In this episode, Katie and Chris discuss personal branding considerations and challenge the notion of what a personal brand is. Do you have to be the loudest person in the room? How do you build a personal brand without being perceived as arrogant and full of yourself? Tune in to find out! Watch the video […]
Read More… from {PODCAST} In-Ear Insights: Personal Brand Building Considerations
Analytics is inherently rearward-looking, looking at what happened. Yet if we want to increase the value and impact of analytics, we have to look forward, to help plan. How do we do this? In this episode, learn 3 different approaches for improving the value of analytics. Watch the video here: Can’t see anything? Watch it […]
Read More… from {PODCAST} In-Ear Insights: Increasing the Value of Analytics
This data was originally featured in the January 13, 2021 newsletter found here: https://www.trustinsights.ai/blog/2021/01/in-the-headlights-january-13-2021-marketing-in-crisis-podcasting/ In this week’s Rear View Mirror, we fielded a question about the overall podcasting landscape from our Slack group, Analytics for Marketers, so we decided to dig deep into podcasting since everyone and their cousin seems to have one these days. […]
In this week’s In-Ear Insights, Katie and Chris discuss the value of professional certifications. What are certifications like Google Analytics IQ, Hubspot Academy, Udemy, Coursera, and LinkedIn Skill Tests worth? Do they help you get hired? What do hiring managers think of them? Tune in to find out! Watch the video here: Can’t see anything? […]
Read More… from {PODCAST} In-Ear Insights: The Value of Professional Certifications
In this week’s In-Ear Insights, Katie and Chris talk through some of the latest data in marketing technology from Scott Brinker’s MarTech 9000. How much marketing technology is too much? Do we really need hundreds of options for CRM software? What lessons should we take from all this data. Tune in to find out! Watch […]
Read More… from {PODCAST} In-Ear Insights: How Much Marketing Technology is Too Much?
In this week’s In-Ear Insights, Katie and Chris discuss what to do when your metrics and KPIs go the wrong way. How do you diagnose your marketing operations and prioritize what to fix? What things are a waste of time? Tune in to find out! Watch the video here: Can’t see anything? Watch it on […]
Read More… from {PODCAST} In-Ear Insights: When Metrics Go The Wrong Way
In this week’s In-Ear Insights, Katie and Chris review the basics of what server-side tagging is, why it’s important, and how it works. Using Google Tag Manager’s server-side edition as the example, we look at why companies like Facebook are pushing it so much, and what benefits – and hurdles – server-side tagging presents. Tune […]
Read More… from {PODCAST} In-Ear Insights: Understanding Server-Side Tagging
In this week’s episode, Katie and Chris discuss recommendation engines. These seemingly straightforward pieces of technology control enormous aspects of our daily lives, from what we buy to what news we’re exposed to. How should your marketing strategy incorporate recommendation engines, and how do you know when recommendation engines are at work? Tune in to […]
Read More… from {PODCAST} In-Ear Insights: The Power and Danger of Recommendation Engines
In this week’s In-Ear Insights, Katie and Chris discuss how artificial intelligence impacts corporate culture and vice versa. Learn about the two different types of organizations, use cases for AI in corporate management, and the hidden danger of AI and institutional knowledge in your corporation. Watch the video here: Can’t see anything? Watch it on […]
Read More… from {PODCAST} In-Ear Insights: Artificial Intelligence and Corporate Culture
In this week’s In-Ear Insights, Katie and Chris discuss whether AI and machine learning will imperil the career of the data scientist. What is data science, and how much of it can be automated and handled by machines? Tune in to find out. Watch the video here: Can’t see anything? Watch it on YouTube here. […]
Read More… from {PODCAST} In-Ear Insights: Will AI Wipe Out Data Science Jobs?
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