Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

A Classification Algorithm: Data Mining and Mathematical Model

In this paper, we propose a classification algorithm based on Recency-Frequency-Monetary (RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the members. Finally, some marketing strategies for different classes of members are given according to the classification results.

New fuzzy RFMD model. A method to identify new customer profiles due to an increase in online activity. Case study of a retail business

Gaining customer loyalty has become one of the main objectives of all companies. Retailers, especially the online ones, have the advantage of knowing their customers’ historical purchase data, which provides them with an understanding of the customers’ buying patterns. A widely-used tool in strategic marketing and customer loyalty is segmentation based on the traditional Recency, Frequency and Monetary (RFM) model. Subsequently, the fuzzy RFM model proved to be an improvement on the traditional RFM model. There has been a change in the retail customer profile, with the growth of a new cluster, the “One-Shot Customer”, new customers that buy from a retailer just once and never come back. In response to this change, the fuzzy RFM model has been modified to include a new dimension capturing Length or Duration. This study presents the new fuzzy RFMD model (Recency, Frequency, Monetary and Duration model), which can be used to better identify that new, large group of customers. The paper also provides a case study based on an e-commerce clothing retailer. Its customer database was segmented using the k-means algorithm and the Isolation Forest algorithm was applied to identify and correctly treat possible anomalies. The Customer Lifetime Value and the weights for the RFMD attributes were calculated by applying the Analytic Hierarchy Process (AHP) model. Results reveal the improvement that the weighted fuzzy RFMD model offers to retailers, enabling them to detect the One-Shot Customers and thus optimize their strategic marketing plans.

Towards Understanding the Structure of Subcritical and Transcritical Liquid–Gas Interfaces Using a Tabulated Real Fluid Modeling Approach

A fundamental understanding and simulation of fuel atomization, phase transition, and mixing are among the topics researchers have struggled with for decades. One of the reasons for this is that the accurate, robust, and efficient simulation of fuel jets remains a challenge. In this paper, a tabulated multi-component real-fluid model (RFM) is proposed to overcome most of the limitations and to make real-fluid simulations affordable. Essentially, a fully compressible two-phase flow and a diffuse interface approach are used for the RFM model, which were implemented in the CONVERGE solver. PISO and SIMPLE numerical schemes were modified to account for a highly coupled real-fluid tabulation approach. These new RFM model and numerical schemes were applied to the simulation of different fundamental 1-D, 2-D, and 3-D test cases to better understand the structure of subcritical and transcritical liquid–gas interfaces and to reveal the hydro-thermodynamic characteristics of multicomponent jet mixing. The simulation of a classical cryogenic injection of liquid nitrogen coaxially with a hot hydrogen jet is performed using thermodynamic tables generated by two different equations of state: Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK). The numerical results are finally compared with available experimental data and published numerical studies with satisfactory agreement.

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.

Customer Behavior Analysis using Machine Learning

RFM (Recency, Frequency, Monetary) investigation is a demonstrated showcasing model for conduct based client division. It groups clients dependent on their exchange history – how as of late, how frequently and what amount they buy.RFM helps partition clients into different classes or groups to distinguish clients who will react to advancements and how. This RFM examination depends on a blend of three boundaries. For instance, we can say that individuals who spend the most on items are our best clients. A large portion of us coincide and think about something very similar. In any case, Imagine a scenario in which they were bought just a single time. Or on the other hand an extremely quiet past? Consider the possibility that they are done utilizing our item. would they be able to in any case be viewed as your best clients? Most likely not. Making a decision about client esteem from only one perspective will give you a mistaken report of your client base and their lifetime. That is the reason, the RFM model joins three diverse clients ascribed to rank clients. In the event that they purchased in the recent past, they get higher focus. On the off chance that they purchase ordinarily, they get a higher score. What's more, on the off chance that they spend greater, they get more focus. Thus, we Combine these three scores to make the RFM score. At long last we can portion the client data set into various gatherings dependent on this RFM score.

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.

Customer Segmentation using RFM Model and K-Means Clustering

Today as the competition among marketing companies, retail stores, banks to attract newer customers and maintain the old ones is in its peak, every company is trying to have the customer segmentation approach in order to have upper hand in competition. So Our project is based on such customer clustering method where we have collected, analyzed, processed and visualized the customer’s data and build a data science model which will help in forming clusters or segments of customers using the k-means clustering algorithm and RFM model (Recency Frequency Monetary) for already existing customers. The input dataset we used is UK’s E-commerce dataset from UCI repository for Machine Learning which is based on customer’s purchasing behavioral. At the very simple the customer clusters would be like super customer, intermediate customers, customers on the verge of churning out based on RFM score .Along with this we also have created a web model where an e-commerce startup or e-commerce business analyst can analyze their own customers based on model we created .So using this it will be easy to target customers accordingly and achieve business strength by maintaining good relationship with the customers .