Methods of Analyzing Consumer Behavior Based on Multi-Source Data
Ładowanie...
Data
2021
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Informatyka techniczna i telekomunikacja
Czasopismo lub seria
EUROPEAN RESEARCH STUDIES JOURNAL
ISSN
1108-2976
ISBN
DOI
10.35808/ersj/2229
Strona internetowa
Wydawca
Wydawca
Wydanie
Numer
Strony od-do
Tytuł monografii
item.page.defence
Tytuł tomu
Opis
Rodzaj licencji
Abstrakt (en)
Purpose: The aim of the article is to develop a system for analyzing processes and data from
various data sources based on machine learning methods.
Design/Methodology/Approach: For data analysis, models for forecasting the level of sales
and the pipeline gathering operations for the preparation of features, data, model training
and its verification were designed. The data analysis pipeline is built of stages related to the
preparation of features and data preparation. The learning process requires a specific
division of data. The data set has been divided into three subsets, i.e., the training and
validation data used in the learning process and the test subset used to verify the quality of
the model.
Findings: The results of the conducted research show that the use of this type of analytical
methods allows for the creation of new business processes, adaptation of services and goods
to customer requirements, or the appropriate location of products on the retail space in
order to optimize the time of shopping (especially taking into account the pandemic
situation).
Practical Implications: The models presented in the article can be used by combining sales
systems and behavioral data related to the movement of customers in the area of the sales
space, where it is possible to build systems that allow optimization of orders, the way of
arranging goods and other customer behavior patterns.
Originality/Value: A novelty is the construction of a multi-source model for data analysis,
where appropriate predictive models were built to predict the level of sales with the use of
machine learning algorithms.