The Customer Context Graph is the foundation of the GTM Decision System. Every insight, recommendation and generated asset you get from Databook reasons over it. This article explains what goes into it, what it produces, and why you can rely on what comes out.
What the Customer Context Graph is
A continuously updated intelligence layer that connects everything your organisation knows about a customer: your interactions with them, external signals about their business, their stakeholders, your opportunities, and the outcomes you have delivered. It exists so that both your sellers and the AI working alongside them can understand what is happening in an account, why it matters, and what to do next.
Two design decisions shape what you get from it.
It is organised around the account, not the deal. Context is established before execution begins and held across every handoff, so it survives conversion, reassignment and turnover instead of leaving with the person who had it.
It starts with your customer's reality, then adds yours. Independently verified third-party intelligence establishes why your customer needs to change and why now. Your own first-party context establishes why you. You need both, and the order matters: starting from internal records alone carries their blind spots forward instead of correcting them.
What it gives you
A single shared picture of the account. Everyone from the CMO to the BDR works from the same customer truth, instead of each seller assembling their own version from whatever they could find.
Context that survives handoffs. "Why this account, why now" is usually held in one person's head and lost within a quarter. Here it is held by the system.
Answers you can defend. Every data point carries its source, so a claim in an executive meeting can be traced back rather than asserted.
Reasoning that is specific to your business. The more of your own context you connect, the more the guidance reflects your solutions, your methodology and your position in the account rather than a generic view.
Verified third-party intelligence
This half of the graph is licensed and curated rather than scraped or inferred, and it is what tells you what is happening in your customer's world.
Financial and company intelligence from S&P Global Capital IQ: financials drawn from filings, annual reports and audited statements, analyst consensus estimates, executive profiles, key developments and earnings transcripts
Firmographics and industry classification using the Global Industry Classification Standard (GICS), covering 11 sectors, 25 industry groups, 74 industries and 163 sub-industries
Technographics covering more than 37,000 products across 172 subcategories, updated weekly
Contact data of approximately 12 million verified contacts, re-verified every 90 days
Analyst-curated intelligence including strategic priorities, sub-industry C-suite, digital and ESG priorities, executive compensation and peer groups
Web search for news on demand and intelligence on private companies
Dimension | Coverage |
Companies indexed | Around 2.6 million, including public and private companies and public sector organizations |
Sub-industry insight sets | More than 160 sub-industries, covering C-suite, digital and ESG priorities |
Peer groups | Human-in-the-loop verified peer groups |
Data validation | Automated validation & data quality checks by data suppliers and Databook, plus review by Databook analysts for selected insights |
Refresh cadence | Financial fundamentals and analyst estimates checked every 6 hours |
Your own first-party context
This half tells Databook where you already stand with the account. You choose what to connect, and most organizations start with three or four sources.
Type of context | Where it comes from |
Accounts and opportunities | A CRM integration such as Salesforce or Dynamics. This also brings the surrounding deal context: activities, opportunity probability and the contacts on each deal |
Meeting transcripts | Your conversation intelligence provider, such as Gong or Zoom |
Upcoming meetings and invitees | Your CRM, Gong or Zoom, or a calendar integration with Google Calendar or Outlook Calendar |
Internal and external communications | Collaboration apps such as Slack or Teams. Email and messaging either from your CRM, where it is already synced there, or from a Gmail or Outlook integration |
Product and field marketing | Use cases, case studies and products configured in Databook, enriched by uploading documents, scraping your marketing sites, or a CMS integration such as Seismic or Highspot |
Custom AI signals | Signals you configure for account scoring or territory planning, which both rank your accounts and feed the Decision System as evidence |
A few things worth knowing before you plan this:
Start with accounts and opportunities. There is no fixed order, but your CRM is the practical starting point and it makes every other source more useful.
Meeting transcripts add what was actually said, rather than what someone summarized into the CRM afterwards.
The invitee list is the valuable part of a calendar integration. It tells you who will be in the room before the meeting happens.
Marketing context is partly configuration rather than integration. Setting up your use cases, case studies and products in Databook is what lets the system speak to your own offering, not just to the account.
Integration is not the only route. If connecting a source directly is not practical, you can assemble the context yourself and provide it to Databook instead.
The graph reflects what your connected systems capture. Interactions that live only in someone's memory, an unrecorded call or a private message thread are not visible to it.
What Databook derives from it
Reasoning over both halves produces the judgment your teams actually use:
Databook Score, a propensity-to-buy score built on Financial Case for Change, Management Intent, Investor Sentiment and Budget Timing
Peer benchmarking against both declared peers and algorithmically derived peer sets
Growth and profitability forecasts, plus industry and sub-industry growth, normalized so they can be compared against reported results
Risk threads and signals surfaced across the whole account rather than a single deal
Account-level judgment: customer priorities, pain points, whitespace, deal risk, propensity, recommended actions, and the why change, why now, why us framing your sellers take into the room
Why you can trust what comes out
Core intelligence cannot be edited. It stays as verified, so a number in a customer-facing document is the number the source reported.
Every data point shows its provenance. Source, last updated and last checked are visible, with a link through to full provenance.
Verification is built in. Third-party data is licensed rather than scraped, and Databook analysts review it at the points where accuracy matters most, including curating strategic priorities across more than 10,000 companies.
What the Context Graph powers
Everything in the Decision System sits on this foundation: the coaches and agents your teams work with, the assets they generate, the guidance delivered into your CRM, Slack or Teams, and the analytics your leaders govern with. Read GTM Decision System for how those layers work.
Getting started
Talk to your account team about which first-party sources to connect first, and in what order. The third-party foundation is available to you from the start.