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This paper discusses an allocation issue of promotion budget and traffic volume, faced by VIP.com that is the largest online flash-sales platform in China. Through flash sales mode, each day VIP.com provides consumers one hundred authorized brands of products on consignment, which last for a short period of time (frequently 3–5 days), called by VIP.com 'Dangqi'. As a consignee, VIP.com has no pricing rights of products but can allocate the promotion budget and traffic volume to each brand. Due to different categories of products and different brands of products having different responses to the same promotion budget and different profit margins, it is very necessary for VIP.com to allocate promotion budget and traffic volume among all brands of products offered in each 'Dangqi' reasonably in order to improve the usage efficiency of limited resources. Based on the real historical data of VIP.com, we first find main elements that influence the sales revenues of different brands of products through machine learning. We then predict the sales of all brands of products in each 'Dangqi' by multiplicative regression model, which has better accuracy of forecasting than other forecast models and obtain the function relationship between the total sales of each brand and its main impact elements. Finally, considering VIP.com's actual concerns, we develop allocation optimization models with objectives of maximizing VIP.com's total sales and total sales profit, respectively. The results from VIP.com's real data tests show that under the same resource investment the presented allocation optimization approach can yield a significant increase in VIP.com's total sales and sales profit in each 'Dangqi'. [ABSTRACT FROM AUTHOR]