Commodity Sales Data Analysis Based on Qoo10 Platform In recent years, many traditional trade practitioners have embraced electronization and digitization and enjoyed the convenience brought by technology. This paper takes Singapore as an example in the emerging Southeast Asian ecommerce market, based on the local mainstream ecommerce platform qoo10 to provide operational strategies for new commodities. Emphasis is laid on the monthly explosive Germa minidehumidifier as an example to study the operation strategy of commodity online initial stage, fierce competition period, heat cooling period, and provide data for the corresponding period as support. Finally, it gives suggestions for the new and old businesses and platform practitioners of qoo10, and summarizes the experience and lessons of the operation process.

A Clusteringbased Sales Forecast Method for Big Promotion Days in O2O OnDemand Retailing O2O ondemand service is a fastgrowing business model which integrates traditional offline retailing and ecommerce. Big promotion is an effective way to attract new customers and promote sales in O2O service. However, high sales brought by big promotions can incur challenges for the operation management, especially for delivery capacity planning and replenishment scheduling. Using an accurate sales forecast is shown to be an effective way to alleviate such problems, though this technique still has its challenges. In this paper, we propose a clusteringbased random forest (RF) method to predict the sale levels in shops for big promotion days. We further apply a dataset from one of the largest O2O grocery platforms in China to validate our method. The results show that the forecast accuracy of our proposed algorithm is 13.43 % higher than nonclustering RF method, 22.85% higher than nonclustering ARIMA method.