Predictive Analytics

How to Implement Predictive Analytics in Your Business

Predictive analytics is a massive money-maker – and if you’re not using it to better serve your customers, your business is seriously missing out. Take a look at Amazon, who implemented predictive analytics to study the behavior of more than 200 million customers. They used this data to create tailored product suggestions which now make the company over $2 billion in sales a year.

$2 billion is a huge chunk of change, and while your business may not be a massive company like Amazon (yet), integrating predictive analytics into your business is a surefire way to pull in sales. If you’re wondering how to implement predictive analytics, I’m going to help you get started. Here are some things you need to consider.

What Is Predictive Analytics?

If you’re in marketing or sales, it’s a pretty safe bet you’ve used some form of predictive analytics before. If you’re a consumer, you’ve definitely seen it in those ultra-specific Amazon ads that always seem to know what you want or even just your perfectly-catered Netflix suggestion queue. Predictive analytics is a subset of business intelligence that learns past behaviors by collecting sets of data and helps predict future behaviors. This has been used in a number of industries including:

  • Retail – to predict customer buying trends to help keep a solid inventory.
  • Healthcare – to predict the likelihood of disease or certain cancers before they’re contracted so preventive measures can be taken.
  • Insurance – to predict the likelihood of accident, death or disease to develop insurance rates.
  • Finance – to predict the likelihood of someone paying off a loan and/or which stocks will rise and fall.
  • Marketing – to put products in front of the consumers who want them via targeted online ads.

Best Practices for Predictive Analytics


Predictive analytics is a powerful tool, but only when it’s implemented properly. Successful predictive analytics implementation requires you to follow a certain set of best practices. Lots of teams use the Cross Industry Standard Process for Data Mining model (CRISP-DM) to collect their data and guide the system they’re trying to create. This helps you organize the data you already have and implement the findings into automated businesses processes. I’ll show you how to get started with this later.

As far as integrating predictive analytics into your business, there are some steps you must take for the largest chance of success: clarify your objectives, build your infrastructure, define your idea of success, launch early predictions, and maintain your data.


Clarify Your Objectives so You Know What Data to Collect

You can’t gather data successfully without knowing what type of data you want to predict. You could want to predict the likelihood that your customers will need a warranty by diving into data related to repairs and broken products. You may want to figure out how much of a new product you’ll need to order, or even which email list promotions help retain customers. This all requires different sets of data. In order to successfully apply the predictive analytics, you need to collect the right kind. Identify the metrics you want to understand before you even start the process of implementation.

Define Success: How Does This Data Help You Reach Your Goals?

You can’t achieve your goals if you don’t what they are. Figure out what you want out of predictive analytics. There’s a margin for error, but that doesn’t mean your business won’t find success by using the data available. Set attainable goals. If increasing online sales is the end game, consider integrating predictive analytics in customer suggestions on your website. If you’re looking to gain x number of repeat customers, consider targeted email campaigns using predictive analytics. Figuring out your end game helps you make a pathway to success.

Build the Infrastructure


You’re going to need a lot of information to implement predictive analytics. The more information you have, the more accurate it’s going to be. This means you have to make sure your team makes all the data available to you possible. You may need to make API connections and import data from tools owned by different departments. Open your lines of communication and get everyone on board while you’re building your predictive analytics infrastructure.

Launch Early Predictions

Predictive analytics get more accurate as time goes on because more and more data is collected and used. Start your predictions now with the data you have. This may not be the most accurate, but it can help you know if you’re on the right path. If your model predicts outcomes you know are already proven, you’re headed in the right direction. If not, collect more data and continue testing.

Maintain Data

Predictive analytics will not be successful if data isn’t constantly being updated and maintained. It’s great that you’re up and running, but you need to make sure your predictions are as accurate as possible. The truth is that customer behavior can change. New trends are started every day and buying habits are finicky. If you want to be ahead of the curve, you’ve got to keep an eye on it always.



Predictive analytics are one of the best things you can do for your enterprise, but you’re only as strong as your analytics team. Because it is a circular process, things must always be built and updated. You want to make sure you have the best analytics team as possible. To get connected and start the process of integrating predictive analytics into your business, post a request on DevTeamSpace. They can help connect you with leading developers in the artificial intelligence space.

When it comes down it, predictive analytics is always helpful, but not an absolute. Computers can take out some of the guesswork within your business, but at the end of the day, you know your customers and their needs. Use predictive analytics to guide you.

Mariel Loveland

Mariel Loveland

A professional tech writer and a tech enthusiast. I like sharing my knowledge and helping readers make educated decisions in the tech space.
Mariel Loveland