Episode 4: Why dirty customer data is killing growth
Customer advocacy and data expert Irwin Hipsman explores the hidden impact of poor customer data—and why dirty databases are quietly hurting go-to-market strategies, customer engagement, AI initiatives, and revenue growth.
Drawing from years of experience in customer marketing and database strategy, the conversation explores why data quality is no longer just an operational issue. From failed cross-sell opportunities to delayed renewals and unreliable AI outputs, Irwin explains how inaccurate customer data creates measurable business risk across B2B organizations.
The episode also dives into practical ways to improve database health, the importance of data governance, and the idea of "minimum viable data quality"—a more realistic approach to building scalable customer data systems.
During this episode, we'll answer:
- Why is dirty customer data a revenue problem, not just a technical one?
- How does poor data quality affect AI initiatives and go-to-market strategies?
- What makes customer data actually usable for decision-making?
- What is "minimum viable data quality" and why does it matter?
- How can segmentation improve customer engagement and advocacy performance?