Summary In today's episode, we break down the latest features of Google's Notebook LM and share practical use cases for marketers, analysts, and content creators. Here's what this means for you. You'll see how this source-restricted AI tool dramatically reduces hallucinations and makes qualitative analysis of customer feedback, interviews, and large document sets far more…
Summary In today's episode, I explain how to determine necessary website traffic levels for meaningful data analysis. Here's what this means for you. You can avoid wasting resources on ineffective tests by working with your current audience's actual behavior. You'll also learn these concepts: how the type of insight dictates traffic needs, how to use…
Today’s was a special day for me and for the Trust Insights team. After literally years of discussion and ideation, today is our first event, the Generative AI for B2B Marketers workshop in London. We sold out weeks ago – 25 people for a full day workshop to teach AI. Why here and now? The…
What blocks AI progress? Over the last two days at the Marketing AI Conference (MAICON), I’ve had the pleasure of sitting down with colleagues and peers to hear how they’re using AI. The most common blocker to AI progress at an individual level? Vocabulary. AI – generative AI, and large language models in particular –…
All automation sets the price for a commodity. In a recent discussion about whether or not AI-driven content will consume content marketing with Joe Pulizzi, Brian Piper, and others, I pointed out one thing: all automation sets the price for a commodity, what the market is willing to pay. Let’s look back to agriculture. Automation…
Summary In today's episode, I explain why ad value equivalence serves as a flawed measurement and how you can transition to more effective strategies. Here's what this means for you. You will learn to capture high-quality first-party data that proves your actual marketing impact through behavioral change. You'll also learn these concepts: why ad value…
Summary In today's episode, I walk through eight real-world data horror stories from marketers and map each one to the Six C's Data Quality Framework (clean, complete, comprehensive, calculable, chosen, and credible). Here's what this means for you. You walk away with a horror-story-tested approach to spotting data quality failures plus a workflow that audits…
