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Title: 高維度自我向量迴歸於零售業行銷與銷售績效分析
High-Dimensional VAR for Retail Marketing & Sales Performance Analysis
Authors: 朱家輝
Ju, Jia-Huei
Contributors: 莊皓鈞

Chuang, Hao-Chun
Chou, Yen-Chun

Ju, Jia-Huei
Keywords: 行銷
Vector Autoregression
High-dimensional Data
Date: 2021
Issue Date: 2021-10-01 10:02:57 (UTC+8)
Abstract: 零售業者在規劃商品行銷策略時,多仰賴各種資料分析技術作為輔助,而近年來許多零售與行銷研究,常採用計量模型評估行銷效果、預測銷售表現,然而在零售業中,因商品數量多、行銷手法多元,資料呈現高維度時間序列樣態,其待估計參數量多,導致模型估計不易。

Many retailers rely on various data analysis techniques to help planning marketing actions.
In recent years, many retailing and marketing research often applied mathematical models to quantify the marketing effects and forecast the sales performances.
However, in the retailing industry, the large number of products with the diversified marketing mix strategies lead to the inherent high-dimensional issue. Specifically, the amount of model parameters would be extremely large, which makes it difficult to estimate.
In this study, we develop the estimation system based on Vector AutoRegression model combined with the Lasso method. Besides the explainable marketing results, we also aim to develop the additional tools which can provide more applicable information.

To assess whether the estimation procedure can recover the true marketing effects outside the analyzed samples, we conduct Monte-Carlo simulation experiments and proposed the stochastic optimization algorithm based on Cross-Entropy method. We further compare it to iterative Lasso estimation procedure and figure out the characteristics of two methods in the case of high dimensional estimation. Making contribution on the theoretical development of retailing and marketing research.
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