This study proposes RFID data analytics methods using consumer purchase behavior data as well as sales data. Consumer purchase behavior data, how consumers select or not select items at stores, has not been available, but with the progress of the radio frequency identification (RFID) technology, it becomes feasible to use it in a commercial basis. The proposed data analytics methods are two: one is to infer items that are compared to each other while customers select items at stores, and the other is to infer good combination items that can be used to stimulate sales of each other. We evaluate our proposal using real consumer purchase behavior data and sales data captured in an RFID pilot experiment. We confirm that the methods successfully infer the items that are useful to the retailer.