5 Reasons Why Business Analytics Will Help Drive Revenue for Your Company

5 Reasons Why Business Analytics Will Help Drive Revenue for Your Company

5 Reasons Why Business Analytics Will Help Drive Revenue for Your Company

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.

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.

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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.

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.

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