Case Studies

The Challenge

Spara's CRM had become the central nervous system for sales, marketing, and customer success, but data quality and account prioritization were holding the team back. They faced four core problems:

• CRM data was inconsistent, incomplete, and expensive to enrich multiple times
• Sales and marketing had no reliable way to prioritize accounts
• Inbound leads came in with little context, forcing manual research before every call
• No historical traffic view to understand which accounts were growing, stalling, or declining

The Strategy

We broke the work into four connected systems:

1. CRM Data Cleaning and Scoring Engine

Created a master enrichment table in Clay that filled missing data, pulled traffic metrics, classified companies as B2B/B2C, detected CTAs, flagged inbound roles, and scored accounts automatically.

Impact: The CRM became clean, deduplicated, and cheaper to maintain. Sales finally had clear account tiers, and marketing could build campaigns around real prioritization instead of guesses.

2. Inbound Intelligence Workflow

For every meeting, HubSpot created a list, Clay enriched people and companies, AI summarized conversations, and Slack alerts went out with pre-meeting briefs.

Impact: Reps stopped doing last-minute research. Inbound meetings became sharper, more relevant, and better aligned to what the prospect actually cared about.

3. Web Traffic Intelligence System

Built historical traffic table with monthly SEMrush data, growth classification, and holistic scoring synced back to HubSpot.

Impact: Sales gained a new lens into account health. They could now reference growth, decline, or momentum directly in outreach and discovery calls.

4. HubSpot Audit and Redesign

Audited pipelines, automation, workflows, and integrations. Cleaned unused fields, documented architecture, and simplified systems.

Impact: The CRM became easier to manage, easier to scale, and easier to trust. Teams stopped fighting the system and started using it as the backbone of their GTM motion.

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Overview

QC Growth is a boutique go-to-market agency that partnered with NodeSource, a global leader in Node.js performance, observability, and security. NodeSource's platform empowers engineering teams to monitor and optimize production Node.js applications with enterprise-grade reliability.

In under three months, the campaigns reached 4,000 prospects, generated 300 responses (7.5%), including 200 positive replies (5%), booked 50 qualified meetings (1.25%), and onboarded 20 senior engineering leaders into a private Slack community.

"Jorge brought structure and scalability to our go-to-market engineering stack, combining technical precision with strategic insight to streamline how we operate."
— Luke Bivens, Founder, QC Growth

The Challenge

NodeSource had cultivated one of the most respected developer communities in the Node.js ecosystem, but their outbound motion lacked the structure, segmentation, and automation needed to effectively engage enterprise-level technical leaders.

Previous outbound efforts relied heavily on broad, persona-based targeting and generic outreach. As a result, engagement rates were low, and community-building initiatives like their private Slack channel were growing slowly.

The Strategy

The collaboration unfolded in three distinct phases:

1. Foundation: Do Not Contact List & Segmentation

Using domain, LinkedIn, and company name matching, the DNC framework ensured the outbound campaign targeted only net-new, high-value accounts. The team segmented the market into community users, product users, and director+ roles.

2. Execution: Director & Above Campaign

Using a tool that tracked engagement with GitHub repositories and related developer communities, we created a segment of leaders whose teams actively contributed to the client's technology stack. We developed a concise, conversation-style message designed for technical audiences.

3. Expansion: Community and Conference Campaigns

In parallel, the agency launched smaller-scale initiatives around industry conferences, open-source community users, and product usage cohorts.

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Overview

A leading legal operations platform that helps venture-backed startups shut down gracefully partnered with us to build a data-driven prioritization system for their sales and research operations.

The Challenge

Manually researching startups that might be closing was tedious and inefficient. The client needed a scalable, automated model to score thousands of companies and highlight the ones most likely to require their services.

The Strategy

We built a multi-variable scoring model that analyzed several indicators of potential shutdowns:

1. Funding and Time-Based Indicators

Calculated months since last funding, assigning higher scores to companies with more than 24 months of inactivity.

2. Organizational Health Indicators

Enriched with employee count, headcount growth, website activity, and email validation status.

3. Founder Behavior as a Closure Signal

Most powerful insight came from founder LinkedIn enrichment—if founders no longer listed startup as current position.

4. Testing and Scale

Piloted with 250 companies (90% accuracy), then scaled to 10,000+ companies.

5. Operational Activation

Deployed targeted advertising and prioritized cold calling.

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Overview

A leading global RPA and enterprise automation company with more than 5,000 employees engaged us to help streamline their account research process for enterprise sales.

The Challenge

Before the engagement, the sales development team handled roughly 100 accounts per quarter per rep, each requiring 2–3 hours of manual research. With each rep spending 250 hours per quarter on repetitive manual research, and average labor cost of $50–$100 per hour, the inefficiency translated into tens of thousands of dollars in wasted time.

The Strategy

We designed an AI-driven account research framework:

1. Data Foundation and Enrichment

Imported 10 sample accounts into Clay, scaled to 100. Automatically enriched with firmographic and contextual data.

2. Role Mapping and People Intelligence

Developed enrichment layer categorizing job titles into three standardized seniority levels.

