There is legislation that makes manufacturers responsible for incorporating recycling of waste electric and electronic equipment (WEEE). The white appliances industry is one of these sectors and in many countries, particularly those that are members of the European Union, there are regulations to guarantee the recycling of white appliances. This paper aims to investigate the data analysis of the white appliances industry in terms of reverse logistics operations. The most important usage and logistics operation data of a white appliances manufacturer are identified and evaluated by using data-mining methods. Important factors for types of white appliances are analyzed with respect to the lifespans of products, regional data, transaction times, campaign period, and choice of new products. A neural network is applied for prediction importance and ANOVA and Pearson correlation tests for region, lifespan, and brand of new product data are performed using SPSS. The results demonstrated that customers are prone to buying the same brand when they are delivering waste white appliances. Besides analysis of the campaign time, important inferences for strategic planning could be drawn from the lifespan and regional data.