GTM Studio Chronicle

44.1% of all Insights generated - The #1 use case where users ask the AI Data Agent to evaluate account quality, fit scores, or churn risk to prioritize where to focus their efforts.

22.8% of all Insights generated - Quick lookups of deal values, open opportunities, and pipeline status to accelerate decision-making.

17.1% of all Insights generated - AI helps summarize account context and suggest focus areas before meetings, replacing hours of manual research.

12.1% of all Insights generated - Helping sales teams write personalized intros, emails, and LinkedIn messages at scale.

Challenge: Spending $1M+ annually on Chorus but struggling to aggregate conversation intelligence data into executive-ready insights for leadership decision-making.

"We're trying to aggregate all the Chorus data and provide executive summaries. We have something internal that we're doing this with already, but it's not working well. We don't know how to prompt correctly." - Internal team feedback via kickoff call

Gap Identified: Customers need executive-ready conversation intelligence summaries, not just raw data aggregation. Current workaround requires manual ChatGPT processing.

Timeline Pressure: September deadline for technology decisions as they merge Paycore and Paychex organizations under new CRO leadership.

Approach: Three-phase implementation - conversation intelligence aggregation through Snowflake integration, white space analysis for net new customer identification, and campaign activation with marketing teams.

Challenge: Multi-sector business (B&I, healthcare, senior living, facilities) needing specialized targeting with accurate data for each vertical.

"It's really like the data integrity and the intent and being able to see a little bit more information about the prospects or the folks that we are trying to go after and probably more research around the potential customers." - Erin Feldman, VP InsideSales

Unique Requirements: Square footage + employee headcount at location level for facilities targeting - "500 employees but 2,500 square feet means they don't have 500 employees on site."

Comparison Context: Evaluating GTM Studio vs Clay for data validation, governance, and multi-sector campaign orchestration.

Implementation Gap: Hit custom field import limitation immediately, requiring CSV uploads for basic functionality and creating adoption friction.

Use Case: Creating a single pane of glass for sellers by stacking intent signals from multiple data sources including ZoomInfo, 6Sense, and TechTarget Priority Engine.

"So essentially anything that's got an API that we can feed out of, we can get in snowflake and bring it into GTM studio. We could get 6Sense insights, proprietary 6Sense insights into GTM studio." - Chris Hayward, GTM Strategy

Vision: Comprehensive account scoring widget showing aggregated intent signals with reasoning for sellers to understand account prioritization at-a-glance.

Integration Challenges: "It's the cost thing, right? Figuring out which data sources have APIs that they wouldn't charge us to access." 6Sense charges extra to export over API.

Adoption Blocker: "I'd love to give people permission to deploy lists... but I would never want that being pushed directly to CRM" - Need for role-based access control.

Challenge: "We have 305-600 cold calls per day from our SDR org and want to stack multiple signals for better targeting."

"The biggest way we'll see success is by bringing in our Snowflake data. We have millions of never-bounce credits and want to build campaigns around former customers who've moved companies." - Jared Volk

Implementation Reality: Success hinged on Snowflake integration complexity and data engineering resources. "We weren't gonna pay for both [tools]" - highlighting integration cost considerations.

Approach: Integrating Gong conversation data, Snowflake user analytics, and ZoomInfo signals for comprehensive account scoring.

Use Case: Private equity firm using GTM Studio to identify acquisition targets and analyze portfolio companies.

"We want to uncover companies that otherwise weren't on our radar. Maybe they were categorized as a manufacturing company but are actually a software company. How do we use GTM Studio to prioritize the top 50 companies we should really make sure don't fall through the cracks?" - Jeff Machlin, Founder

Solution: AI-powered web research to reclassify companies and identify true software businesses within manufacturing SIC codes.

Goal: "Combine first-party data with ZoomInfo insights to run more effective plays for new customer acquisition and existing relationship expansion."

Strategy: Using Form Complete integration, RingCentral events data, and pardot engagement to create unified customer journey views.

Implementation Challenge: Hit custom field import limitation, requiring workarounds for basic functionality.

Challenge: "Stacked signals" - multiple tools providing signals in disparate locations without unified view for prioritization and action.

"I think the buzzword is stacked signals. We've got signals in multiple places and multiple tools." - Mike Mathewson, Director of Sales Operations

Data Sources: LinkedIn paid social campaigns, Assembly tool integrations, and G2 review data for comprehensive account intelligence.

Activation Strategy: Marketing team targeting with segmented messaging based on signal combinations, routing through custom inbound processes.

Dual Focus: Better prospecting targeting for new logos plus new upsell motion launched this year requiring white space analysis.

"We want to be able to target better from a prospecting standpoint and then we as of this year, have more of an upsell motion. Making sure we understand the white space and how we should be targeting those with first and third party data." - Tierney Didier, VP Sales

Integration Priority: Gong conversation intelligence and Salesforce usage data for comprehensive account health and expansion opportunity identification.

Scope: ~1,000 current customers for upsell analysis, ~500 enterprise prospects for new logo targeting.

Implementation Challenge: Hit custom field import limitation immediately, creating adoption friction.

Implementation: Built workbooks with custom audiences based on intent signals and technographics.

AI Usage: Revenue model classification (subscription vs transactional) with source citations from pricing pages and investor docs.

Innovation: Tracking companies with high-volume travel-related job postings for B2B travel sales.

Result: Identified white space accounts not in CRM with strong buying signals.

Challenge: Identifying existing customers where they could expand contact coverage.

"The one thing I'm building out right now for our CS team is logic around: are we multi-threading yes or no? What would be really helpful is if we're not multi-threading, show those contacts within ZoomInfo that we could be reaching out to." - Brex Customer Success

Approach: Technographic analysis categorizing accounts by competitive technology, integration partners, and banking relationships.

Use Case: Replacing manual BDR research processes with AI-powered enrichment for comprehensive ABM scorecards.

"We have essentially a 2-page ABM scorecard with account headquarters, employees, revenue, revenue per employee, news, quotes, market research on accounts plus stakeholder information. This will enable us to do this to a lot more accounts - it's streamlined and easier than our current manual process." - Paul Zonis, Coretelligent

Duplicate Management Innovation: Using workbooks to identify and prioritize duplicate accounts - "We've got 3 Bone Dry Roofing accounts - which do we care about, which is a duplicate? If one has an account manager and is booking, that's the primary."

Impact: Scaling account research from manual, time-intensive process to automated AI-driven workflows for higher volume prospecting.