Category: Data Science
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Stop Hating Vanity Metrics in Marketing Analytics
Without fail at nearly every marketing conference, someone rails against vanity metrics. Stop measuring them. They don’t matter. They’re the devil. Variations on those themes. So let’s clear the air a bit, because just as some people put too much importance and faith in vanity metrics, other people discount them too much. What Are Vanity…
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Marketing Data Science: Introduction to Data Blending
One of the truths we don’t talk about nearly enough in marketing data science is the need to blend data. There are very few data sources that are a single source of truth by themselves, that need no augmentation or supplementation to provide maximum value. Even our most favorite, most used tools like Google Analytics,…
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Why Do I Love Data So Much?
Kate asks, “Why do you have such a fixation on data?” This is a more interesting, philosophical question than it might first appear. Yes, I emphasize data a great deal in my work; my company, Trust Insights, is rooted in data-driven marketing. But where did this all come from? A significant part comes from my…
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Why AI Will Not Create Great Content Any Time Soon
I am bullish on AI creating content at scale. I am bearish on AI creating GREAT content at scale – or at all. Why? It comes down to limits of training data, fundamentally. All machine learning models, from the most basic to the most sophisticated, need something to learn from. In the case of language…
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What Personal Data Science Teaches Marketers
A few years ago, there was a big movement called Quantified Self, in which we would apply principles of analytics and data science to our lives with fitness trackers and other apps that would help quantify how we were living. For a variety of reasons – mainly overhead – that movement never really took off.…
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How To Determine Whether Something is a Trend
How do you know whether something is a trend or not? First, we need to define a trend. A trend is: a general direction in which something is developing or changing Second, we should mathematically define and be able to detect a trend. Trend analysis (and any kind of statistical analysis) is generally not something…
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Building Trusted AI Systems: A Fireside Chat with IBM
I recently had the opportunity to sit down with Lauren Frazier from IBM to discuss how we go about building trusted AI systems in a fireside chat livestream. We covered a ton of ground. Can’t see anything? Watch it on YouTube here. What is Fair? Fairness is a difficult subject to tackle, because people have…
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Learning Data Science Techniques With IBM Watson Studio Modeler
When it comes to learning data science, one of the challenges we face is just how seemingly daunting the field is to learn. There are so many techniques, tactics, and strategies that it’s difficult to know where to start. Learning something new always begins with an understanding of the basics. From martial arts to dance…
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You Ask, I Answer: Adopting Data-Driven Marketing Strategy?
Kait asks, “How do you suggest to go about to creating a data-driven strategy standard and breaking the “because we’ve always done it this way” mentality?” Strategy is always a sale. Every sale has to answer the question, “What’s in it for me?” for the buyer. Not what’s best for the company, for the organization,…