Fig 1.
Top level layout of the proposed LRFMV model.
Fig 2.
Barchart of sales by category based on order-dates.
Table 1.
Determined Length (L).
Table 2.
Determined Recency (R).
Table 3.
Determined Frequency (F).
Table 4.
Determined Monetary (M).
Table 5.
Determined Volume (V).
Fig 3.
Silhouette score for the LRFMV model.
Fig 4.
Cumulative explained variance for the LRFMV model.
Fig 5.
PCA on the proposed dataset.
Fig 6.
K-means clusters over PCA components for LRFMV model.
Fig 7.
Heatmap for LRFMV model.
Fig 8.
Profit analysis for RFM, LRFM and LRFMV models using K-Medoids algorithm.
Fig 9.
Profit analysis for RFM, LRFM and LRFMV model using Mini Batch K-means algorithm.
Fig 10.
Profit analysis for RFM, LRFM and LRFMV models using standard K-means algorithm.
Fig 11.
Volume-profit relationship of LRFMV model for K-Medoids algorithm.
Fig 12.
Volume-profit relationship of LRFMV model for Mini Batch K-Means Algorithm.
Fig 13.
Volume-profit relationship of LRFMV model for K-Means algorithm.
Fig 14.
Profit of each cluster in LRFMV model.
Fig 15.
Number of customers (%) in each cluster for LRFMV model.
Table 6.
Revenue generation for each cluster and their cost to serve.
Table 7.
Comparison amongst the avg of L, R, F, M, V with each cluster.
Table 8.
Analysis of profit for each cluster and identifying potential customer segment using customer profitability matrix.
Table 9.
Customer type analysis and target audience identification for each cluster.