How predictive analytics can help you. Case 1: Retail
Customers rarely explicitly reveal their preferences. They usually do so implicitly in their purchases and transactions. Analysing their purchases, we can gain insights on what customers want, how much they are willing to pay, and how they choose between competitors and competing brands.
Consider the following example: Suppose a person’s credit card transactions includes a hockey stick. Would this indicate that the person likely plays hockey? What about if the same credit card is used during hockey season to buy hockey gloves and jerseys? Would this indicate, then, that the person likely plays hockey? What if the person purchased only a particular hockey team’s jerseys consistently? Would this indicate loyalty to that team? Predictive analytics can help answer those questions by “figuring out” what is on the customers’ mind.
One business that illustrates profoundly the power of analytics is Wal-Mart. Wal-Mart’s ability to use predictive analytics to guess what customers will be interested in, how much they will buy, and at what price and its ability to act based on that information has made it a leader in the retail industry. For example, Wal-Mart have determine that before a hurricane, customers stock-up food items that do not require cooking or refrigeration. In that market segment, they found that Kellogg’s Strawberry Pop Tarts are the most popular. We can expect that Wal-Mart will make a rush order of Pop Tarts just before the hurricane hits.
In short, predictive analytics allows retailers to “figure out” what is on the customers’ mind. Using this knowledge to optimize the performance of the entire value chain is key to outmaneuver competitors and guarantee success in today’s competitive marketplace.
To learn more:
- Wikipedia’s entry on predictive analytics
- Competing on Analytics by T.H. Davenport and J.G. Harris