In the rapidly evolving landscape of Go-to-Market (GTM) strategies, the quality of underlying data is paramount. Jordan Hamilton, Head of Marketing at Sumble, and Camela Thompson, Head of RevOps at Funware, explain that scaling GTM efforts with stale or fragmented data leads to confidently wrong AI agents and wasted resources.
They delve into the concept of "data rot," where information quickly becomes irrelevant due to constant changes in organizations and job titles, and how an overabundance of signals can cause sales teams to ignore valuable insights. These Big Ideas are drawn from Hamilton and Thompson's interview with Matthew Volm on RevOps Co-op.
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Understanding data rot. Jordan Hamilton defines 'data rot' as the rapid decay of data accuracy due to frequent job changes, the ebb and flow of technologies within companies, and the rise of vague job titles. This makes it challenging to ensure outreach is directed at the right person, at the right time, with the right message.
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Data freshness is a top concern. A poll during the discussion revealed that data freshness, particularly for contact information, is the leading concern for GTM teams. Jordan Hamilton noted that this is a common problem customers seek help with, underscoring the dynamic nature of organizational changes and job titles.
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Scaling bad data amplifies errors. Jordan Hamilton explains that scaling outreach without a complete and accurate understanding of customer context leads GTM agents to operate with misplaced confidence. This problem 'proliferates at scale,' meaning increased automation only magnifies negative impacts and wasted resources.
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The three eras of GTM data. Jordan Hamilton outlines three eras of GTM data: brute-force calling, workflow-driven scoring, and the emerging third era focused on foundational accuracy. He argues that while workflows improved data movement and scoring, they haven't addressed the core issue of data accuracy and contextual relevance.
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Redefining ICP from first principles. Jordan Hamilton notes that ICPs are often based on generic, readily available data like job title or company size. He suggests that modern capabilities allow organizations to define ICPs from first principles, focusing on specific problems they solve, such as 'companies hiring people in Seattle moving from Airflow to Databricks,' rather than being limited by existing data fields.
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Signal overload leads to ignored data. Jordan Hamilton observes that when 'everything gets a score'—including lead, account, and engagement scores—the sheer volume causes sales teams to disregard them. He suggests that if every signal is deemed important, none truly stand out, leading to a lack of actionable insight.
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Engaging loud objectors in change management. Camela Thompson advises RevOps professionals to identify 'loudest objectors' on the sales team and involve them in requirements gathering. By making them initial testers, these individuals, who tend to 'gossip,' will become the loudest champions if the new system proves effective, positively influencing their peers.