Category: AI

  • The Predictive Analytics Process: Plan

    In the last post, we examined the output of an S-ARIMA-based prediction. Let’s now dig into the most important part of predictive analytics: planning and acting. The Power of Predictive Analytics The power of predictive analytics is our ability to forecast with greater accuracy and specificity than generalized, “gut instinct” predictions. We know when something…

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  • The Predictive Analytics Process: Predicting

    In the last post, we examined different methods for identifying which variable to predict. Once we’ve made a sound, data-driven assessment of what variables matter most to us, we build a predictive model around it. Predicting Ahead To create accurate forecasts, we must use software built for the explicit purpose of time-series prediction. The generally-accepted…

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  • The Predictive Analytics Process: Picking Variables

    In the last post, we examined different ways to prepare data to counteract known, common problems. Let’s turn our eye towards picking which data to predict. Picking Variables Picking a variable to predict is a blend of both human insight and machine analysis. The best comparison I know is that of a GPS app. We…

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  • The Predictive Analytics Process: Preparing Data

    In the last post, we examined the basics of extracting data from various data stores and the different types of datasets we have access to. Let’s now look at the process of preparing data. Three Data Challenges In the preparation of our data, we typically face three challenges: Missing data Corrupted data Irrelevant data To…

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  • The Predictive Analytics Process: Pulling Data

    In the last post, we looked at the consequences of having poor process in our predictive analytics practice. Let’s look at the first step of that process now. Pull If data is the new oil, pulling data is analogous to drilling and extracting oil from the ground. We need to identify what data sources we…

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  • The Predictive Analytics Process: Introduction

    The Predictive Analytics Process: Introduction While we understand the value of predictive analytics, the ability to see into the future with specificity and precision, we are often unclear on the process to develop predictions. As a result, our predictive analytics outputs are often incomplete, lacking context, or difficult to understand. Introduction: Where Predictive Analytics Goes…

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  • Cognitive Marketing: How AI Will Change Marketing Forever

    I had the pleasure and privilege of delivering the opening keynote at MarketingProfs B2B Forum. This year’s keynote is titled Cognitive Marketing: How AI Will Change Marketing Forever. For those who would like to see the slides, they are below. In addition, if you’d like a deeper dive into the type, I invite you to…

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  • AI for Marketers: An Introduction and Primer

    We’ve read about AI and marketing for years now. We’ve heard the promises of AI and how it changes marketing for the better, makes us more efficient, helps us unlock vast potential trapped in our data. Yet, marketers still remain confused by AI and machine learning. What is it, really? How does it work? What…

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  • Why Marketing Needs AI

    Why must we discuss AI in marketing? What’s so wrong with marketing today that we need the incredible powers of artificial intelligence and machine learning to solve? You’ve likely heard the cliché, “fast, cheap, good. Choose any two.” The premise is that we can have two out of three of these attributes, but we can’t…

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  • Where Is Your Data Refinery?

    Marketers and business folks love the expression, “Data is the new oil”, and I find it apt. Like oil, data has incredible potential to change and transform business. The energy surplus of the last century was powered mainly by oil, in the sense that oil vastly amplified the amount of work our species does. There’s…

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