A Short Primer on Predictive Analytics for Marketers

Olin Hyde
December 28, 2016

Are you developing a B2B marketing plan for 2017?

Are you brainstorming about your next inbound marketing moves?

How about predictive analytics?

Have you included that in your plan?

Given that 89% of marketers reported in January that predictive analytics (PA) was on their roadmaps, there’s a good chance you’re going to be tasked with figuring out how it works and how it can help boost sales.

Predictive analytics is a game-changer, capable of boosting the effectiveness of your branding and your marketing campaigns. But some marketers fear PA. They shouldn’t. It’s a powerful marketing tool that can drive marketing to new heights.

Below is a short primer on PA to help you understand what’s going on behind the scenes. The information below can increase your chances of having an intelligent conversation with a data analyst provider and use predictive analytics to boost sales:

For many marketers, predictive analytics can seem daunting and unnecessary.  After all, it’s rare to find someone who got into marketing because they like math, and it’s such a new concept that many shy away from using it.

We never needed predictive analytics before, so why start using it now? The answer is, as with so many things, because the internet is where most business happens now and because data is much more plentiful now.

Let’s take a closer look at this tool to see how it can help you boost sales:

What Is Predictive Analytics?

 Predictive analytics is exactly what it sounds like: using data to make educated guesses as to what will happen in the future. Put another way, predictive analytics is the science of learning from your mistakes.

To do PA properly, you must keep records. For example, a simple predictive analytics experiment that you may have done yourself is tweaking your diet. This effort can be for weight loss, general health, complexion, or if you’re trying to pinpoint an allergy.

To do this experiment right, you need to diligently log not only your food and drink, but also contextual information that may be relevant – time asleep, water intake, locations visited, and so forth. That’s predictive analytics.

To give you an idea of the value these calculations can bring to a business, one of the major equations that most businesses are eager to perfect is their Customer Lifetime Value equation.

This measure will indicate how much money a customer is likely to spend over the course of their “lifetime” – their total relationship with the brand – based on their behavior. But predictive analytics isn’t magic. It’s statistics. To get the data you need to do predictive analytics, you have to measure things accurately.

Data Collection is Imperative

To make accurate predictions, and make sure you aren’t wasting your time on marketing that doesn’t work, you need to have quality data. Below are two sources of quality data:

  • Loyalty Programs

Ever wonder why so many stores have loyalty programs? Why would they give so much away free? Well guess what – that 10th coffee, 5th burrito, or birthday surprise gift isn’t free. Consumers trade it for providing data about when and where (in the case of a franchise) they shop and what they buy. More often than not, they also submit their gender, home address, and sometimes even their income and occupation. All for a free lipstick once a year. This approach works just as well for B2B. So don’t be afraid to offer incentives to buy, though we suggest ebooks and swag over cosmetics or coffee.

  • Login Information

Further, if you process purchases online (and who doesn’t these days) and you don’t collect data, then you’re actively fighting progress. Every login’s purchases should be tied together, and compared and analyzed against others to find the common trend. What does this mean for you, the B2B marketer? Allow your data analyst to communicate with your IT and site admin to make sure they’re getting the data they need.

Personal information like that generated with the above programs provide marketers with data that can fuel a predictive analytics effort.

Assumed Information Helps Also

The last piece of the predictive analytics puzzle is the assumptions that allow equations to be created. Assumptions are essentially the context of a situation. In using this information, we generally feel that things will remain as they are. That enables us to build models to predict someone’s behavior.

It’s important to note that if there is a major change, from breaking news that affects your industry, to a national event that affects everyone, these models will no longer work correctly and the equations derived from the models no longer accurate.

To develop predictive models, you must assume that the assumptions these models and equations are based on are true, and that due diligence has been done to ensure that they are as accurate as possible. If everything works out, your models and your equations will provide an accurate results that can help boost your marketing efforts.

Last Word — Boosting Sales and Profits

While these principles cover the basics of predictive analytics, there are many more complexities that you’ll soon be diving into. In many respects, predictive analytics is both an art and a science.

So work with your data analysts and tech teams to ensure that everyone understands what’s going on at all times when it comes to predictive analytics. The more you know about it, the better your chances of using it effectively to boost sales and profits.

Have you started using predictive marketing at your company? What have been the results?

Further Reading

Lainey Mebust
July 9, 2020
Lainey Mebust
June 15, 2020
Lainey Mebust
May 29, 2020