AI signals answer specific questions about the accounts in your list. A signal can be a simple factual lookup, such as which cloud provider a company uses, or a more nuanced assessment, such as whether sustainability is a board-level priority. Signals are configured for your business, around the topics, products and roles you care about.
What signals can cover
The following table contains just a handful of examples - AI signals are flexible and can be tailored to your needs
Category | What it surfaces | Example |
Strategic priorities | Whether a topic is a board-level focus area for investment | Does the company have a stated sustainability priority? |
Web intelligence | References to specific initiatives on the company's own website or in its job postings | Public references to Scope 3 emissions targets |
Buyer insights | Whether the company employs people in particular roles or departments | Count of supply-chain related employees, director-level and above, including a list of relevant job roles at the account |
Technographics | Investment in, or use of, a specified product or vendor | Which cloud platform the company primarily uses |
Transcripts | Mentions of macro topics by company leadership on public earnings calls | How often leadership discussed supply chain risk |
Financial insights | Qualitative strong, medium or weak ratings on top-level financial metrics | A weak rating for gross margin versus competitors, indicating a case for change at the account |
How AI signals are configured
AI signals are flexible and defined per customer, rather than picked from a fixed catalogue. In practice that means working with you to name the specifics: the topic for a strategic priorities or web intelligence signal, the list of products or vendors for a technographics signal, or the roles and seniority for a buyer insights signal. Your Databook account team works through this with you before the project starts.
Once a signal is defined it can be re-run, so you can refresh the same signals across the same accounts on an agreed cadence.
AI signal quality
AI signals draw on the same high-quality data sources (including premium licensed data and human-in-the-loop reviewed insights) as the rest of the Databook product. AI signals can also use Databook's powerful reasoning capabilities to provide signals you can trust to make strategic GTM decisions (e.g. designing territories or campaigns).
What signals cannot do
They are not a raw data feed. Signals are an assessment built on underlying data, and the underlying third-party data itself is not delivered to you, either live or as a file.
Financial insights are banded. You get qualitative strong, medium or weak ratings on top-level financial metrics. Exact figures, such as a precise revenue growth percentage, cannot be shared this way.
Transcript signals cover public companies only. They rely on public earnings calls, so private companies are out of scope for that category.
Using signals alongside scoring
Algorithmic scoring runs across a whole list quickly, and AI signals go deeper on each account. A common approach is to score the full list first, then run signals only against the accounts that score well, so the deeper analysis is spent where it will change a decision.
See How the Databook Score and use case score work for what the algorithmic scores measure.
AI signals as a source for the GTM Decision System
The AI signals you choose can be stored in a data table and refreshed regularly (e.g. monthly or quarterly) and used as a source of context for the GTM Decision System. This helps customize your Decision System outputs with the signals that matter to your organization.
Getting started
Contact your account team to scope and co-design the AI signals you need.