier cost by usable records, not by records submitted. Records that fail verification still consumed credits.
A worked example
The numbers below are placeholders chosen to make the arithmetic visible — substitute the current published values for the operations you intend to run. Say you process 40,000 records a month, and your workflow is two operations per record. That is 80,000 credit-consuming operations, and if each operation consumes more than one credit your monthly credit need is a further multiple again. Now suppose 82% of records pass verification cleanly. Your cost per usable record is the full tier cost divided by 32,800, not by 40,000 — roughly a fifth higher than the naive figure, before anyone has dialled anything.
The lesson generalises past this vendor: the denominator is where the cost lives. We work the same arithmetic for phone data in the skip tracing cost calculator.
The figures in the worked example above are illustrative placeholders, not quoted vendor pricing. Confirm current credit rates and per-operation credit costs with Melissa, and with our sales team for Scout Data.
What this category is genuinely good at
Data-quality tooling earns its place on the maintenance side of the house. If you hold a CRM with years of accumulated records, standardising addresses, parsing names consistently and flagging undeliverable contacts is real work with a real return, and doing it with a purpose-built verification service beats writing it yourself. That discipline pays off continuously rather than once, which is the argument we make in list hygiene for call centers.
Where it does not help is the acquisition question. Verification tells you whether the contact you hold is well-formed and deliverable. It does not tell you which homeowners are worth calling this month, and it cannot produce a phone for an owner whose number you never had. Teams that buy verification hoping to solve an empty-pipeline problem are solving the wrong half — a distinction we draw out in our Melissa alternatives guide.
Where Scout Data fits
List Builder sits on the acquisition side and removes most of the cleanup step by shipping records clean. Audiences are built from live property signals — storm exposure, permit activity, roof and system age, ownership change — and delivered with phones matched by name to the owner of record, scrubbed against the federal do-not-call registry before delivery, with dead numbers replaced. Pricing is quoted per record against your list profile, so the unit you are billed in is the unit you actually work.
Frequently asked questions
Is one Melissa credit the same as one lookup?
No, and this is the single most expensive misreading of a credit rate card. Credits are the billing unit; an operation is what you actually run. A given operation can consume more than one credit, and different operations consume different amounts. Before you size a tier, find the credit cost of each specific operation you intend to run and multiply.
Does Melissa find phone numbers for a list of addresses?
That is a different job from the one this category is built for. Verification and data-quality tools are strongest at checking, parsing and standardising contact data you already hold. Building a homeowner audience from scratch and attaching a phone that reaches the owner of record is an acquisition job — see bulk skip tracing for how that side is priced and measured.
What happens to unused credits?
Ask explicitly, and get the answer in writing before you buy a larger tier. Expiry and rollover terms differ across vendors in this category and they materially change the effective rate for anyone whose volume is seasonal. A cheaper per-credit tier that expires annually can cost more than a dearer one that does not.