1. Contact your account team
Tell them what you are trying to do: split a territory, build a target list for a campaign, enrich your CRM, or something else. The stages of the service are combinable, so the goal determines what you actually need.
2. Decide where the account list comes from
You have a list. Export the accounts from your CRM, or share a table from your own data environment.
You do not have a list. Describe the firmographic profile you are targeting instead, for example industry, revenue band and geography, and a candidate list is built from Databook's company universe.
Most projects fall between 500 and 20,000 accounts, but we can work with you to accommodate the coverage you need.
3. Include the strongest identifiers you have
Accounts are matched to Databook company records before anything can be scored, and the quality of that match depends on what you send.
Identifier | Effect on matching |
DUNS number | Most reliable. Include it wherever your CRM holds it |
Legal company name | Strong, and used to validate matches made on other identifiers |
Company website | Useful as a fallback when a name is ambiguous |
Company name only | Workable, but produces more unmatched accounts and needs closer review |
Where a subsidiary is the better fit at a parent company, or the reverse, accounts may be mapped to a recommended parent company.
4. Choose your scoring and signals
Decide whether you want algorithmic scoring alone, or algorithmic scoring plus AI signals.
For scoring, decide whether you also want the strengths and weaknesses detail behind the financial metrics. See How the Databook Score and use case score work.
For AI signals, think about questions you want answered about each account. See AI signals for account prioritization for the categories available and how signals are defined. We can work with you to co-design the AI signals you need.
5. Choose a delivery format
Method | Best suited to |
Flat file export (CSV) | One-off or smaller requests, or teams without a data environment to share into |
A shared table in your own data environment | Larger lists, and anything that needs refreshing on a recurring basis |
If you want the Databook Score to update daily rather than as a point-in-time score, it needs to be delivered as a shared table.
6. Delivery
The work runs in sequence: matching your accounts, adding any missing companies, generating scores and signals, then compiling the output. The Databook team will agree a timeline with you for delivery based on the size and complexity of the request. The Databook team will also provide an estimate of how many credits would be required to provide scoring and signals for the selected accounts.