LRFMV: An efficient customer segmentation model for superstores.

The Recency, Frequency, and Monetary model, also known as the RFM model, is a popular and widely used business model for determining beneficial client segments and analyzing profit. It is also recommended and frequently used in superstores to identify customer segments and increase profit margins. Later, the Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. Superstores have a varying number of different products. In RFM and LRFM models, the relationship between profit and purchased quantity has never been investigated. Therefore, this paper proposed an efficient customer segmentation model, namely LRFMV (Length, Recency, Frequency, Monetary and Volume) and studied the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model to show a direct profit-quantity relationship in customer segmentation. The V stands for volume, which is derived by calculating the average number of products purchased by a frequent superstore client in a single day. The data obtained from feature extraction of the LRMFV model is then clustered by using conventional K-means, K-Medoids, and Mini Batch K-means methods. The results obtained from the three algorithms are compared, and the K-means algorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model, and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at profit maximization existed previously, but there was no clear and direct depiction of profit and quantity of sold items. This study applied unsupervised machine learning to investigate the patterns, trends, and correlations between volume and profit. The traits of all the clusters are analyzed by the Customer-Classification Matrix. The LRFMV values, larger or less than the overall average for each cluster, are identified as their traits. The performance of the proposed LRFMV model is compared with the legacy RFM and LRFM customer segmentation models. The outcome shows that the LRFMV model creates precise customer segments with the same number of customers while maintaining a greater profit

Determined Volume (V).

Determined Volume (V).</p

Number of customers (%) in each cluster for LRFMV model.

Number of customers (%) in each cluster for LRFMV model.</p

In vivo analysis of toxic effect of hydrose used in food preparations in Bangladesh

Objective: To evaluate the toxic effect of hydrose used in the molasses preparation in Bangladesh. Methods: Molasses were collected from open markets in different parts of Bangladesh. The presence of hydrose in selected molasses was detected using commercial kit. To evaluate the toxic effect of hydrose, Swiss albino male mice were divided into four groups. Group I was used as control, while Groups II, III and IV received hydrose mixing food (5, 10 and 25 g/kg food), respectively, and these supplementations were continued to the end of the study (16 weeks). Blood was collected from thoracic arteries of the mice under ether anesthesia and then organs were taken. To determine the effect of hydrose on host, blood indices related to liver, heart and kidney dysfunctions were measured. Results: Creatinine and urea levels were significantly (P<0.05) increased in a dose dependent manner in hydrose treated mice, whereas calcium level was significantly decreased in hydrose exposed mice compared to control mice. Histological study of kidney showed the glomeruler inflammation, increased diameter of renal glomeruli and enlargement of proximal tubular lumen of kidneys of mice exposed to hydrose compared to that of control animals. Conclusions: The results of this study indicated that use of hydrose in molasses and other food preparations in Bangladesh may cause kidney impairment

Profit analysis for RFM, LRFM and LRFMV models using standard K-means algorithm.

Profit analysis for RFM, LRFM and LRFMV models using standard K-means algorithm.</p

Heatmap for LRFMV model.

Heatmap for LRFMV model.</p

Volume-profit relationship of LRFMV model for K-Means algorithm.

Volume-profit relationship of LRFMV model for K-Means algorithm.</p

Profit analysis for RFM, LRFM and LRFMV model using Mini Batch K-means algorithm.

Profit analysis for RFM, LRFM and LRFMV model using Mini Batch K-means algorithm.</p

Barchart of sales by category based on order-dates.

Barchart of sales by category based on order-dates.</p