Knowledge discovery on RFM model using Bernoulli sequence

Yeh, I-Cheng; Yang, King-Jang; Ting, Tao-Ming

doi:10.1016/j.eswa.2008.07.018

Cited by 255 publications

(109 citation statements)

References 7 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…Yeh et al [27] included two additional parameters, time since first purchase and churn probability, to model the likelihood that a customer will buy next time. Their research used a blood transfusion service for empirical analysis and the results showed greater predictive accuracy than using single RFM traditional approaches.…”

Section: Introductionmentioning

confidence: 99%

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Moro

Cortez

Rita

2014

Neural Comput & Applic

View full text Add to dashboard Cite

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell longterm deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, under a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve predictive performance without even having to ask for more information to the companies they serve.

show abstract

Section: Introductionmentioning

confidence: 99%

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Moro

Cortez

Rita

2014

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…In this paper, the model accuracy(A) is calculated by the total count of tags in training set and the total count of tags in testing set located in an interval [6]. m1= the tag set whose TEI score located in [75,100] belongs to the training set and testing set at the same time.…”

Section: Text Messagesmentioning

confidence: 99%

Study on Life Tag Value Evaluation Based on the Improved RFM Model

Jiao¹,

Gao²

2016

dtetr

View full text Add to dashboard Cite

Abstract. Various needs of customers' life are divided into 1068 tags in the life tag system. The visit to each tag reflects its value indirectly. As a result, how to find these higher-value tags is an urgent problem to be solved for marketing strategy. This paper gives a brief description of the life-tag value and its construction based on an improved RFM model, which introduced the temporal weighting and took multi-dimensional visiting record of tags as analytic target. Experimental results show that the higher integrated score of one tag, the higher the value, with up to 58.2% percent accuracy.

show abstract

“…This is a traditional classification problem [8]. The result of the classification was used as a target group in a marketing project.…”

Section: Application Of Classification Problemmentioning

confidence: 99%

A knowledge Conversion Tool for Expert Systems

Kim¹

2011

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

Most of expert systems use the text-oriented knowledge bases. However, knowledge management using the knowledge bases is considered as a huge burden to the knowledge workers because it includes some troublesome works. It includes chasing and/or checking activities on Consistency, Redundancy, Circulation, and Refinement of the knowledge. In those cases, we consider that they could reduce the burdens by using relational database management systems-based knowledge management infrastructure and convert the knowledge into one of easy forms human can understand. Furthermore they could concentrate on the knowledge itself with the support of the systems. To meet the expectations, in this study, we have tried to develop a general-purposed knowledge conversion tool for expert systems. Especially, this study is focused on the knowledge conversions among text-oriented knowledge base, relational database knowledge base, and decision tree.

show abstract

Knowledge discovery on RFM model using Bernoulli sequence

Cited by 255 publications

References 7 publications

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Study on Life Tag Value Evaluation Based on the Improved RFM Model

A knowledge Conversion Tool for Expert Systems

Contact Info

Product

Resources

About