At Blotout, our CRM sits at the heart of how we manage B2B relationships across multiple divisions and embedded partnerships. It serves as a central repository for all customer interactions—powering partner introductions, cross-selling, and pipeline growth.
As we scaled, inefficiencies in data management became clear. Manual processes, incomplete records, and poor lead validation slowed us down. We needed an automated solution that could clean, enrich, and validate CRM data—while saving time and supporting hyper-segmented outreach.
This case study shares our methodology, implementation, and measurable outcomes from deploying AI-powered automation across our CRM.
Business Context and Challenges
Our CRM contains three distinct data segments:
- 1P Data (Blotout Native Customers) – core base for retention and expansion.
- 2P Data (Embedded Partner Customers) – acquired via strategic partnerships; high cross-sell potential.
- 3P Data (Prospects) – target accounts requiring ongoing validation and enrichment.
Identified Pain Points
- Data Completeness: 62% of 1P/2P records lacked firmographic data (revenue, headcount, funding).
- Lead Quality: 28% of 3P contacts were invalid or outdated.
- Operational Efficiency: Sales teams spent 15–20 hours per week on manual cleanup.
- Hyper-Segmentation Needs: Outreach required nuanced targeting by ICP (direct, partner, or outbound).
Why We Needed an In-House Solution
While tools like CrossBeam or AISDR exist, they are expensive and partial solutions. By building in-house, we achieved:
- Cost savings compared to agency-led data ops
- Control and customization to fit our business model
- Shared learning with partners in our ecosystem
Solution Architecture
1P/2P Data Enrichment Framework
Data Sources:
- Customers.ai → firmographics (revenue, funding, headcount)
- Seamless.ai → commerce indicators
- Internal CRM data
Implementation:
- Automated n8n workflows for API integrations
- Custom validation rules for consistency
- Automated field updates in CRM
3P Data Validation System
Process Flow:
- Contact import
- Multi-point validation (email, phone, domain)
- Automated enrichment (job titles, seniority)
- Deduplication & merging
- Final scoring and routing
Business Impact and ROI
Quantitative Outcomes
1P/2P Data:
- More identified expansion opportunities
- Higher cross-sell conversion rates
- Faster account research
3P Data:
- Lower bounce rates
- Improved lead-to-meeting conversions
- Higher sales productivity
Operational:
- 60 hours/month saved on manual cleanup
- Significant reduction in manual entry
Strategic Advantages
- Customization: Proprietary logic aligned with our CRM
- Real-time Processing: Instant updates
- Scalability: Handled 300% volume growth seamlessly
- Cost Efficiency: 40% lower TCO than commercial alternatives
Future Roadmap
- Predictive Analytics Layer
- AI lead scoring
- Churn risk indicators
- AI lead scoring
- Enhanced Enrichment
- Technographic data
- Intent signals
- Technographic data
- Expanded Automation
- Campaign triggers
- AI contact recommendations
- Campaign triggers
Conclusion and Learnings
By automating data validation, enrichment, and cleanup, our CRM shifted from being a maintenance burden to a strategic growth engine.
This initiative not only improved productivity but also laid the foundation for SMBLead.ai, our standalone AI-driven CRM product. By sharing code and learnings between our internal system and SMBLead.ai, we accelerate innovation for both our team and our partners.
FAQs: CRM AI Automation
Q1: Why build in-house instead of using tools like CrossBeam?
Cost, flexibility, and the ability to share learnings with partners made in-house automation a better fit.
Q2: What’s the biggest operational win?
60+ hours/month saved from manual data cleanup, freeing sales teams for higher-value activities.
Q3: How does this scale with business growth?
Our architecture handles 3× data volume without requiring new resources.