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. [podcastsponsor] Watch […]
Read More… from {PODCAST} In-Ear Insights: Why Consumer Recommendation Engines Fail
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 […]
Read More… from {PODCAST} In-Ear Insights: The Sorry State of Advertising
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 […]
Read More… from {PODCAST} In-Ear Insights: Should AI Adopt a Clinical Trials Process?
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
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
I remember years ago when I was talking with Mike Volpe and he said with a bit of frustration “Marketers are lazy.” This is a problem since training a machine learning model for any kind of marketing is a lot of labor. Of course, you have to keep in mind that Mike, at that time […]
Read More… from Training Your Machine Learning Model with Your “Day In The Life”
In this week’s In-Ear Insights, Katie is out keynoting an event in Europe, so Marketing Over Coffee host John Wall steps in to talk about account-based marketing (also known as target account marketing or key account marketing) and applications of AI to it. John and Chris talk about uses of machine learning for lead scoring, […]
Read More… from {PODCAST} In-Ear Insights: Account-Based Marketing (ABM) and AI
On October 25, 2019, Google changed everything again with its announcement that its deep learning powered natural language processing model, BERT, would now power search queries. With this announcement, machine learning expertise for SEO is no longer optional. Let’s dig into what this all means. What Is BERT? BERT is a massive language model that […]
Read More… from AI and SEO in the Post-BERT World: What Marketers Need to Know
In this episode of In-Ear Insights, Katie and Chris discuss what to do when you data sources off the beaten path. What if the data you have doesn’t answer the questions you have? Where do you go to get such data? Listen in as they explore Mastodon, cryptocurrencies, and so much more. [podcastsponsor] Listen to […]
Read More… from {PODCAST} In-Ear Insights: Nontraditional Data Sources
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