Consumer Packaged Goods (CPG) Predictive Analytics Models

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Consumer Packaged Goods (CPG) Predictive Analytics Models (EN)

Mantzoufa, Ioanna (EN)

Baltagiannis, Agamemnon (EN)

masterThesis

2020-06-04
2019-04-17
2020-06-04T13:10:14Z
2020-06-05T00:00:34Z


This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. It aims at analyzing the marketing mix of a retail company’s product through the use of data science techniques and algorithms. Initially, an introduction is written in terms of marketing, marketing mix and modeling. Thence, the challenges faced by retailers in modeling marketing mix are analysed and some policies to address these situations are proposed. Afterwards, some examples take place of cases where data science is involved in procedures relating to retail companies. Finally, the algorithms used for retail analysis are analyzed and the procedure followed for a product’s sales prediction is explained, using multiple linear regression. Next, a detailed description of the dataset used for the analysis and the variables according to which the result was extracted, was made. Furthermore, exploratory data analysis was applied by visualizing the dataset’s features using the R language, SAP HANA Studio and SAP Predictive Analytics. Τhe next step was the implementation of multiple linear regression algorithm purposing to predictive modeling for retail sales forecasting using the aforementioned tools, concluding that this method has proven to be very effective. At last, some conclusions are drawn and some issues for future research are suggested. (EN)


Consumer packaged goods (EN)
CPG (EN)

English

School of Science and Technology, MSc in Data Science
IHU (EN)

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