Facts are Better than Assumptions
Whether you’re marketing, managing, or plotting strategy one of the most common (and sometimes deadly) mistakes made by organizational leaders is making key decisions based on assumptions rather than data. In the era of big data, organizations have access to data in qualities and quantities that are exponentially more effective than ever before.
But an increase in the available data is only part of the picture. Until recently, the computing power needed to perform complex business analysis wasn’t very accessible, so big data couldn’t be leveraged to its full potential. Today, cloud-based computing through services like Amazon Web Services (AWS) run complex algorithms using big data to predict buying behavior using information harvested from social media, loyalty programs, or even web browser histories. Smart companies use the data and patterns uncovered by AWS to make highly accurate decisions about strategy.
Let’s look at a few companies that have used or failed to use big data and business analytics to their benefit.
Yelp: Using AWS for Rapid, Comprehensive Simulation Testing
Internet information provider Yelp has used cloud computing power provided by AWS to dramatically decrease their software development times. Most of this condensing of the development timetable comes from massive reductions in the time it takes to perform comprehensive testing – especially simulations. Because AWS provides a high level of computing power, scenario-based algorithms can be run quickly – even with thousands of variables.
What kinds of organizations would benefit from the ability to analyze huge amounts of data quickly? Yelp used AWS to run scenario-based testing on new software, but what other products or industries would benefit from the ability to use cloud computing to quickly perform hundreds or thousands of simulations?
JCPenney: Failing to Use Big Data to Inform Pricing Strategy
From about the year 2000 well into the 2010s, the national department store JCPenney struggled to find a strategy to improve dwindling sales and fierce competition. During this time, the retailer attempted to abandon the typical high-low pricing model on the assumption that many customers wanted the best price first rather than a high price that dropped over time.
This assumption, at least in part, proved to be inaccurate. In reality, sales fell even further with the new strategy. After conducting further analysis, the retailer realized that market research actually indicated that customers were far more enthusiastic about getting a great bargain rather than simply getting a low price. Since the new Penney’s pricing model abandoned sales or discounts, customers who were used to working hard for a bargain were largely ambivalent to the lower prices. Ultimately, the retailer was forced back into a pricing model that resembled the status quo.
What was JCPenney’s most serious error in this case? What did the company miss or fail to utilize when planning the new pricing model? Would a big data analysis through AWS have helped the retailer in this instance? Why or why not?