AI is rapidly shaping how brands understand consumers, but what happens when you feed those models stale, fragmented data?

On this episode of Inside the Stack, AdExchanger's Lynne d Johnson sits down with Matt Tipperreiter, VP of Product Strategy at Experian, and John Oronte, Director of Partnership Growth at AtData, to talk about the flaws hiding in modern consumer intelligence.

Marketers have spent the last five years obsessed with hashed emails. But according to Oronte, a lot of those addresses aren't what they seem. When AtData looked at one client's verified new-to-file emails, almost 14% were burners set to expire within a month. On another platform, 15 to 20% of the records hadn't been used in six months. Brands are tying identity to stale emails, and AI is making decisions based on them.

Tipperreiter and Oronte also get into data freshness across identity graphs, transparency, and whether you should own or rent your identity graph. And they explain identity to a five-year-old, with Tipperreiter turning his daughter's magnet tiles into the metaphor.

This episode is brought to you by AtData, a part of Experian. AtData helps businesses unlock intelligence about the person behind the email address with email validation, data enrichment, identity matching, and fraud protection.

AI can build a marketing mix model in days, not months. But can you trust what it tells you?

In this episode of AdExchanger's Inside the Stack, Madan Bharadwaj, founder and CEO of M-Squared, explains why faster modeling makes human judgment more important. With 20 years in the attribution space, Bharadwaj walks through why no single method is the truth and why causal attribution means triangulating MMM and incrementality testing. He also argues that a go-to-market thesis tells you whether a model is credible, and that a marketing accounting framework gives marketing and finance a shared language for outcomes like contribution margin.

And then there's the story of how a beauty brand's AI-built model recommended cutting $30 million from its Amazon ad spend, and how SKU-level modeling led to a very different investment thesis and 5–7% top-line growth. Plus: why he thinks the future of measurement is super cognition.

Supply path optimization (SPO) has become industry shorthand for aggressive fee-cutting and stripping out intermediaries, but according to Tom Wolfe, Senior Vice President of Programmatic Strategy and Partnerships at Locality, that's only half the equation .

In this episode of AdExchanger's Inside the Stack, Wolfe explains why a shorter path is better—and even better when premium inventory sits on the other side. While reducing the tech tax matters, a hyper-focus on cutting hops often ignores signal degradation and data loss across fragmented systems . Wolfe argues that buyers need to pair SPO with "data path optimization" to ensure rich signals survive the trip and actually reach a destination worth going .

Key topics covered in this episode include:

• Why local advertising requires a bottom-up strategy, and how consumer behavior varies drastically between the dense, brick-lined streets of Philadelphia and the dirt roads of Dothan, Alabama .

• The hidden costs of fragmented supply, including data loss and signal degradation as information moves through multiple systems .

• Why AI's true competitive advantage lies in the quality of data feeding the models, comparing today's AI hype to everyone suddenly gaining access to Microsoft Excel in the 90s .

• Why Wolfe says the current state of the converging TV ecosystem is about maturing, recovering, and collaboration.