TL;DR
- Compare vendors on accepted records for your actual territories, industries and roles. Database size and filled cells do not establish correct, current or usable data.
- Give each vendor the same input, preserve its raw output and blind reviewers where practical. Separate identity, completeness, freshness and contactability so one favourable metric cannot hide a serious failure.
- People Data Labs is a useful API-first candidate, Apollo a workflow and enrichment candidate, and Cognism a relevant phone-data candidate. Their units and verification labels differ, so price the same accepted output rather than equal credit counts.
- Use a stratified sample and report missing or uncertain evidence explicitly. A hypothetical cost model can guide procurement; it must not be presented as a live benchmark.
Write the acceptance rule before requesting samples
A GTM data bake-off is a controlled purchasing exercise: vendors receive comparable inputs and their outputs are judged against a written business requirement. It is useful because advertised database breadth tells little about the subset your sellers can act on.
Start with the downstream decision. An SDR calling German finance leaders needs different fields from an analyst resolving US corporate subsidiaries. Define the target unit, required fields, acceptable age, permitted use and exclusions before opening the samples. Otherwise the test tends to become a tour of whichever fields each vendor fills most confidently.
For a calling motion, an accepted row might require the correct person at the correct current employer, an eligible role and an independently assessed business phone route. For account planning, acceptance might require the correct legal/trading entity, operating geography and a sourced company attribute. Do not combine these into a universal “accuracy” figure.
Separate mandatory fields from useful extras. A missing mobile can fail a mobile-calling requirement without making the company identity wrong. Conversely, a plausible phone does not rescue a contact attached to the wrong subsidiary. Our B2B data API comparison covers product procurement; this framework governs the evidence used to make that choice.
What to test in three different data offers
People Data Labs: inspect the match as well as the returned fields
People Data Labs documents Person Enrichment input parameters, including the importance of supplying identifying information. For an API-first team, it is useful to test matching with the same realistic input quality the production pipeline will supply. A rich hand-curated test row can exaggerate performance if the real system only has a name and vague company label.
Its pricing and credit guidance describes successful enrichment responses as a charging basis for relevant APIs. A technically successful match still needs your acceptance check. Count paid successful responses separately from correct current people and useful fields.
This route fits engineering teams that can preserve raw responses, use the matching controls and integrate accepted fields into their own process. It is less suitable if the buyer expects the API to decide territory suitability and campaign permissions automatically. Obtain the current product-specific tariff and commitment for the intended person/company operations; one credit should not be assumed to have the same price across products and tiers.
Apollo: distinguish base data from a waterfall
Apollo's waterfall overview describes sequential enrichment through Apollo and connected sources. That is operationally useful, but a waterfall result is not the same test as Apollo's own source alone. Label which configuration produced each field and which validation step ran.
The waterfall usage guide distinguishes native usage reporting from external providers connected through API keys. Include external bills when calculating the output cost. Otherwise a convenient combined result can appear cheaper than a single-source competitor because part of its spend is off the platform's dashboard.
Choose this candidate when discovery, enrichment and seller workflow need to operate together. Avoid treating its workflow convenience as proof that every territory's data is suitable. Freeze the waterfall order, plan, settings and credit schedule for the test, then compare either the whole configured workflow or the base source consistently.
Cognism: test the phone proposition in your actual market
Cognism's Diamond Data documentation describes additional phone verification for its Diamond data. The meaningful procurement question is how much of your desired sample has that status and whether the field is suitable for your calling process.
Keep verified and other returned phone fields in separate columns. Record available verification dates and status definitions; do not extend a vendor label into a claim about live answer rate or meeting conversion. Those outcomes also depend on call timing, caller reputation and the commercial offer.
Cognism is worth evaluating where phone-led prospecting and relevant market coverage justify a contract. It is weaker value if your motion only needs account attributes or you cannot operationally use the verified phone subset. Obtain a scoped quote for users, markets, export/API rights and verification services. No dependable like-for-like public unit price was established here, so the example below uses clearly labelled illustrative budgets rather than a supposed Cognism tariff.
Construct a fair sample
Use 500 accounts and 1,000 contact rows as a manageable example, with two relevant contacts per account. Stratify the accounts by the segments you actually serve. A sample dominated by large US technology companies should not decide a purchase for small continental-European manufacturers.
Include difficult cases deliberately: recent role changes, subsidiaries, similar company names, non-English titles and records with incomplete inputs. Label those strata rather than slipping them into the sample invisibly. Report ordinary and difficult-case performance separately so the buyer can see whether a problem is frequent enough to affect production.
Keep a known reference subset whose facts have recent independent evidence, but avoid using only easy known records. The purpose is to assess both matching and net-new coverage. Preserve an input hash, run date, API version, plan and query settings. Give each vendor the same information unless the documented test explicitly compares different discovery workflows.
