A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Predicting the loss to follow-up (LTFU) of HIV/AIDS patients in China using a recency-frequency (RF) model

BACKGROUND

We constructed a recency-frequency (RF) model for predicting the loss to follow-up (LTFU) in HIV/AIDS patients in China.

METHODS

Data on HIV/AIDS outpatients in the research unit from 1 August 2009 to 30 September 2020 and from 1 October to 31 December 2020 were exported as the observation and prediction datasets, respectively. The classic recency-frequency-monetary (RFM) model was expanded into RFm, RF, RFL and RFmL models. In the observation dataset, the best predictive model was obtained using k-means clustering and C5.0 verification. Then, two rounds of k-means modelling were performed on the best model: data with R ≤ 6 months were retained, randomly divided into a training set (70%) and a testing set (30%) and used to perform the second round of modelling to subdivide the churn and non-churn patients. Next, an ANN algorithm was used to predict LTFU, and the confusion matrix with prediction datasets was constructed.

RESULTS

The observation and prediction datasets included 16 949 and 10 748 samples, respectively. The RF model with three clusters and a quality of 0.82 was the best predictive model. From the observation set, 13 799 samples were retained, and the model accuracy was 100% on the training and testing sets. These 13 799 samples were subdivided into 1563 samples of churn patients and 12 216 samples of non-churn patients. The accuracy of ANN prediction was 99.89%. The accuracy and precision of the confusion matrix were 85.41% and 99.76%, respectively.

CONCLUSION

The RF model is effective in predicting the LTFU of HIV/AIDS patients in China and preventing its occurrence.

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.

An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers

(1) This study aims to predict the youth customers’ defection in retail banking. The sample comprised 602 young adult bank customers. (2) The study applied Machine learning techniques, including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factors to churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88% validated the findings. (4) Customer retention is one of the critical success factors for organizations so as to enhance the business value. It is imperative for banks to predict the drivers of churn among their young adult customers so as to create and deliver proactive enable quality services.

RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.