CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE

Customer Lifetime Value, abbreviated as CLV, is a method for companies to measure the benefits of a customer over that person's lifetime. CLV can be calculated by calculating the present value of the amount of money that each customer is expected to earn over their lifetime as a customer. If th...

Full description

Saved in:
Bibliographic Details
Main Author: Nilsen, Andrew
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/71928
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:71928
spelling id-itb.:719282023-02-28T10:26:47ZCALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE Nilsen, Andrew Indonesia Theses CLV, RM, mod-PCF (1,1,2), NLLSRBH, K-means INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/71928 Customer Lifetime Value, abbreviated as CLV, is a method for companies to measure the benefits of a customer over that person's lifetime. CLV can be calculated by calculating the present value of the amount of money that each customer is expected to earn over their lifetime as a customer. If this value is known, then companies can optimize marketing and customer management strategies to increase company profits and identify valuable customers. This value can be estimated through several models. In this thesis, Markov Chain (RM) stochastic process model with discrete time parameters is selected. One of the advantages of this model is that it can measure the profitability of a customer who frequently changes status e.g., from loyal to ex-customer. The number of states is limited to four, namely prospective customers, customers, and two types of former customers, called former customer 1 and former customer 2. The CLV model follows the equation: ????????????=?(????????4×4)????????? ?????=0, with discount factor ????, interest rate ????, transition matrix ????4×4, and reward vector ????? . The matrix ????4×4 shows the probability of a customer being in a state over time, ????? is a vector that shows the money received and spent by the company. The model is the development of Prospect, Consumer, and Former (1,1,2) or PCF (1,1,2) model built by Permana et al. (2016). Furthermore, in this thesis, the following developments or/and updates are made. First, the interest rate ???? is dynamic, modeled by a sinusoidal trigonometric function. Second, the optimal period of CLV calculation is determined. Third, the CLV results for each customer are grouped into 5 levels, namely loyal, tend to be loyal, normal, less loyal, and disloyal. As a case study, interest rate data from May 2009 to July 2016 is taken from the Bank Indonesia website. Based on the data, the parameters of the trigonometric function can be estimated, through the Non-Linear Least Squares Regression method with the help of Basin Hopping optimization (NLLSRBH). In the second case, the data was built through simulation assuming the PCF (1,1,2) model created by Permana et al. Some of the results obtained are 1) the NLLSRBH method provides trigonometric function parameter estimates that can provide accurate predictions for Indonesian interest rate data. 2) determining ????? helps the CLV calculation process because the value obtained is the maximum value of the customer. 3) grouping data from CLV calculation results helps companies to analyze the profile of customers in the company. This thesis is expected to contribute to the development of effective and efficient marketing strategies in increasing company profits. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Customer Lifetime Value, abbreviated as CLV, is a method for companies to measure the benefits of a customer over that person's lifetime. CLV can be calculated by calculating the present value of the amount of money that each customer is expected to earn over their lifetime as a customer. If this value is known, then companies can optimize marketing and customer management strategies to increase company profits and identify valuable customers. This value can be estimated through several models. In this thesis, Markov Chain (RM) stochastic process model with discrete time parameters is selected. One of the advantages of this model is that it can measure the profitability of a customer who frequently changes status e.g., from loyal to ex-customer. The number of states is limited to four, namely prospective customers, customers, and two types of former customers, called former customer 1 and former customer 2. The CLV model follows the equation: ????????????=?(????????4×4)????????? ?????=0, with discount factor ????, interest rate ????, transition matrix ????4×4, and reward vector ????? . The matrix ????4×4 shows the probability of a customer being in a state over time, ????? is a vector that shows the money received and spent by the company. The model is the development of Prospect, Consumer, and Former (1,1,2) or PCF (1,1,2) model built by Permana et al. (2016). Furthermore, in this thesis, the following developments or/and updates are made. First, the interest rate ???? is dynamic, modeled by a sinusoidal trigonometric function. Second, the optimal period of CLV calculation is determined. Third, the CLV results for each customer are grouped into 5 levels, namely loyal, tend to be loyal, normal, less loyal, and disloyal. As a case study, interest rate data from May 2009 to July 2016 is taken from the Bank Indonesia website. Based on the data, the parameters of the trigonometric function can be estimated, through the Non-Linear Least Squares Regression method with the help of Basin Hopping optimization (NLLSRBH). In the second case, the data was built through simulation assuming the PCF (1,1,2) model created by Permana et al. Some of the results obtained are 1) the NLLSRBH method provides trigonometric function parameter estimates that can provide accurate predictions for Indonesian interest rate data. 2) determining ????? helps the CLV calculation process because the value obtained is the maximum value of the customer. 3) grouping data from CLV calculation results helps companies to analyze the profile of customers in the company. This thesis is expected to contribute to the development of effective and efficient marketing strategies in increasing company profits.
format Theses
author Nilsen, Andrew
spellingShingle Nilsen, Andrew
CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
author_facet Nilsen, Andrew
author_sort Nilsen, Andrew
title CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
title_short CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
title_full CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
title_fullStr CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
title_full_unstemmed CALCULATION OF CLV CONSUMER SURVIVAL VALUE THROUGH MARKOV MODEL WITH 4 STATESCASE STUDY: MOD-PCF (1,1,2) MODEL WITH DYNAMIC INTEREST RATE
title_sort calculation of clv consumer survival value through markov model with 4 statescase study: mod-pcf (1,1,2) model with dynamic interest rate
url https://digilib.itb.ac.id/gdl/view/71928
_version_ 1822992333390479360