Analytics include predictive analytics, prescriptive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.
Retailing and banking sectors were the early birds and was followed by other areas like medicine, social media. Assume a retail supermarket scenario, where by enrolling a particular customers as members, every single purchase of the customer is recorded, over a period of time. By applying analytics on this data, customer behavioral patterns could be drawn. These insights can be used to improve the overall service deliveries and to come up with individual customer specific offers for better advertisement and sales. Similarly, with the availability of past consumer datasets, a banking firm can actually predict future defaulters or profitable consumers, thereby improving profitability. Such is the potential of Data Analytics.