MODEL STOKASTIK RANTAI MARKOV EMPAT STATUS PADA PENENTUAN NILAI HIDUP PELANGGAN

2017 ◽  
Vol 18 (01) ◽  
pp. 78-85
Author(s):  
Dony Permana

Customer Lifetime Value, familiar as CLV is valuability a customer in marketing system. High CLV has a meaning that the customer will bring in a big return for a firm. CLV is determined by some factors, as retention rate, acquisition rate, some costs, product price, and interest rates. Markov Chain is one of model that used to determine CLV.  In Markov Chain, a customer is assumed some state. Transition inter states are assumed Markovian. Here, we make CLV model using Markov Chain with four states. There are four type of model that have four states. Each type have different transition chart and of course have different probability transition matrix. Here, we describe every type of CLV model using Markov Chain.

1990 ◽  
Vol 4 (3) ◽  
pp. 333-344 ◽  
Author(s):  
Vernon Rego

A simple random algorithm (SRA) is an algorithm whose behavior is governed by a first-order Markov chain. The expected time complexity of an SRA, given its initial state, is essentially the time to absorption of the underlying chain. The standard approach in computing the expected runtime is numerical. Under certain conditions on the probability transition matrix of an SRA, bounds on its expected runtime can be obtained using simple probabilistic arguments. In particular, one can obtain upper and lower (average time) logarithmic bounds for certain algorithms based on SRAs.


2013 ◽  
Vol 411-414 ◽  
pp. 2130-2133
Author(s):  
Qing Hua Chen ◽  
Zhi Liu

This paper introduces a method which uses the gene expression programming algorithm to conduct multivariate nonlinear function modeling, which is applied in the earthquake magnitude prediction. The experiment shows that the prediction accuracy of the GEP is significantly higher than that of the neural network model. Finally, by using the non-delayed effects and stability of the earthquake magnitude prediction data, the state-transition matrix is obtained through the Markov chain, and the state interval and corresponding probability of the GEP model prediction are obtained. In this way, the credibility of the prediction results has been increased.


2017 ◽  
Vol 893 ◽  
pp. 012026
Author(s):  
Dony Permana ◽  
Udjianna S. Pasaribu ◽  
Sapto W. Indratno ◽  
Suprayogi

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