Blind the reviewer to the provider where practical. Randomise row order and assign neutral vendor codes. Keep raw outputs read-only, with a separate decision sheet. A vendor can explain an ambiguous result, but any corrected output should be labelled as a second pass rather than silently replacing the original sample.
Score evidence without rewarding filled cells
| Dimension | Question | Failure example |
|---|---|---|
| Identity | Is this the intended company/person? | Same name, different employer |
| Completeness | Are the required fields present? | Mobile missing in a mobile-only test |
| Correctness | Does independent evidence support the field? | Wrong operating country |
| Freshness | Is the evidence timely enough for the decision? | Former employee returned as current |
| Usability | Can we use the record under the workflow and licence? | Excluded territory or prohibited activation |
Mark supported, unsupported and uncertain states. A missing reference source is not automatically proof that the vendor is wrong. It also should not count as verified correctness. Report the uncertain fraction alongside the acceptance rate and examine whether uncertainty clusters in a commercially important segment.
Independent email validation is another observation, not an infallible truth label. Catch-all domains, temporary errors and provider disagreement need explicit treatment. Avoid sending unsolicited messages or making calls merely to manufacture a benchmark; use an authorised operational pilot when live contactability is genuinely required.
Use the ABM Account Scoring Template to retain segment and suitability reasons. Keep vendor field quality separate from your own fit judgement. A record can be accurate and still describe a company your business should not pursue.
A five-hundred-account bake-off
Suppose Vendor A returns 900 contact rows, of which 760 pass identity and freshness checks and 640 satisfy every mandatory rule. Vendor B returns 820, with 740 passing identity/freshness and 680 fully accepted. Vendor C returns 950, with 720 passing identity/freshness and 610 fully accepted. These are hypothetical values, not measured vendor results.
Against the original 1,000-row requirement, usable coverage is 64%, 68% and 61% respectively. Against returned rows, the accepted fractions are about 71%, 83% and 64%. Both views matter. B wins the usable total in this example despite returning the fewest rows; A's larger file does not win simply because more cells arrived.
Then inspect account coverage. If B's 680 accepted contacts cluster in 350 accounts while A covers 390 accounts, an account-based motion may prefer A despite its lower contact total. Define whether the goal is two contacts per account, at least one eligible contact or the greatest total of usable people. The denominator must match that goal.
Do not hide segment reversals in the aggregate. If C is best in one high-value region, it may earn a targeted secondary role. A waterfall can be economically sensible, but run it on the unresolved subset and measure the incremental accepted output. Adding every source to every row makes duplication expensive.
Calculate cost per usable record
Assume total sample costs, including vendor charges, validation and operator time, are $600 for A, $850 for B and $500 for C. Cost per fully accepted contact is $0.94, $1.25 and $0.82, rounded. The cheapest per accepted record is C; the most complete is B. Neither fact alone determines the purchase.
Add commercial floors. A low marginal API rate can still require unused monthly commitment. An annual seat contract may make sense for a staffed calling team but be expensive for a small monthly export. Include integration, refresh, extra credits, licensing rights and support in the production model.
Calculate incremental fallback cost too. If a second source costs $120 to produce 80 additional accepted contacts, it adds those contacts at $1.50 each, even if its standalone average is lower. Prefer the fallback when those gaps matter enough to justify the premium, rather than buying breadth for its own sake.
Turn the result into a buying decision
Choose the provider that meets the mandatory segment requirements with the best total economics and an operable integration. Document the trade-off in plain terms: more usable accounts, better phone coverage, lower cost or fewer ambiguous matches. Avoid an unsupported universal ranking from one sample.
Before signing, verify production entitlement and repeat a small sample after any major configuration change. Keep the accepted benchmark as a regression fixture, with reference facts refreshed when they age. The Salesforce enrichment guide and HubSpot enrichment guide cover the destination-specific workflow after the data purchase.
FAQ
Is a 1,000-row sample statistically conclusive?
It can support a useful procurement decision, but its design and segment mix matter more than the round count. Small subsegments can remain uncertain even when the total sample is large. Report those limits and request a targeted follow-up where a critical territory has too few examples.
Should unknown fields count as wrong?
Count them as failures of a mandatory completeness requirement when the field is needed. For correctness, distinguish unknown from demonstrably wrong. That preserves the reason for rejection and avoids claiming an accuracy result that the available evidence cannot establish.
Can the vendor clean the sample first?
Only if that service is part of the production offer you intend to buy. Keep the original pass and assisted pass separately, with their time and cost. A bespoke sample prepared by specialists should not be compared directly with an unassisted API response and priced as ordinary self-service usage.
What if no vendor wins every segment?
Buy a primary source for the largest suitable segment and evaluate a fallback on the unresolved rows. Track provenance and marginal accepted cost. A blended purchase is justified by useful incremental coverage, not by the reassurance of having several familiar vendor names in the stack.
