Applying Data Mining in Customer Relationship Management

Rodpysh, Keyvan Vahidy; Aghai, Amir; Majdi, Meysam

doi:10.5121/ijitca.2012.2302

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Association of Information Systems and finally Produce Knowledge that can be trusted (Rodpysh, Aghai, & Madji, 2012).…”

Section: Associationmentioning

confidence: 99%

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Irfiani

2019

SinkrOn

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A good sales strategy has an effect on increasing the number of sales of goods. Problems that often occur in outdoor sports equipment stores are the difficulty in determining sales strategies because there is not much interest in outdoor sports in the community. In addition, the amount of inventory in the store is excessive, which affects the sales cycle of goods. One way to help determine strategy is to use apriori algorithm. In this method can determine consumer shopping behavior patterns. Apriori algorithms are part of the data mining analysis association. This algorithm is used to determine association rules. In the study, a combination of sports equipment purchased by consumers will be determined. Determination of the combination starts from 1 itemset to 3 itemsets, the combination of rule association produces different sales transaction patterns. The results of the study in the form of a combination of consumer shopping behavior patterns that will be used as recommendations for shop owners in determining the sales strategy. The resulting Rule association will help sales promotions and add the amount of inventory that many customers buy.

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“…Association of Information Systems and finally Produce Knowledge that can be trusted (Rodpysh, Aghai, & Madji, 2012).…”

Section: Associationmentioning

confidence: 99%

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Irfiani

2019

SinkrOn

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Decision Tree: Customer Churn Analysis for a Loyalty Program Using Data Mining Algorithm

Lee

Claudia

Zainol

et al. 2019

Communications in Computer and Information Science

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In the world of retailer, customers typically patronize multiple shops thus making loyalty programs a favorite among retailer to retain their customers. Loyalty programs are utilized across many different businesses as a marketing strategy to encourage customers to continuously shop or patronize the services provided by a certain organization. However, one of the biggest problem faced by these businesses is customer churn. The purpose of this research was to build a predictive model, which could predict customer churn, where visualization of data was generated to better understand the existing members and see the patterns and behavior demonstrated by members of the loyalty program. Through these, meaningful insights about the businesses' analysis on customers could be gathered and utilized for better actions which could be taken to address the issues which the company faces. At the end, based on the issues found, strategies were proposed to address the issues found. For this research, SAS Enterprise Miner was used to perform predictive analysis while Tableau was used to perform descriptive analysis.

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A Dynamic Thresholding Technique In Spatially Co-Located Objects Mining From Vehicle Moving Data

Anitha¹,

Duraiswamy²

2017

IJBIDM

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Co-location pattern discovery is intended towards the processing data with spatial contexts to discover classes of spatial objects that are frequently located together. The existing moving vehicle location prediction technique not analyses the moving vehicles co-location instance. So, we improve the previous technique process by mining spatially co-located moving objects using spatial data mining techniques. Initially, the neighbour relationship is computed by the prim's algorithm. After that, the candidate co-locations are pruned according to the presence of candidate co-location in the input data and the final stage of co-location instances selection is performed by compute neighbourhood and node membership functions. The values obtained using neighbourhood membership function is compared with the dynamic threshold values. The co-location instances are selected which satisfy the dynamic threshold value. Moreover, the proposed co-location pattern mining with dynamic thresholding technique is compared with the existing co-location pattern mining technique.

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Applying Data Mining in Customer Relationship Management

Cited by 3 publications

References 9 publications

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores

Decision Tree: Customer Churn Analysis for a Loyalty Program Using Data Mining Algorithm

A Dynamic Thresholding Technique In Spatially Co-Located Objects Mining From Vehicle Moving Data

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