In this week’s In-Ear Insights, Katie and Chris look at the upcoming release of hate crime data analysis and broader implications. When data collection is flawed or missing, how do you still make decisions? What do you do when you have to do something because lives are on the line, but you lack enough data? […]
Read More… from {PODCAST} In-Ear Insights: Data-Driven Decisions and Social Good
In this week’s In-Ear Insights, Katie and Chris discuss math. Specifically, what are the math skills that marketers of all stripes, but especially those who have an interest in marketing analytics, marketing data science, and machine learning/AI applied to marketing need to know. Do you need a Ph.D.? Do you need to spend a year […]
Read More… from {PODCAST} In-Ear Insights: Math Skills for Marketing Analytics
In this week’s In-Ear Insights, Katie and Chris discuss the importance of transparency and disclosure in research and data. These are troubled, uncertain times – not for the macro situation, but for our willingness to collectively accept statements of fact without verifying those facts or inspecting the methodology behind a piece of data. They also […]
Read More… from {PODCAST} In-Ear Insights: Transparency in Research and Data
Introduction The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this […]
Read More… from 5 Ways Your AI Projects Fail, After Action Reviews and Post-Mortems
In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss early warning systems. When looking at your analytics, what metrics constitute leading, realtime, or lagging indicators, and how should you think about using them to determine when trouble is brewing? Learn about what exponential math is, how it factors into trend detection, […]
Read More… from {PODCAST} In-Ear Insights: Early Warning Systems for Marketing Analytics
This past week, our CEO Katie Robbert said, “can we be done with the “something about me and tag five people” on twitter thing? it’s really annoying now“. If you’re unfamiliar with the reference, there’s a Twitter meme where you share 5 jobs you’ve held, and then tag 5 people: These tag memes at first […]
Read More… from The Importance of Tag Memes for Understanding Social Networks
In this episode of In-Ear Insights, Katie and Chris respond to a listener question: how valid are predictive analytics forecasts when you’re in the middle of massively unpredictable events? How do you deal with the anomalies of a black swan event, and how do you tell the difference between an anomaly and a breakout (continuing […]
Read More… from {PODCAST} In-Ear Insights: Predictive Analytics for Unpredictable Events
In this week’s In-Ear Insights, Katie and Chris answer a mailbag question from Denise: “My question is about UX copywriting. Other than using a service like “crystalknows.com,” how do I know which words and phrases influence a specific buyer persona? Which words or CTAs influence a 35-year-old CTO male who makes craft beers on the […]
Read More… from {PODCAST} In-Ear Insights: Data-Driven B2B Marketing Personas
In this episode of In-Ear Insights, you’ll enjoy the Data Science 101 session at Social Media Marketing World given by Chris. An off-the-books speaker meetup talk, this is the first time this content has been shown publicly, and due to A/V issues, the slides were hand draw and can be found below. Enjoy the workshop! […]
Read More… from {PODCAST} In-Ear Insights: Data Science 101 at #SMMW20
In this episode of In-Ear Insights, Katie and Chris discuss whether or not you need data science skills to be an effective marketer. Find out the answer, plus why machines won’t be taking your job any time soon, as well as some helpful tips for growing your marketing analytics skills. Mentioned in the show: Hugging […]
Read More… from {PODCAST} In-Ear Insights: Should You Be a Data Scientist?
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