Clustering retail products based on customer behaviour

Hol, Vladimr; Sokol, Ondřej; ern, Michal

doi:10.1016/j.asoc.2017.02.004

Cited by 51 publications

(28 citation statements)

References 23 publications

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“…There are factors to consider during the analysis. The need to categorize retail products for the business decision-making process is emphasized as mentioned by Holý et al (2017). In addition, also consider the occurrence of food waste due to the performance of operations, as they have negative impacts on the store's performance in terms of costs, reduction of product profit margins (Teller et al, 2018).…”

Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

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Application of costing methods: A case study in a retailer in the Triângulo Mineiro

Solon

Araújo

2021

BJO&PM

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Goal:The aim of this article is to show the importance of applying costing methods to increase the contribution margin and decision-making, besides helping the company to become more competitive against competitors.Design / Methodology / Approach: This study carried out a case study of a quantitative nature, based on bibliographic works. He employed a semi-structured interview. The Direct / Variable Costing method was used to recognize points that could be improved. In addition, the data was organized into spreadsheets in the Office package. Results:The results of the survey show at the end of the action survey that the company chose to outsource the raw material from its second item of highest revenue: cheese bread. With the change, the item increased its contribution margin by 30.16% and reduced costs by 77.98%, increasing the company's profit, reducing the product's equilibrium point from 54.14% to 4.14% of average daily production. Limitations of the research:As limitations of the study, we highlight that the company passed on the already established costs for some products, so the costs were calculated considering this percentage.Practical Implications: Its practical implications are to demonstrate the importance of the relationship between costs and sales price as a decision-making strategy.Originality / Value: The study contributes to the academic area and adds knowledge related to costs and sales price for the bakery sector, being useful for users and interested in the bakery sector and its costs.

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Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

“…Also, according to Bruni and Famá (2010) costs and expenses differ to the extent that costs are directly related to the manufacturing process and expenses are more associated with administrative expenses. As such, retail chains try to minimize costs in any way (Holý et al, 2017).…”

Section: Costsmentioning

confidence: 99%

Application of costing methods: A case study in a retailer in the Triângulo Mineiro

Solon

Araújo

2021

BJO&PM

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“…Vladim´ir Holy et al [1] proposed a method for clustering products. The author framed optimization problem by applying genetic algorithm on simulated data and real data.…”

Section: Review Of Literaturementioning

confidence: 99%

Finding Best Possible Number of Clusters using K-Means Algorithm

Maheswari¹

2019

IJEAT

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Customers are assets for business. The companies are investing more for customer relationship management. Retaining customer for long time is a difficult process in today’s trend. On line shopping is also increasing day by day. People are more interested to visit popular web sites and they are spending very less time to choose their products. On line shops are paying more interest to analyze customer preferences, their needs, shopping behaviors through data mining technique. Proper classification is necessary for organizing such data. In this work, Customer with the same buying behavior is grouped based on the features age and salary. K-Means algorithm is applied to form clusters with different K values for original data and normalized data. The within sum of square (wss) is calculated for both the data for different cluster size. The minimum wss is considered to be better which is achieved in normalized data. The validity of cluster is evaluated by elbow, silhouette and gap statistic method to choose the optimal number of clusters. This work is implemented in R software.

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“…Additionally, Tsai et al (2017) developed a shopping behaviour prediction system based on moving patterns and product characteristics indicating suitable strategies for an individual customer to increase profit. Another relevant study was conducted by Holý et al (2017), which was related to product categorisation based on customer behaviour using only market basket data. Finally, Wang and Tseng (2015) work, which proposed the Naïve Bayes classifier-based approach for matching the customer requirements to existing products, provided a good foundation for this study.…”

Section: Introductionmentioning

confidence: 99%

Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach

et al. 2021

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Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency-Frequency-Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers' product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide ABOUT THE AUTHORS Arthit Apichottanakul is currently working as lecturer in the Faculty of Technology, Khon Kaen University, Thailand. He completed his PhD in Industrial Engineering from Khon Kaen University, Thailand. His current research interests include intelligent applications, optimization and data science in logistics and supply chain management.

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Clustering retail products based on customer behaviour

Cited by 51 publications

References 23 publications

Application of costing methods: A case study in a retailer in the Triângulo Mineiro

Application of costing methods: A case study in a retailer in the Triângulo Mineiro

Finding Best Possible Number of Clusters using K-Means Algorithm

Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach

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