Profiling and Segmenting Clients with the Use of Machine Learning Algorithms
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/2281
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 solution for customer profiling and
segmentation using modern machine learning methods.
Design/Methodology/Approach: Models were developed to improve the analysis of data,
human behavior, data mining business processes, and as a result, the creation and provision
of new improved solutions using machine learning algorithms. The GRU method was used,
which is a simplified but also a more streamlined version of the LSTM cell offering similar
performance with a much lower computation time.
Findings: The main purpose of the developed solution is to enable and improve the analysis
of profiling and segmentation of customers for forecasting sales, due to the possibility of
detecting or determining additional seasonal effects.
Practical Implications: Effective tools have been developed to enable customer
segmentation. A more complex model was used, taking into account the sale, especially in the
sense of the time series in which the sale took place. In its form, the model consists of a trend
function modeling non-periodic changes in the value of time series periodic changes.
Originality/Value: A novelty is the use of the GRU network, which is an improved version of
the standard recursive neural network and a simplified version of the standard LSTM
network. Similarly to LSTM networks, it aims to solve the problem of a vanishing gradient,
i.e., its disappearance or explosion. In the presented solution, a more complex model was
used, consisting of several components and taking into account sales, especially in the sense
of the time series in which the sale took place.