Start with a representative sample.
Select accounts that reflect the work your sales and marketing teams plan to do. Include your main security categories, company-size bands, and regions. Add difficult cases such as renamed companies, subsidiaries, and accounts where you know a security leader recently changed roles.
Keep the sample fixed across providers. If each provider chooses its strongest records, you are evaluating different populations. Record the sample's selection rules and avoid treating a small convenience sample as a statistically representative view of the whole market.
Questions and evidence to request
| Evaluation area | Question | Evidence to inspect |
|---|---|---|
| Account coverage | Which of our sampled companies can you match? | Matched and unmatched accounts, with domain and parent-company handling |
| Role coverage | Can we identify the functions our use case needs? | Current employer, title, function, and confirmation source where available |
| Freshness | What does a refresh actually check? | Field-level timestamps where available, validation definitions, and update process |
| Contact quality | What is validated, and what remains uncertain? | Validation status definitions and treatment of unknown or catch-all results |
| Export quality | Will these records fit our systems? | Sample export, stable identifiers, field dictionary, and null-value conventions |
| Data operations | Can we prevent duplicates and unwanted overwrites? | Account matching, deduplication, suppression, and field-ownership workflow |
| Corrections | How does an incorrect record get fixed? | Documented reporting, correction, and removal process |
| Commercial scope | What limits affect our intended use? | Written plan details for seats, exports, API access, and support |
Use an evidence scorecard.
For every area, record one of three outcomes: demonstrated, partially demonstrated, or not demonstrated. Attach the sample observation or documentation that supports the result. Set essential requirements before comparing providers so a high total score cannot conceal a critical gap.
- Account match rate: correctly matched sample accounts divided by all sample accounts.
- Required-role coverage: sample accounts with the agreed buying roles identified divided by eligible sample accounts.
- Observed record accuracy: records confirmed correct divided by records actually reviewed, with uncertain records reported separately.
- Duplicate rate: duplicate records under your matching rule divided by exported records.
Include the raw counts beside each percentage. Explain how the records were checked and when. A daily refresh process does not mean every field on every record was independently reverified that day; ask the provider to explain its exact process.
Run a bounded operations check.
- Inspect a sample export before importing it.
- Map provider fields to a test copy of your CRM schema.
- Check account identifiers, duplicate contacts, and empty values.
- Define which system owns each field and how updates are approved.
- Apply your organization's outreach eligibility and suppression rules.
- Review corrections and unexpected records before expanding the segment.
A data evaluation does not require sending a campaign. You can learn a great deal from matching, record inspection, and a controlled import. If you later run an outreach pilot, keep data quality results separate from message relevance and sales execution.
Evaluating One Cyber
Bring this checklist and a description of your ideal customer profile to our team. One Cyber provides cybersecurity market segmentation, buyer contact context, daily refresh and validation, unlimited leads and downloads, and API access. Ask to see how those capabilities apply to your sample.
Explore the buyer database, buying committee worksheet, and current pricing before your evaluation.
Bring your target market to the conversation.
Tell our team which accounts and buying roles you need to understand. We will discuss how One Cyber fits your revenue workflow.
Talk to our team →