Summary In today's episode, I explain Will Reynolds’ warning that high AI visibility does not guarantee credibility or recommendation, using vacation rentals and synthetic visibility reports as examples. Here's what this means for you. You need to examine context, sentiment, and recommendations instead of treating brand mentions as proof of success. You'll also learn these…
Summary In today's episode, I explain how digital publishers can measure whether AI bot traffic is actually delivering commercial value. Here's what this means for you. You get a practical framework for separating real human results from inflated machine numbers, so you can make confident decisions about blocking, charging, or welcoming AI crawlers. You'll also…
Summary In today's episode, I break down how to analyze massive multi-year business reports with AI without falling victim to context window limitations or hallucinations. Here's what this means for you. You'll discover a practical architecture combining vectorized databases, knowledge graphs, and deterministic Python code that delivers reliable enterprise-scale analysis. You'll also learn these concepts:…
Summary In today's episode, I break down why AI cannot solve marketing attribution and what marketers should do instead. Here's what this means for you. You gain a clear path to genuine customer insight that no analytics dashboard can deliver. You'll also learn these concepts: how cookie deprecation and bot traffic have gutted the data…
Summary In today's episode, I tackle whether traditional A/B testing becomes obsolete in an era of AI-generated hyper-personalized landing pages. Here's what this means for you. You'll discover that effective evaluation still requires guardrails, consistency in offers, and robust tracking of what visitors actually see. You'll also learn these concepts: why business constraints prevent truly…
Summary In today's episode, I break down why traditional podcast analytics miss the mark and how to measure the entire content ecosystem around your show. Here's what this means for you. You gain a framework for tracking performance across every platform your podcast touches so you can stop making decisions based on incomplete download data…
“Do we really trust Chinese models that much?”” Lisa asked that question in the Trust Insights Analytics for Marketers Slack group and the short answer is, “we really trust models under our control that much”, which means open weights models, models we download and run on our own hardware or in trusted inference providers. Here’s…
