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What is Waterfall Enrichment?
Incomplete CRM records are a persistent and costly problem for GTM teams. Missing emails prevent campaigns from running, missing phone numbers block SDRs from reaching prospects, and missing job titles make it impossible to target the right buyers. Waterfall enrichment addresses this by querying multiple data providers in sequence until a match is found, delivering meaningful coverage gains for teams operating at scale.
Traditional sequential waterfalls introduce tradeoffs that compound over time, including first-match limitations that prioritize speed over accuracy, unpredictable per-field credit costs, and conflicting data with no reliable source of truth.
What is Waterfall Enrichment? breaks down how the method works, where it breaks down, and how parallel enrichment logic changes the calculus for data-driven GTM teams.
In this guide, you’ll learn:
- How sequential waterfall enrichment works and how teams configure provider priority
- The coverage, cost, and quality tradeoffs that emerge as volume grows
- How parallel enrichment and confidence scoring improve on traditional sequential approaches
- When a multi-vendor waterfall makes sense and when a unified platform delivers better ROI
GTM Engineers: The Real Trends Behind the Hype
GTM Engineering is having a moment. Job boards, hot takes, and no shortage of hype. But most of what passes for GTM Engineering today is patchworked data, fragile workflows, and a single technical hire holding it all together.
The real shift is not a new job title. It is a new capability, one that spreads across RevOps, demand gen, SDRs, and AEs when the underlying data infrastructure is built to support it. The teams getting this right are not the ones with the most specialized headcount. They are the ones with clean, continuously refreshed data and orchestration tools that do not require a specialist to operate.
In this guide, you’ll learn:
- Why GTM Engineering is a distributed skill set, not a single role, and what that means for how teams are structured
- The data infrastructure requirements that separate production-grade GTM systems from hacked-together enrichment workflows
- How waterfall enrichment models work, where they break down, and what a pre-built, QA'd alternative looks like at scale
- What flexible, signal-rich orchestration actually enables across outbound, expansion, and churn prevention motions
Find out what it takes to build a GTM system that any operator can run, not just the one person who knows how it works.
Building Modern Data, Analytics, and AI Governance
As generative AI transforms business operations, organizations are racing to deploy powerful new technologies while struggling to maintain control, compliance, and trust. Without proper governance frameworks, enterprises risk data breaches, algorithmic bias, regulatory violations, and eroded stakeholder confidence.
This comprehensive TDWI Best Practices Report reveals how leading organizations are building modern governance frameworks that span data, analytics, and AI to ensure responsible, secure, and compliant use of these critical business assets. Download this essential research to learn:
- Why only 26% of organizations have mature AI governance practices and what separates leaders from laggards
- How to extend data governance foundations to cover analytics pipelines and AI model lifecycles
- The critical roles, teams, and organizational structures needed for successful governance at scale
- Which technologies and platforms deliver the biggest impact, from data catalogs to model monitoring tools
- Key challenges organizations face when operationalizing AI in production environments and how to overcome them
- Best practices for measuring governance success across trustworthiness, security, privacy, ethics, and compliance
Download the full TDWI report now and discover how to build governance frameworks that enable innovation while protecting your organization from risk.
The Hidden Gap in Most ABM Motions | ZoomInfo
Most teams running ABM execute it well within a narrow window. Marketing drives top-of-funnel engagement, sales takes over for deal progression, and customer success handles onboarding. Each function operates independently, and the precision that made ABM valuable dissolves the moment a prospect converts.
Account-based go-to-market extends that precision across the entire customer journey, from initial targeting through expansion, with sales, marketing, and RevOps aligned around the same accounts, the same signals, and the same outcomes.
In this video, you’ll learn:
- How account-based go-to-market differs from traditional ABM and where the full-funnel extension changes the motion
- Why a shared data foundation is the prerequisite for sharper targeting, more relevant messaging, and cleaner handoffs
- How to activate an account-based motion across paid media, outbound, website personalization, and field channels
- How to measure account movement across the full journey rather than relying on top-of-funnel marketing metrics
See what it looks like when ABM precision carries through the entire customer lifecycle rather than stopping at awareness.
ZoomInfo GTM Studio vs. Clay: Better Filtering, Easier Workflows, More Data
Most GTM teams building outbound workflows in Clay run into the same friction points: limited front-end filtering that forces expensive enrichment runs just to qualify a list, complex setup requirements that take months to learn, and data coverage that is constrained by what is visible on LinkedIn.
This walkthrough breaks down three dimensions that directly affect how efficiently a GTM team can build and activate a list: filtering depth before credits are spent, ease of use for operators who are not dedicated GTM engineers, and the breadth of the underlying contact and company database.
