Predictive Analytics: How Marketers Can Improve Future Activities

Want your marketing to be more efficient?

Wondering how predicting your marketing cycles can help?

To explore how marketers can get started with predictive analytics, I interview Chris Penn.
More About This Show
The Social Media Marketing podcast is an on-demand talk radio show from Social Media Examiner. It's designed to help busy marketers, business owners, and creators discover what works with social media marketing.

In this episode, I interview Chris Penn, the co-founder and chief innovator at Brain+Trust Insights. He's also the co-host of the Marketing Over Coffee podcast and the lead analytics expert for Social Media Marketing World.

Chris explains how to ensure the quality of underlying data used in predictive analytics.

You'll also discover data sources and tools used to make predictions.

Share your feedback, read the show notes, and get the links mentioned in this episode below.
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Here are some of the things you'll discover in this show:
Predictive Analytics
Chris's Story

Chris got started in analytics through his background in IT. In 2003, he started working as IT director of a student loan startup, where his role expanded beyond traditional IT responsibilities. In addition to running the web and email servers, he also updated the websites and sent the weekly email.

Chris was doing this work before Google Analytics existed, so when the CEO of his company asked how the websites and emails were performing, Chris didn't have an answer. To figure it out, Chris and his team started developing their own tools to understand the basics, like how many people visited the website each day.

Over time, the analytics practice became a core focus for Chris. He was not only trying to learn what happened, but why it happened and how the business could respond.

Listen to the show to hear Chris discuss his educational background.

What Are Predictive Analytics?

Predictive analytics use statistics and machine learning to analyze data and make predictions. Humans are very predictable. We all follow routines, such as brushing our teeth and then taking a shower, or putting on each piece of clothing in a certain order each morning.

Because humans are predictable on both a micro and a macro scale, marketers can mostly predict what will happen. For instance, in North America, if you're a B2C marketer, you pretty much know that you're going to be busy from November 1 to December 26 because that's a peak time for product sales.

Similarly, if you're a B2B marketer, your busy time is January 1 to about the end of May. Then business picks up right after Labor Day in the United States and Canada and continues through U.S. Thanksgiving. Outside of those times, it's a lot harder to be a marketer, whether you focus on digital, social, or paid.

Listen to the show to hear more examples of predictable human behavior.

What Can Predictive Analytics Do?

Because we know these things generally, machines can help us make these predictions more specific. The value of predictive analytics is their specificity. If you know which week you should do more Facebook Live or spend less on ads, you can be more efficient and effective in your marketing. If you know how to predict, you can make money, save money, save time, and not get fired.

Predictive analytics specifically focus on trying to find out what happens next. For the average marketer, time series predictions (or when something is going to happen) are the most conventional and useful application. To illustrate, if you're a social media marketer, you want to know when to staff up your customer service team to answer customer inquiries.

Predictive analytics can also figure out things like when someone will buy a new car or if they're expectant parents. However, those applications are more nuanced than time series predictions.

Listen to the show to hear about my experiences with predictive analytics when I was a B2B write...