Can using business analytics drive and increase revenue for your company?

For the better half of this decade, much of the discussion has centered on the idea of big data. What do you do with it? How do you extract any discernible meaning from it all? What happens to the rest?

With business analytics, the goal is to take all of your company’s thoughtfully collected data, and statistically turn it into methods, processes and approaches that can increase revenue. Overall, the function of business analytics is to explore an organization’s data statistically. The term “statistics” itself is commonly downplayed—advanced statistics can do a lot more for a business that just reporting.

This article seeks to combine both concepts—analytics and statistics—and show you five solid reasons why harnessing said concepts can serve to drive revenue growth.

1. Discovering Unexpected Connections

Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. It’s what makes everything from email clients accurately detecting spam to credit card companies flagging suspicious activity possible, and even having a pregnancy revealed by a national chain–there’s a constant influx of data being collected and analyzed.

Another example: A retail grocery store discovered that men between 30-40, shopping on Fridays between 5 p.m. and 7 p.m., were most likely to purchase beer while looking for diapers. This discovery lead to the retail store placing their beer aisle closer to the diapers, generating a 35 percent increase in sales of both.

You can utilize this granular cross-referencing, so to speak, to discover new audiences, generate more diverse marketing and advertising campaigns, and even reduce the need for crisis communications procedures by knowing what message to convey, and how to convey it, ahead of time.

2. Combining Multiple Datasets

When mixing business data with statistics, you can add an extra layer by applying spatial data to solve problems.

Global conglomerates use this method when trying to solve supply chain efficiency fairly often. For example, UPS realized how much of a drag on fuel efficiency, and how many more accidents occurred, when their trucks would make left-handed turns. Through the use of statistics and spatial data though, their engineers were able to save 10 million gallons of gas and reduced emissions by the equivalent of taking 5,300 cars of the road for a year.

3. Making Sense of It All

So once you’ve collected, analyzed and interpreted all the data that’s most important to your company—Now what?

Because it’s difficult to understand raw data and only slightly less difficult to understand simple graphs, big data visualizations—which are often an outcome of some business analytics tools—are an easy, powerful way to explore any data set. A successful big data visualization strategy can help convey the importance of data sets to those who may not be as analytically inclined, and can range from infographics and charts to interactive visualizations and more.

4. Big Results for a Small Price

Historically, companies were forced to either bring in an expensive specialist or craft an entirely new department to create and execute a successful, data-driven digital strategy.

Over time, there has been a sharp decrease in the costs of analytics tools as the number of open-source and cost-effective software options have increased. Also, with more employees becoming specialized within the business analytics realm, it’s become easier to bring someone in house who can help with:

  • Data manipulation
  • Building reports
  • Data presentation
  • Software training
  • Maintaining best practices

Having someone who can execute all of these tasks and more will help to keep data visualization strategies lean and efficient, too.

5. Predicting the Future of Business Analytics

There are several questions that swirl around the future of big data and business analytics. From data security issues to solving bank churning woes, the effective predictability of big data doesn’t have a guaranteed success rate, but there are still plenty of viable options for you and your company.

You can see examples of predictive analytics used in the wild all the time. Netflix uses it to make suggestions on what you should watch based on past viewing experiences. Amazon employs its use to determine what it should recommend to you next because of what you’ve purchased in the past. Essentially, anytime you’ve ever found yourself interacting with a website and catch yourself asking, “How did they know?”, there’s a good chance there was a predictive analytics strategy involved.

The trick with predictive analytics is that in order to improve the efficacy of the predictability, the consumer has to be willing to provide as much data as your company is also willing to collect.

Overall, there’s no big-data crystal ball that can determine what a consumer may want based on hunches or premonitions. But with the help of a trained data scientist you start making substantial, data-driven decisions that can work to transform your business.