In this video, you’ll learn:
- How front-end filtering options differ between the two platforms and what that means for credit spend and time to a qualified list
- How waterfall enrichment setup compares and where the operational complexity lives in each tool
- Why database coverage matters when building TAM lists and what the gap looks like across specific industries
- How the AI agent inside GTM Studio guides workflow construction for operators who want results without building complex multi-step automations
See how the two platforms compare across the workflows that matter most to outbound GTM teams.
GTM Studio: The AI Tool Replacing an Entire GTM Stack
Most sales teams are running five or more tools to accomplish what should be a single, connected workflow: building lists, enriching contact data, finding signals, personalizing outreach, and pushing records into a sequencer. The operational overhead of connecting and maintaining those tools often costs more in time and money than the tools themselves.
This walkthrough breaks down what a consolidated GTM workflow looks like in practice, covering audience building, custom AI enrichments that go beyond standard filtering options, waterfall enrichment that returns the highest-confidence result across multiple data providers, and signal detection across hiring, funding, and M&A events.
In this video, you’ll learn:
- How custom AI enrichment columns can surface insights that standard filters and shared signal tools cannot replicate
- How parallel waterfall enrichment differs from traditional sequential approaches and what it means for contact data coverage
- How to layer signals onto a list and use them to trigger personalized outreach automatically
- How automated refreshes keep GTM workflows running across an entire TAM without manual intervention
See what it looks like when data, signals, enrichment, and activation operate inside a single workflow.
Empowering Marketing Leaders with Data‑Driven Strategies
Marketing leaders are constantly challenged to drive growth, personalize engagement, and uncover new revenue opportunities. But without the right Go-to-Market Intelligence foundation, marketing efforts can become fragmented, leading to wasted budget, missed opportunities, and ROI that’s nearly impossible to prove.
If you’re looking to sharpen your strategy and drive better outcomes, consider this your blueprint for success and the inspiration to make it happen.
Running ZoomInfo Inside Claude and Slack: Turning Data Into Action Anywhere You Work
GTM work doesn't happen in one system anymore. It happens across AI assistants, collaboration tools, CRM, and engagement platforms. The question is whether your data can keep up with where the work is actually happening.
The Model Context Protocol makes it possible to embed live B2B intelligence directly into the tools GTM teams already use. In this walkthrough, ZoomInfo CEO Henry Schuck demonstrates what that looks like in practice, running ZoomInfo inside both Claude and Slack to show how the same intelligence surfaces across different working environments.
In this guide, you’ll learn:
- How to build a hyper-targeted account list using a natural language prompt inside Claude
- What it looks like when first-party and third-party data combine to generate a full pre-call brief
- How the same ZoomInfo intelligence surfaces inside Slack without any change to the underlying workflow
- What it means for GTM execution when data follows you across tools instead of living in one place
See how the surface area for acting on B2B intelligence is expanding and what that means for how GTM teams operate.
Turn ChatGPT into a Prospecting Machine with ZoomInfo
ChatGPT is already part of how most GTM teams work. But when it comes to finding the right contacts at the right accounts, it runs out of road fast. Without a live connection to verified B2B data, it can point you toward a database. It cannot replace one.
The Model Context Protocol, or MCP, is an open standard that allows AI models to pull from external data sources natively within a conversation. It is what makes a direct connection between ChatGPT and a live B2B database possible, and it changes what prospecting inside ChatGPT can actually look like.
In this guide, you’ll learn:
- What MCP is and how it enables AI tools to work with live, external data
- What prospecting looks like when ChatGPT has access to verified contact and company intelligence
- How natural language prompts can replace manual research and enrichment workflows
- Where this kind of integration fits into a broader GTM motion
Find out how the right data connection transforms what AI can actually do for your team.
The GTM Laws of Physics
Every go-to-market team is racing to embed AI into their revenue motion. Most are stalling before they see measurable results. The problem is rarely the model. Models are now a commodity. What separates the teams producing extraordinary outcomes from the ones generating expensive noise is the data beneath the model.
In The GTM Laws of Physics, ZoomInfo VP of Account Management Alex Lazerowich introduces a governing framework built around four sequential laws: Context, Timing, Targeting, and Content. These laws cannot be violated without consequence, and the returns compound in order.
In this guide, you’ll learn:
- Why context is the real competitive moat in an AI-powered GTM motion
- The four laws that govern every GTM motion and why the order matters
- The Four Foundational Layers that turn raw data into AI-ready intelligence
- Real-world examples of companies that built this framework and what it produced
Find out what it takes to build a GTM engine that respects the laws and compounds returns over time.
