I often think of my grandfather, Max Feldman, a salesman of the 1960s—a man who would be well over 110 years old today. I have an advertisement that features him, proudly representing a long-distance telephone company. In the image, he’s greeted warmly by an office full of cheerful staff, coffee at the ready, all because he had the foresight to call ahead before showing up at his prospects’ offices. It’s a charming relic of an era when the right piece of timely information—like a phone number and a name—could transform a transaction from cold introduction into a moment of hospitality and promise.
But rewind to a century earlier, and such sophistication was scarcely imaginable. In that era, the Mercantile Agency—precursor to Dun & Bradstreet—was painstakingly verifying and cataloging company information, laying the groundwork for modern business intelligence. Even Abraham Lincoln had a hand in these efforts, helping to bring order and insight to an otherwise chaotic marketplace. Without such systematic intelligence, the traveling salesman of the time might have resembled Harold Hill from The Music Man: a figure propelled by confidence and charm but lacking factual underpinnings. By turning guesswork into a structured resource, these early data pioneers demonstrated that knowledge, not just showmanship, shapes how companies identify and approach their prospects.
By the late 20th century, another revolution arrived: email. This shift took data-driven outreach to unprecedented levels. Providers such as NetProspex (later absorbed by Dun & Bradstreet), ZoomInfo, and Jigsaw (acquired by Salesforce) compiled expansive email databases. Global, low-cost outreach became the norm. The global martech ecosystem embraced this abundance, turning “cold emailing” into a staple of sales and marketing. Volume became the watchword—more contacts, more emails, more campaigns.
But this strategy, while powerful, had its limits. Today, as we enter the era of artificial intelligence and machine learning, the requirements have evolved once again. Massive lists and stale addresses no longer suffice. AI models depend on accurate, current data to produce meaningful insights. Outdated or incomplete records can derail even the most sophisticated models. Moreover, businesses now seek to integrate multiple, often custom, data sources into their workflows, necessitating a stable, continuously updated foundation of company and contact data.
This is the challenge RevenueBase aims to meet. We focus on data quality and recency, providing a dependable backbone that supports advanced analytics, AI-driven recommendations, and seamless integrations. In a world inundated by information, we prioritize relevance and reliability, ensuring that organizations act on insights grounded in truth rather than guesswork.
And so we return to that photograph of my grandfather. He thrived in an era when a single, verified phone number could turn a cold encounter into a warm reception. Today, the stakes are higher and the data more complex, but the principle remains the same: those who wield the right information—accurate, current, and contextual—set themselves apart. As we journey from dusty ledgers and telephone directories to email lists and AI pipelines, the lesson my grandfather once embodied still holds: better data leads to better outcomes, and a better welcome awaits those who know exactly who they’re talking to.
