How data quality makes or breaks lead gen success

The basic mechanics of lead generation,forms, follow-ups, and funnels,have stayed largely the same for years. What has shifted dramatically is the volume, velocity, and complexity of the data that fuels those operations. Modern lead generation now depends on automated nurture sequences, AI-driven scoring models, and predictive analytics. That makes the quality of the underlying data more critical than ever before.

In a recent episode of Conversations with MarTech, Jason Gladu, chief operating officer at Convertr, argued that successful modern lead generation strategies must treat data quality as a precondition, not an afterthought. He warned that the costs of neglecting data hygiene are steep and include serious compliance risks,such as GDPR violations,as well as what he calls “media waste,” where sales teams burn expensive time chasing leads that don’t actually exist.

Gladu cautioned that businesses do not want to learn the hard way what happens when AI tools ingest unverified or synthetic data, or when companies target individuals without proper consent. The consequences can ripple across compliance, budget, and brand trust.

The episode breaks down several key topics:

1:04 – Meet Jason Gladu 1:46 – What has changed about lead generation over the last 15 to 20 years 4:21 – What a successful modern lead gen strategy looks like and what holds businesses back 8:47 – The risks and rewards of using AI in lead generation 13:12 – Where to start if you are unsure about your data quality