Λογότυπο αποθετηρίου
 

Data analysis in a "dark store" environment

Μικρογραφία εικόνας

Ημερομηνία

2022-02-02

Συγγραφείς

Katsis, Ilias

Τίτλος Εφημερίδας

Περιοδικό ISSN

Τίτλος τόμου

Εκδότης

Επιβλέποντα

Διαθέσιμο από

2022-02-08 23:52:07

Περίληψη

The scope of this thesis is to analyze multidimensional data from a dark store called PockeeMart and derive deep business insights in order to achieve a complete data-driven strategy. The main objective of the dissertation concerns the application of machine learning to the data with purpose to elicit association rules, segment the customers based on their shopping behavior and create a predictive scenario, which classifies the order’s placement recency of the customers. PockeeMart is active in the FMCG sector, which is very demanding and competitive. The number of issues, that need to be taken into consideration and the number of tasks that must executed, makes it really challenging. During the pandemic period dark stores became more popular, as they do not require physical presence being part of online retailing and offer various other benefits for the users.

Περιγραφή

Λέξεις-κλειδιά

FMCG, Dark store, Association rules, Customer segmentation, Classification

Παραπομπή

Άδεια Creative Commons