Databook can score the accounts in your territory so you know which ones to work first. You choose which parts of the service you need: a scored version of your existing account list, a new list built from your ideal customer profile, deeper AI signals on the topics you care about, or all of these together.
What you get
A scored account list. Every account matched to a Databook company record and scored for propensity to buy.
Use case scores. A score per use case at each account, so you know what to lead with as well as who to call.
AI signals. Answers to specific questions about each account, built around your own topics, products and priorities.
Output in a usable format. A flat file, or a table shared into your own data environment.
How it works
The service runs in four stages. The service is customized to your needs: you can choose which types of scoring are most relevant to include.
1. Build the account list
You can start from your own accounts or from a profile.
Your existing account list. You sync the accounts from your CRM through the Databook Salesforce integration, share your account list as a csv file, or share a table via Databricks Delta Share. Databook matches each account to the corresponding company record.
A firmographic search. If you do not have a fixed list, describe the profile you are targeting, for example industry, revenue size or geography. Databook searches its company universe and returns a candidate list matching that profile.
2. Algorithmic scoring (optional)
Databook runs its standard automated scoring against the list: the Databook Score, its four subscores, and a score per use case. You can also ask for the underlying strengths and weaknesses ratings on the financial metrics behind the score.
See How the Databook Score and use case score work for what each score measures.
3. AI signals (optional)
AI signals answer specific questions about each account, from a simple factual lookup to a more nuanced assessment. They are configured for you, around the topics, products and roles that matter to your business.
See AI signals for account prioritization for the categories available.
4. Delivery
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 via Databricks Delta Share | Larger lists, and requests that need to be refreshed on a recurring basis |
Example use cases
Splitting a territory across a sales team by propensity to buy rather than by account size.
Choosing which accounts to include in a campaign for a particular use case or product.
Building a target list from scratch when entering a new industry or region.
Enriching your CRM with a prioritization score your reps can sort and filter on.
What to expect
Scores are an outside-in, objective input. They are a guide to which accounts look high-propensity, not a definitive prediction, and they work best alongside your own qualification and relationship knowledge.
Most scoring is delivered as a one-off, point-in-time score. The Databook Score can also be delivered as a daily updating score when it is shared into your own data environment as a table.
The stages run in sequence: matching your accounts, adding any companies not already covered, generating scores and signals, then compiling the output.
Many teams run algorithmic scoring across the full list first, then run AI signals only against the accounts that score well.
Getting started
Contact your account team. They will help you scope the list, choose the scoring and signals you need, agree a delivery format, estimate the credits required to run the service, and agree a timeline.