Client: Moss
The Data Foundation | Prospecting Engine, TAM + Buying Committee Mapping
Before partnering with Revenanas, Moss faced a critical bottleneck in their go-to-market motion: their team couldn't reliably identify high-quality prospects using the public data available to them. This challenge manifested in several key pain points:
- Fragmented and Unreliable Lead Data: Prospect information was scattered across multiple sources, incomplete, and often outdated, making it impossible to get an accurate picture of potential customers.
- Manual Prospecting Paralysis: The sales team was spending significant time on manual research and qualification work—time that should have been spent on actual selling conversations.
- No Predictive Capabilities: Without clean, unified data, Moss couldn't build the machine learning models needed to predict which prospects were most likely to become high-value customers.
-Low Confidence in Targeting Decisions: The team knew they needed better intelligence on indicators like website traffic from ads, office locations, and remote work policies, but had no systematic way to capture and analyze these signals.
The breakthrough came when Moss committed to an intensive, focused engagement. Rather than a drawn-out project, we compressed the work into a four-week sprint designed to deliver maximum impact quickly.
1. Building Advanced Clay Workflows
We engineered sophisticated Clay workflows to systematically clean and unify data from across Moss's tech stack. These workflows automated the enrichment process and ensured consistent, reliable data flowing into their systems.
2. Creating a Unified Target Account Database
We delivered a comprehensive, enriched database of target accounts—going far beyond basic firmographic data to include the specific signals Moss needed to identify high-spend potential customers.
3. Delivering Foundational Datasets
We provided clean, structured datasets that served as the bedrock for all future analytics and targeting work. This wasn't just about cleaning existing data—it was about building a permanent asset.
4. Running Predictive Regression Analysis
We conducted regression analysis to identify which data points were the strongest predictors of high credit card spend. This included factors like:
- Website traffic volume from paid ads
- Number of office locations
- Remote work policy indicators
- Other public signals of business scale and spend patterns
5. Building Signal Monitoring Infrastructure
We implemented systems to track and surface buying signals across target accounts, ensuring the team would never miss a high-value opportunity.
The four-week sprint delivered immediate and transformational capabilities for Moss's revenue operations.
For Moss:
- Higher Confidence in Targeting: The team now operates with data-backed confidence when identifying and prioritizing prospects, eliminating guesswork from the targeting process.
- Dramatic Time Savings: By automating data enrichment and research workflows, the team reclaimed hours previously lost to manual prospecting work.
- Predictive Targeting Capabilities: Moss can now identify which prospects are most likely to become high-spend customers before engaging them, fundamentally changing their go-to-market efficiency.
- ML-Ready Infrastructure: The clean, unified data foundation enables Moss to run sophisticated machine learning models for Lifetime Customer Value (LCV) prediction and monthly credit card spend forecasting.
- Strengthened Signal Coverage: Moss now monitors comprehensive signals across their entire target account universe, ensuring no high-value opportunity goes unnoticed.
"To accelerate this, we partnered with Revenanas on an intensive four-week RevOps sprint to upgrade our lead data infrastructure. Among other things, Revenanas built advanced Clay workflows to clean and unify data across our tools, strengthening coverage and enabling higher-confidence signals across our target accounts."
— Max Feider, VP of Revenue Operations and Strategy, Moss
