The term "Big Data" was the Internet buzzword of 2012. The use of Big Data promises to become more prevalent in 2013, as merchants large and small who use Big Data analytics can gain a significant competitive advantage.
What is Big Data? Webopedia defines it as "a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques."
The "structured" portion of Big Data refers to fixed fields within a database. For ecommerce merchants, this could be customer data — address, zip code — that's stored in a shopping cart.
The "unstructured" part encompasses email, video, tweets, and Facebook Likes. None of the unstructured data resides in a fixed database that's accessible to merchants. But the feedback from, say, social media has become a very useful research tool for businesses.
A Powerful Tool; Be Careful What You Use It For
Big Data can provide detailed insights into customer behavior that can be unsettling. Here's an example. Early last year a 15 year-old girl in Minneapolis went to her local Target store and purchased unscented body lotion. Target assigns every customer a guest ID number tied to a credit card, name, or email address. Target maintains a history of everything that person buys along with demographic information.
Within a few weeks, Target mailed the girl coupons for pregnancy and baby-related items. The girl's father found the coupons and in an angry mood went to talk to the Target store manager, accusing him of encouraging his daughter to become pregnant. The store manager, unaware of why the coupons were sent, apologized. He called the father a few weeks later to again say how sorry he was. But this time the father apologized. It turns out that Target, using Big Data analytics, knew more about the man's family situation that he did. The girl was indeed pregnant. It turns out that pregnant women buy a lot of unscented lotion.
In short, Target's analytics are so good it can predict the trimester of pregnancy based on what a female buys. But Target received a good deal of negative feedback after this story was publicized.
How Can Ecommerce Merchants Use Big Data?
Merchants can use Big Data in many different scenarios. This can include comparing traffic to a particular product to the sales of that product.
Mark Ledbetter, global vice president for SAP Retail offered this example to Retail Info Systems News, a magazine. "Retailers can compare the volume of website traffic for a given product versus number of sales of that product. You'd expect a correlation between web traffic and sales — consumers find the product they want, and then they buy it. But if you find a lot of web traffic and few sales, something is wrong. It's a signal to keep the product, whereas in the past, it may have been discarded due to low sales. Now you just need to confirm the product is competitively priced, has a compelling and informative presentation, an array of colors and sizes, and all other aspects that are required to incent the customer to make that final, and most important, step: the purchase."
Is Big Data Only for Big Companies?
Any sized company can benefit from Big Data. Amazon is a pioneer in its use, but smaller ecommerce companies can benefit as well. “Big data is quite simply data that cannot be managed or analyzed by traditional technologies,” according to Rebecca Shockley, global research leader for business analytics at the IBM Institute for Business Values, quoted in IBM's Forward View magazine. “So what is considered big data for one company may be different for another company. ‘Big’ doesn’t have to be really that big; it’s just bigger than what you’re used to dealing with,” she adds.
Many business-analytics consulting firms exist to help smaller companies deal with Big Data. Software to deal with Big Data is also available. One of the most popular is Apache Hadoop, an open source software framework that supports data-intensive applications. As Big Data becomes entrenched in more companies, the software tools will become more sophisticated and less costly.
Seek a positive return on investment for Big Data. It helps to have a strategic intent as to how you will use the data when you start out. Ask the right questions that identify the specific decisions that data and analytics will support to provide favorable business outcomes. Initially keep things simple and then move on to more sophisticated uses.