{PODCAST} In-Ear Insights: Simple and Complex AI in Marketing

{PODCAST} In-Ear Insights: Simple and Complex AI in Marketing

In this episode, Katie and Chris discuss the different levels of complexity when it comes to AI and machine learning in marketing. What’s the difference between AI and data science, and when should you focus on one or the other? What cautionary tales should marketers understand before positioning a product as powered by AI? Tune […]

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{PODCAST} In-Ear Insights: AI Applications and Distractions in Marketing

{PODCAST} In-Ear Insights: AI Applications and Distractions in Marketing

In this week’s In-Ear Insights, Katie and Chris talk about AI applications and distractions. How important is it that you know the technology? What level of depth is necessary for marketers to make marketing technology work for them? Tune in to find out! Watch the video here: Can’t see anything? Watch it on YouTube here. […]

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{PODCAST} In-Ear Insights: Advances in AI Natural Language Generation and Marketing Implications

{PODCAST} In-Ear Insights: Advances in AI Natural Language Generation and Marketing Implications

In this week’s In-Ear Insights, Katie and Chris talk about the newest advances in natural language generation and walk through an example of what’s available now for creating content with the assistance of AI. Watch the demonstration, listen to the implications for marketers, and start formulating your AI-based content marketing strategy. Tune in to find […]

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{PODCAST} In-Ear Insights: Google MUM, SEO, and Content Strategy

{PODCAST} In-Ear Insights: Google MUM, SEO, and Content Strategy

In this episode of In-Ear Insights, Katie and Chris dig into the announcements around Google’s newly announced AI model for advanced search, MUM, or the multitask unified model. Learn what MUM is, why it should matter to content producers, and how to think about adapting your content strategy to deal with what could be a […]

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{PODCAST} In-Ear Insights: Shiny Object Syndrome and Tech Arrogance

{PODCAST} In-Ear Insights: Shiny Object Syndrome and Tech Arrogance

In this week’s In-Ear Insights, Katie and Chris discuss shiny object syndrome, blind spots in your marketing technology (especially around AI and machine learning) and how arrogance can lead to substantial technical problems in your tech stack and company culture. How can you avoid pitfalls and blind spots? How do you manage AI and machine […]

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{PODCAST} In-Ear Insights: Why Consumer Recommendation Engines Fail

{PODCAST} In-Ear Insights: Why Consumer Recommendation Engines Fail

In this episode of In-Ear Insights, Chris and special guest John Wall discuss the state of consumer recommendation engines. Why are recommendations so narrow and ineffective many times? What could we do to improve them beyond what we get now? Listen in as we discuss limitations of computational power, algorithm choice, and more. Watch the […]

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{PODCAST} In-Ear Insights: When Algorithm Choices Go Wrong

{PODCAST} In-Ear Insights: When Algorithm Choices Go Wrong

In this week’s In-Ear Insights, Katie and Chris discuss what happens when junior or naive AI engineers or data scientists make bad choices for algorithms. Using an example from a writing analysis website, we discuss what went wrong, what an appropriate choice should have been, and why it’s likely things went sideways. Most important, we […]

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{PODCAST} In-Ear Insights: The Sorry State of Advertising

{PODCAST} In-Ear Insights: The Sorry State of Advertising

In this episode of In-Ear Insights, Katie and Chris tackle the sorry state of digital advertising, and advertising in general. Why is advertising so terrible? Are companies and marketers focused on the wrong metrics? What are we doing with the data we collect, and could we be doing something different and better with it? Find […]

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{PODCAST} In-Ear Insights: Should AI Adopt a Clinical Trials Process?

{PODCAST} In-Ear Insights: Should AI Adopt a Clinical Trials Process?

In this week’s In-Ear Insights, Katie and Chris discuss the current state of AI deployment. Companies are rushing ahead to put models and algorithms into action with little to no due diligence, and the consequences can be disastrous. Should AI adopt a practice similar to clinical trials, where a model must prove that it causes […]

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