Pumpkin Spice Data Analytics, 2021 Edition

Pumpkin Spice Data Analytics, 2021 Edition

It’s pumpkin spice season again, and nothing says pumpkin spice like some pumpkin spice-flavored data analysis. Let’s dig into the flavor of fall and see what’s new this year. Pumpkin Spice Search Interest First, let’s look at the trend itself. Using a basket of search terms such as pumpkin spice, pumpkin spice latte, pumpkin spice […]

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Case Study: Using Influencer Identification Data

Case Study: Using Influencer Identification Data to Build Audience

One of the challenges of marketing data is putting it to use. It’s fine to run reports and provide analysis, but if you don’t make decisions with your data, if you don’t change what you’re doing, then the data is of limited value. What does it look like when we put data to immediate, practical […]

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{PODCAST} In-Ear Insights: Behind The Scenes Instagram Data

{PODCAST} In-Ear Insights: Behind The Scenes Instagram Data

In this week’s episode, Katie and Chris preview the rough draft of an upcoming talk for the Agorapulse Instagram summit. Go behind the scenes on the talk, the research that went into it, and how we take data and try to make a coherent story about it. Please note the data shown in the episode […]

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So What? The Marketing Analytics and Insights Show

So What? How do I clean and prep my data for analysis – part 3

So What? Marketing Analytics and Insights Live airs every Thursday at 1 pm EST. You can watch on Facebook Live or YouTube Live. Be sure to subscribe and follow so you never miss an episode!   In this week’s episode of So What? we focus on bringing the data set into machine learning software. We walk through more […]

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So What? The Marketing Analytics and Insights Show

So What? How do I clean and prep my data for analysis – part 2

So What? Marketing Analytics and Insights Live airs every Thursday at 1 pm EST. You can watch on Facebook Live or YouTube Live. Be sure to subscribe and follow so you never miss an episode!   In this week’s episode of So What? we focus on prepping your data for analysis. We walk through pulling your planning from […]

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{PODCAST} In-Ear Insights: What to Do When Data Doesn't Have Answers

{PODCAST} In-Ear Insights: What to Do When Data Doesn’t Have Answers

In this week’s In-Ear Insights, Katie and Chris tackle an unusual situation. What happens when data doesn’t have answers? What happens when data simply doesn’t provide any useful insights beyond confirmation of what you already knew? We review what your options are, and what might have gone wrong that led to the situation. If you’ve […]

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{PODCAST} In-Ear Insights: Avoiding Data-Driven Marketing Traps

{PODCAST} In-Ear Insights: Avoiding Data-Driven Marketing Traps

In this episode of In-Ear Insights, Katie and Chris dig into this key question: is there such a thing as being too data-driven? “I’m not sure if you and I have discussed this in the past but it’s been on my mind a lot lately, and I’m not sure if there’s formal thought around this: […]

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Business Analytics 101: How To Monitor Business Trends

Business Analytics 101: How To Monitor Business Trends

In the weeks and months to come, expect significant challenges to business, B2B and B2C. Companies with a solid business analytics strategy and great data sources will have a significant advantage over companies without these assets. How can business analytics help? What do you, a marketer and business analyst, need to know? To answer this […]

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Pumpkin Spice Data Analytics, 2020 Edition

Pumpkin Spice Data Analytics, 2020 Edition

Last year, we examined the wonder that is pumpkin spice, to learn what foods contained actual pumpkin spices. It’s been a year since then; what’s changed, if anything? Market Interest First, before we dig in, let’s look at pumpkin spice itself. The topic of pumpkin spice (or more accurately, pumpkin pie spice), is a perennial […]

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{PODCAST} In-Ear Insights: When Algorithm Choices Go Wrong

{PODCAST} In-Ear Insights: When Algorithm Choices Go Wrong

In this week’s In-Ear Insights, Katie and Chris discuss what happens when junior or naive AI engineers or data scientists make bad choices for algorithms. Using an example from a writing analysis website, we discuss what went wrong, what an appropriate choice should have been, and why it’s likely things went sideways. Most important, we […]

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