The amount of data gathered and stored everyday is staggering. When disk drives had small capacities only essential information was stored by a company. Today data farms commonly contain Terabytes, Petabytes and more of useable facts, figures and statistics. How is this data gathered? Anyone surfing the internet, purchasing with a loyalty or credit card typically contribute to the collection. Surveys with drawings, gift cards or free products identify needed improvements but also find out information about you. Social media activity gathers helpful information as well.
Big data not only records what is being bought but from who, how and when. Was the purchase on-line or through a traditional store and how was the decision buy made? It can show how useful reviews have been. Big data will show time of day of purchases such as before or after a workday, on a holiday or weekend. Time of year is also essential.
Obviously retail sales will be higher during the holiday buying period but for other businesses it may be different. For example, merchandise or ticket sales for a sports team may increase after a significant sporting event win. These types of factors may explain why a product was purchased.
So once the data is captured and stored how is it used? First, it allows a look back to analyze performance. Not just sales performance but how was the shopping experience. When one item was bought did it lead to the purchase of another? What were the triggers that encouraged shoppers to make a decision? From the technical point of view, how well did the installed hardware perform? These factors can generate marketing strategies.
But just as important, having this data allows for a look forward to the future. Known as “predictive analysis”, working with a large amount of data can be helpful in scheduling and capacity planning. The air travel industry can understand when the heaviest passenger loads will take place allowing them to adjust staffing, aircraft and even pricing. IT system managers know when to increase storage or compute power. Customers groups with similar likes can be targeted. Processes can be made to work better as inefficiencies are uncovered. Next time you’re asked to leave feedback, remember big data has made it possible to answer the question “what’s trending now”.
