Today, we’re excited to announce that our latest Command R model family is now available in Amazon Bedrock. We look forward to continuing our partnership with AWS to provide enterprise customers best-in-class AI solutions for their business needs to unlock powerful productivity and efficiency gains.
At Blotout, which runs multiple divisions and embedded partnerships, our internal CRM system on how we reach to our B2B customers serves as a central repository for all customer interactions for partner introductions, and cross selling. As our business scaled, we identified critical inefficiencies in our data management processes that necessitated an automated solution. This case study outlines our methodology, implementation, and measurable outcomes from deploying an automated data validation, cleanup, and enrichment system across our CRM segments.
Our CRM architecture comprises three distinct data segments:
As we scaled our different product lines, we needed to hyper personalize companies that were a fit with our direct customer base, or partner base, or outbound but with a different core ICP within the customer organization. We felt going inhouse would save us a ton of agency cost and learn how we can scale partners in our ecosystem and our software in partner ecosystems while we grow the overall pool. We are also sharing our CRM automation approaches with partners, unlike tools like CrossBeam or AISDR that are expensive and only solve parts of the problem.
Data Sources:
Technical Implementation:
3P Data Validation System
Process Flow:
1P/2P Segment:
3P Segment:
Operational Efficiency:
1. Predictive Analytics Layer
2. Enhanced Enrichment
3. Process Expansion
Our CRM automation initiative has transformed raw data into a strategic asset, driving measurable improvements across sales productivity, marketing efficiency, and revenue growth. Our internal solution’s flexibility ensures continued value as our business evolves.
Our learnings eventually drove us to understand the complexities of CRM, that eventually enabled us to understand the basis for simplifying internal CRM systems in the AI era where AI can hyper personalize using tools like n8n but the core of the CRM needs to be hyper simplified and connected to traffic drivers. These learnings informed our separate product smblead.ai, which we’re moving from Alpha to Beta and the codebase between our own sales CRM and SMBLead.ai is shared.
In our next post, we’ll explore “The AI-Powered CRM: How We’re Scaling 1:1 Personalization for Every Customer”