The impact of big data market segmentation using data mining and clustering techniques

Fahed Yoseph, Ahamed Hassain Malim, Nurul Hashimah, Markku Heikkilä, Brezulianu Adrian, Geman Oana, Paskhal Rostam Nur Aqilah

Tutkimustuotos: LehtiartikkeliArtikkeliTieteellinenvertaisarvioitu

3 Sitaatiot (Scopus)
25 Lataukset (Pure)

Abstrakti

Targeted marketing strategy is a prominent topic that has received substantial attention from both industries and academia. Market segmentation is a widely used approach in investigating the heterogeneity of customer buying behavior and profitability. It is important to note that conventional market segmentation models in the retail industry are predominantly descriptive methods, lack sufficient market insights, and often fail to identify sufficiently small segments. This study also takes advantage of the dynamics involved in the Hadoop distributed file system for its ability to process vast dataset. Three different market segmentation experiments using modified best fit regression, i.e., Expectation-Maximization (EM) and K-Means++ clustering algorithms were conducted and subsequently assessed using cluster quality assessment. The results of this research are twofold: i) The insight on customer purchase behavior revealed for each Customer Lifetime Value (CLTV) segment; ii) performance of the clustering algorithm for producing accurate market segments. The analysis indicated that the average lifetime of the customer was only two years, and the churn rate was 52%. Consequently, a marketing strategy was devised based on these results and implemented on the departmental store sales. It was revealed in the marketing record that the sales growth rate up increased from 5% to 9%.

AlkuperäiskieliEnglanti
Sivut6159–6173
Sivumäärä15
JulkaisuJournal of Intelligent and Fuzzy Systems
Vuosikerta38
Numero5
DOI - pysyväislinkit
TilaJulkaistu - 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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