3. Industry and Department Standardization

Cross-standardized industry and department data. Dynamically linked relevant case studies.

4. Multilingual Personalization

Added language detection and routing workflow (English/Spanish).

5. Automation and Execution

All enriched data flowed into account research template, generating one master table instead of 100 separate spreadsheets.

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Overview

IAG Real Estate, the Institutional Advisory Group, helps institutional investors, private equity funds, and family offices uncover off-market commercial real estate opportunities. Their research-driven approach relies on proprietary data, deep market expertise, and advanced automation.

When IAG approached us, their challenge was scaling their ability to retrieve and verify ownership contact information for off-market properties.

The Challenge

In commercial real estate acquisitions, speed and accuracy define competitive advantage. IAG's acquisition researchers were manually researching hundreds of off-market properties per month, pulling ownership and contact data from multiple disconnected databases.

This process was slow, inconsistent, and prone to gaps in data coverage.

The Strategy

We built a custom Clay-based automation system:

1. Property-Level Data as the Source of Truth

Each workflow began with property address

2. Layered API Automation

Created intelligent sequence of HTTP API calls: Atom API, Reonomy API, ContactOut API

3. Data Consolidation and Normalization

Built data parser and normalization workflow to standardize JSON outputs

4. Automation + Human Validation

Included manual verification checkpoint

5. Team Training and Knowledge Transfer

Conducted hands-on training

"Outside of being truly wonderful to work with, Jorge is incredibly knowledgeable in the tools and strategies that drive success in the GTM world. He helped us reverse-engineer a system of multiple HTTP API calls within Clay, connecting our industry-specific databases with alternative data providers. The end result was an automated workflow that could replace 40% of our manual research work."
— Shae Lawson, Institutional Advisory Group (IAG)

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Overview

Nexrizen, a software engineering firm specializing in digital solutions for law practices, faced a challenge common among early-stage service companies: identifying and reaching the right clients efficiently.

To overcome this, we developed a systematic process to qualify potential clients using Google Reviews as a proxy for operational pain points, transforming anecdotal feedback into a lead-generation engine.

The Challenge

Before our engagement, Nexrizen relied on broad, manual prospecting lists that failed to differentiate between thriving law practices and those struggling with client satisfaction. They wanted to:

• Target firms most likely to benefit from their solutions
• Validate outreach relevance by finding "proof of pain"
• Scale a repeatable automated lead qualification system

The Strategy

We designed a data-enrichment workflow combining Clay and Apify:

1. Identify Law Practices within Nexrizen's ICP
2. Find Google Maps Links using Clay's enrichment tools
3. Extract Reviews via Apify (10 recent reviews per firm)
4. Merge & Analyze Data into unified text document
5. Classify Issues using AI to detect customer service problems
6. Prioritize Outreach to firms with recurring issues

The Execution

We operationalized this process as a self-contained "lead qualification engine." Clay became the central command center, Apify provided automation for extracting reviews, and AI-driven prompts summarized key pain themes.

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Overview

RBC is an educational platform that helps entrepreneurs and small business owners leverage business credit—not personal credit—to grow their companies. The company teaches business owners how to clean up personal credit, establish business credit, and secure funding for expansion.

When RBC decided to evolve from a training-only model into a done-for-you service, they faced a new challenge: building relationships with banks at scale.

The Challenge

RBC's transition into a service offering meant they now needed direct partnerships with banks and relationship managers who could process credit applications for their students. Their internal team had no structured method for identifying key decision-makers, finding accurate contact information, or personalizing outreach.

The Strategy

We started by defining the target roles inside different types of banks. The first workflow focused on banks in Minnesota, then expanded to Florida, Wyoming, Colorado, and Utah.

For each region, we used Clay to:

1. Identify and enrich contacts at local and regional banks
2. Validate email and LinkedIn data
3. Create customized LinkedIn and email outreach copy tailored to each banker's background

To test response quality, we personally ran one of the email sequences.

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Overview

Our client is a marketing consultancy helping B2B companies scale their marketing performance through customer led growth, events done correctly, and focus on IRL life interactions.

We collaborated to build a modular outbound system that combined LinkedIn first-degree connection data, Clay automation, and channel experimentation across email and LinkedIn.

The Challenge

They wanted to validate a hypothesis: that first-degree connections, when properly segmented and enriched, could outperform cold outbound. However, the team lacked an efficient system for segmentation, personalization, and experimentation.

The Strategy

We designed and documented a repeatable process combining Clay's data enrichment with LinkedIn ad signal detection:

1. Extract and Enrich LinkedIn Connections

Downloaded founder's connections and imported into Clay

2. Segment by Job Function and Seniority

Divided into marketing roles and executive roles

3. Identify Advertising Activity

Queried LinkedIn Ads Library to determine if companies running paid campaigns

4. Personalize Outreach at Scale

Personalized PS lines, dynamic email body based on ad activity

5. Experiment Across Channels

Ran controlled experiments: LinkedIn-only, Email+LinkedIn, Reverse-sequenced

The Execution

Each stage tracked and automated inside Clay, with verified contact data sent to Lemlist. Built dashboards to monitor campaign health. All steps documented in SOP and recorded on Loom.

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