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Trade Promotion Forecasting (TPF) is the process that attempts to discover multiple correlations between trade promotion characteristics and historic demand in order to provide accurate demand forecasting for future campaigns. The ability to distinguish the uplift or demand due to the impact of the trade promotion as opposed to baseline demand is fundamental to model promotion behavior. Model determination enables what-if analysis to evaluate different campaign scenarios with the goal of improving promotion effectiveness and ROI at the product-channel level by selecting the best scenario. ==Trade promotion forecasting challenges== Trade Promotion spending is one of the consumer goods industry’s largest expenses with costs for major manufacturers ranging from 10 percent to 20 percent of gross sales. Understandably, 67 percent of respondents to a recent survey said they were concerned about the return on investment (ROI) gained from such spending. Quantifying ROI depends heavily on the ability to accurately identify the “baseline” demand (the demand that would exist without the impact of the trade promotion) and the uplift.〔“(Trade Promotion Management: The Haves and Have-Nots ),” ''Consumer Goods Technology'', September 2012.〕 In fact, forecast accuracy plays a critical role in the success of consumer goods companies. Aberdeen Group research found that best-in-class forecasting companies (with an average forecast accuracy of 72 percent) have an average promotion gross margin uplift of 28 percent, while laggard forecasting companies (with an average forecasting accuracy of only 42 percent) have a gross margin uplift of less than 7 percent.〔“Sanhir Anand and Nari Viswanathan, “(Plan, Spend and Prosper: Making the Most of Trade Promotion ),” AberdeenGroup Research Report, September, 2007.〕 A bottom-up sales forecast at the SKU-account/POS level requires taking into account product attributes, historical sales levels and store specifics. The large number of different variables which describe the product, the store and the promotion attributes, both quantitative and qualitative, could potentially have many different values. Selecting the most important variables and incorporating them into a prediction model is a challenging task.〔Michael Trusov, Anand V. Bodapati, Lee G. Cooper, “(Retailer Promotion Planning: Improving Forecast Accuracy and Interpretability ),” ''Journal of Interactive Marketing'', Summer/Autumn 2006.〕 Despite these challenges, two-thirds of companies in the consumer supply chain consider forecast accuracy a high business priority. 74 percent said it would be helpful to develop a bottom-up forecast based on stock-keeping unit (SkU) by key customer.〔“Half of consumer goods firms still using Excel for forecasting, need better tools,” ''TechJournal'', August 3, 2012.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Trade Promotion Forecasting」の詳細全文を読む スポンサード リンク
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