Baseline prediction of point of sales data for trade promotion optimization

机译：销售点数据的基线预测以优化贸易

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Baseline prediction is an important to devise marketing strategy for a consumer goods product. Simulation techniques, time series algorithms are often used to generate baseline for the future. However the algorithm that fits a particular point of sales (POS) data varies according to the datasets. Sample set of point of sales data were simulated under different conditions and constraints incorporating seasonal and non seasonal trends. This study has compared the performance of two time series models namely Winters model and linear exponential smoothening on the simulated datasets. Winters model was found to be a better fit for the point of sales data that were used for testing.

机译：基线预测对于设计消费品产品的营销策略很重要。仿真技术，时间序列算法通常用于生成未来的基准。但是，适合特定销售点（POS）数据的算法根据数据集而有所不同。在包含季节性和非季节性趋势的不同条件和约束下，模拟了一组销售点数据样本。这项研究在仿真数据集上比较了两个时间序列模型（即Winters模型）和线性指数平滑的性能。发现Winters模型更适合用于测试的销售数据。

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来源
《2012 2nd International Conference on Communications and Information Technology》|2012年|p.17- 20|共4页
会议地点 Hammamet(TN)
作者
Sundararaman Karthik; Parthasarathi Jinka; Rao G Subrahmanya VRK; Nandha Kumar S;
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作者单位

Cognizant Technology Solutions, Chennai, India;

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正文语种 eng
中图分类通信;
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Baseline prediction of point of sales data for trade promotion optimization

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