RevOps leaders know the frustration: your CRM is filled with thousands of accounts showing revenue estimates that are wildly off-base, leading to misaligned account scoring and wasted prospecting efforts. The problem stems from static databases that aggregate stale information and resell the same inaccurate data to everyone. Kernel breaks this cycle with a custom revenue estimation approach that maps corporate hierarchies accurately, pulls from multiple live data sources beyond simplistic revenue-per-employee formulas, and stays current with regular refreshes from annual reports and market announcements. Unlike black-box solutions, Kernel shows you exactly how each estimate is calculated and improves with feedback from RevOps and sales teams.
See LinkedIn post →Enterprise RevOps teams understand that headcount drives everything from market segmentation to territory planning, but generic enrichment tools are systematically getting it wrong. Most platforms rely solely on LinkedIn data and assume it represents total employee count, creating massive blind spots like counting every Uber driver as an Uber employee. The result is small accounts getting enterprise treatment while real enterprise prospects get ignored, and territory assignments become meaningless. Kernel fixes headcount estimates by addressing four critical failure points: entity confusion, where tools measure subsidiaries instead of parent companies, LinkedIn bias that misses entire industries, temporal lag where current data is actually 12-18 months behind reality, and primary source neglect that ignores verified numbers from annual reports and regulatory filings.
See LinkedIn post →The hidden efficiency killer in your CRM: generic vertical classifications like "Professional Services" that lump together everything from Accenture to local law firms, making 90% of your account lists irrelevant for outbound sales. These broad categories force sales teams to waste time sifting through poorly matched prospects instead of focusing on accounts that actually fit your GTM strategy. Kernel solves this by building custom vertical schemas unique to your business, transforming vague labels like "Professional Services" into precise, actionable segments like "Corporate Law." The result is outbound sales teams aligned with your actual GTM strategy and account lists where prospects are genuinely qualified fits.
See LinkedIn post →Strategic account selling breaks down when reps waste weeks researching complex webs of sub-brands and parent-child relationships, often prospecting into accounts with zero buying power. Corporate hierarchy mapping is the most overlooked efficiency driver in strategic sales, yet most RevOps teams leave reps to navigate these relationships manually. Kernel builds and maps corporate hierarchies for every account in your CRM, customized to your specific rules of engagement and ICP criteria. Whether you only want to sell to companies owned by LVMH or IBM subsidiaries that have actual buying power, Kernel identifies the precise buying center from day one so sales can focus their attention on the right accounts without wasted research or prospecting.
See LinkedIn post →Businesses rushing AI deployment with low-quality master data are creating mass hallucinations in their CRMs, where critical data points get fabricated with plausible but completely incorrect information. This turns the entire CRM into a house of mirrors where RevOps teams can't trust whether accounts or insights are real, as false certainty and compounding errors reinforce bad patterns. Kernel prevents AI hallucinations through a five-step framework built on proprietary master data of corporate entities: master data foundation, input validation, multi-step processing, structured output, and quality assurance checkpoints that ensure AI modules operate with enterprise-grade reliability.
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