Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data

Kurniawan, Fachrul; Umayah, Binti; Hammad, Jehad; Nugroho, Sudarmanto Budi; Hariadi, Mochammad

doi:10.17977/um018v1i12018p20-25

Cited by 43 publications

(30 citation statements)

References 6 publications

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“…These was done towards solving the concept drift problem inherent in the transactional data stream. This study verifies the submission of [9]. Regarding considering consumer behavior theories, which recognize four factors that contribute to the reason why customers purchase the items in a particular shop.…”

Section: Discussion Of Findingssupporting

confidence: 77%

“…Regarding considering consumer behavior theories, which recognize four factors that contribute to the reason why customers purchase the items in a particular shop. This study verified the four-factor and expanded on things that can cause a changed of behavior by a consumer using two main consumer behavior theories (reasoned action and the planned behavior theories) which were not used in [9] study. Previous studies such as [4] and [2] that tried to handle concept drift problems, did not attribute it to market basket analysis; instead, they only decided to solve the problem in stream mining applications.…”

Section: Discussion Of Findingsmentioning

confidence: 68%

“…Author in [9] examined Market Basket Analysis in other to identify customer behaviors through the use of Transaction Data. The study actually examined the sales transactions of a particular company to better understand their customers.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Data Analytics Market Basket Model for Transactional Data Streams

Izang¹,

Nicolae²,

Shade³

et al. 2019

IJACSA

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Transactional data streams (TDS) are incremental in nature thus, the process of mining is complicated. Such complications arise from challenges such as infinite length, feature evolution, concept evolution and concept drift. Tracking concept drift challenge is very difficult, thus very important for Market Basket Analysis (MBA) applications. Hence, the need for a strategy to accurately determine the suitability of item pairs within the available billions of pairs to solve concept drift chalenge of TDS in MBA. In this work, a Scalable Data Analytics Market Basket Model (SDAMBM) that handles concept drift issues in MBA was developed. Transactional data of 1,112,000 were extracted from a grocery store using Extraction, Transformation and Loading approach and 556,000 instances of the data were simulated from a cloud database. Calibev function was used to caliberate the data nodes. Lugui 7.2.9 and Comprehensive R Archive Network were used for table pivoting between the simulated data and the data collected. The SDAMBM was developed using a combination of components from elixir big data architecture, the research conceptual model and consumer behavior theories. Toad Modeler was then used to assemble the model. The SDAMBM was implemented using Monarch and Tableau to generate insights and data visualization of the transactions. Intelligent interpreters for auto decision grid, selectivity mechanism and customer insights were used as metrics to evaluate the model. The result showed that 79% of the customers from the customers' consumption pattern of the SDAMBM preferred buying snacks and drink as shown in the visualization report through the SDAMBM visualization dashboard. Finally, this study provided a data analytics approach for managing concept drift challenge in customers' buying pattern. Furthermore, a distinctive model for managing concept drift was also achieved. It is therefore recommended that the SDAMBM should be adopted for the enhancement of customers buying and consumption pattern by business ventures, organizations and retailers.

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Section: Discussion Of Findingssupporting

confidence: 77%

Section: Discussion Of Findingsmentioning

confidence: 68%

See 1 more Smart Citation

Scalable Data Analytics Market Basket Model for Transactional Data Streams

Izang¹,

Nicolae²,

Shade³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…decision tree and SVM-Lib support vector machine in order to analyze the portfolio and customer classification. Each of these algorithms has the properties shown in the tables (6)(7)(8). Table 4 shows the details of the deep neural networks algorithm for basket analysis and customer classification.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…There are challenges and difficulties in how to provide services in value-added telecommunication systems such as inadequate accuracy and high error of providing related services to the customers. Until now, there are various methods for analyzing customers' portfolio such as the method of customer basket analysis based on their transaction records [8], customer basket analysis approach by process category [9], portfolio analysis approach. Customer Acquisition with Apriori Algorithm [10], Customer Basket Analysis Approach Using a Combination of Artificial Intelligence Techniques and Associated Laws and Minimal Spanning Tree [11], Customer Basket Analysis Approach with the Advance System Business Strategy Forecast [12], Improving the approach of customer basket analysis in an efficient way called feasibility Utility Mining [13], is provided.…”

Section: Introductionmentioning

confidence: 99%

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Etebarian

Azmi

Dastjerdi

2020

Preprint

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Value Added Services at Mobile Communications Company provide customers with a variety of services. Value added services generate significant revenue annually for telecommunications companies. Providing solutions that can provide customers of a communications company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customers' carts and provide related services. Despite the many applications that these methods have, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile communications company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile communications company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, it has improved the accuracy of about 7%.

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