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?
Based on a question from our analytics community, in this episode of In-Ear Insights, Katie and Chris tackle the challenges of new technology. When a new piece of technology makes a big splash, how do we evaluate it? How should we assess whether it’s right for us, whether it makes sense to pursue it? What […]
Read More… from {PODCAST} In-Ear Insights: Evaluating New Technologies
On June 18, 2020, I sat down with Jeff Coyle and the MarketMuse community to answer questions after our webinar together on Natural Language Processing and its application to content marketing. Let’s see what’s on the minds of the content marketing community. Tatiana asks, “I’ll kick things off with my question – do you have […]
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, 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
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, Part 5: Deployment-Related AI Failures
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, Part 4: Modeling-Related AI Failures
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, Part 3: Data-Related AI Failures
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