Summary In today's episode, I walk through the best tools for cleaning data and why the right choice depends on your data type, what you're trying to fix, and the scale of the problem. Here's what this means for you. You'll see why human judgment and process matter far more than any specific software tool.…
I had a chance to sit down with Jason Falls to chat about analytics, data science, and AI. Catch up with us over 35 minutes as we talk about what goes wrong with influencer marketing, why marketers should be cautious with AI, and the top mistake everyone makes with Google Analytics. Can’t see anything? Watch…
Summary In today's episode, I sit down with analytics expert Christopher Penn to unpack how marketers can cut through data noise and measure what truly drives business results. Here's what this means for you. You'll discover a practical framework for turning analytics, AI, and influencer programs into clear revenue-driving decisions instead of vanity dashboards. You'll…
Allison asks, “When it comes to reporting – how have you successfully educated others in your company on results? Is a simple glossary enough or have you taken another approach, especially with our less tech-savvy peeps?” The key is identifying what they care about from a measurement perspective. We often make the mistake that what…
Summary In today's episode, I walk through a year-over-year analysis of FBI hate crime data comparing 2017 to 2018 and reveal what the numbers actually say versus what they appear to say. Here's what this means for you. You'll see how a metric can look great on paper while the underlying data quality quietly collapses…
Summary In today's episode, I walk through a data analysis of brand responses to the Black Lives Matter movement by mining Instagram hashtags and separating vocal brands from silent ones. Here's what this means for you. You'll gain a practical framework for using social listening data to hold companies accountable for their stated values and…
Summary In today's episode, I break down how the scientific method applies to data science and why the problem definition phase matters far more than most people realize. Here's what this means for you. You'll learn why rushing past exploration and straight to a hypothesis almost always leads to flawed experiments and wasted effort. You'll…
Alessandra asks, “The customer is getting leads organically in the same number as before the start of the ads campaign, so the few conversions (1 month running) makes him think it’s not worthwhile to advertise. My thoughts are going in the direction of explaining the value of impressions on their own, the values of clicks…
Summary In today's episode, I tackle a follow-up question about how to advise a customer who doubts the value of their paid ad campaign because organic leads haven't changed. Here's what this means for you. You'll see why true channel comparison demands identical metrics and proper tracking rather than vanity numbers like impressions. You'll also…
David asks, “I’m working on a Twitter performance report. I’m trying to determine the best metric for demonstrating our performance (assuming the thing that matters for the moment is engagement, and not necessarily conversions). My feeling is that engagement rate could potentially be the most valuable – albeit imperfect – metric, since absolute numbers like…
