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Predicting CLV direct selling Arsie P Maurico

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Predicting Customer Lifetime Value through Data
Mining Technique in a Direct Selling Company
Arsie P. Mauricio1, John Michael M. Payawal2, Maida A. Dela Cueva3, Venusmar C. Quevedo4
Industrial Engineering Department
Adamson University
Manila, Philippines 1000
[email protected], [email protected], [email protected], [email protected]
Abstract— The Direct Selling Industry in the Philippines is
continuously growing as more people become direct sellers. With
this, the ability of direct selling companies to manage its sellers
will be a challenge. Customer Lifetime Value (CLV), or the
monetary value a customer is expected to contribute to the
company before churning, is one measure that can be used as a
basis for managing customers and for this matter, the
computation of the CLV must be accurate enough to be used
effectively. However, CLV computation that is specific only for
direct selling companies is not yet established. This research used
Data Mining Techniques, specifically Binomial Logistic
Regression Analysis and Multilayer Perceptron Neural Network
to develop a model that can predict CLV based on historical
customer’s transaction and demographic data. Through
Binomial Logistic Regression Analysis, the direct seller’s average
Service Lifetime was found to be 12 years and the significant
factors that affects customer churn was determined as well.
Through Multiple Linear Regression Analysis the significant
factors that affect customer profit contribution was identified.
Markov Chain Analysis was then used to establish possible
customer states and a state transition probability matrix. Finally,
Multilayer Perceptron Neural Network with 1 hidden layer was
used to establish a neural network predictive model. The results
are used to develop the final model, which was based on the
Present Worth formula. The resulting model has a hold out
relative error of 0.018, which indicates a good predictive
accuracy of the equation. The model can be used by direct selling
companies to help them manage their customers more effectively.
Keywords— Selling, Customer Lifetime Value, Data Mining,
Binomial Logistic Regression, Multilayer Perceptron Neural
Network, Multiple Linear Regression, Markov Chain
I. INTRODUCTION
Customer Lifetime Value (CLV) is one of the core
tools in Customer Relationship Management. Customer
Lifetime Value is the monetary net present value of the profit
contribution of the customer throughout his lifetime or service
length with the company. It can be used to determine which
customer segments are the most profitable (high value) and
which are not (low value) by predicting how much profit will
the customer contribute to the company in the future.
CLV model in each business differs, as the behaviour
of the customers in each of the different business varies.
Different CLV models are proposed by several researchers for
specific industries in the past years but the Direct Selling
Industry was not tapped yet.
This study created a CLV model, based on Data
Mining Models, to be applied on the Direct Selling Industry
with the direct sellers as its Customers. The researchers
conducted the study on a leading direct selling company in the
Philippines that sells fashion items. Three (3) year customer
transaction and demographic data were collected and are
subjected to Data Mining Techniques.
In this study, we aim (a) to identify the significant
factors that affect customer churn in a direct selling company,
(b) to identify the significant factors that affect customer profit
contribution in a direct selling industry, and (c) to develop a
model that can be used to predict customer lifetime value in a
direct selling company.
II. REVIEW OF RELATED LITERATURE AND STUDIES
The Direct Selling Industry of the Philippines
(DSAP) defined Direct Selling as the “face to face selling” of
any product to customers via independent distributors or
sellers [1]. Customer lifetime value (CLV) prediction is
considered the “touchstone for customer relationship
management” [2]. In a direct selling sense, CLV is the
measure of how much a direct seller will contribute to the
company, in monetary terms, before he/she terminates his/her
transaction terminate, say he/she stops being a direct seller or
switched transactions to other direct selling company.
Through the years, CLV prediction models have been
established by several researchers. In the context of banking
sector, Khajvand and Jafar proposed an RFM model for
segmenting customers and a time series method for CLV
prediction [3]. A customer-pyramid approach segmentation
technique and Markov Decision process in determining the
maximum customer lifetime value was also studied [4]. On a
Department Store and Supermarket setting, a weighted RFM
by AHP method and Artificial Neural Network SOM method
for sorting customers and enhancing customer lifetime value
prediction was presented [5]. In a car manufacturing and
maintenance sectors, a study regarding the use of data mining
models, specifically Decision Tree, Logistics Regression,
Markov Chain, and Neural Network, was used to determine
the most applicable method in customer lifetime value
prediction [6]. Based on different literatures present, Data
978-1-5090-1671-6/16/$31.00 ©2016 IEEE
Mining is the most appropriate technique to use in CLV
prediction.
Data Mining is a means of “explaining the past and
predicting the future” through the use of data analysis [7].
Data Mining uses mathematical models and algorithms to
segment data and predict values through searching large
collections of data to detect patterns and trends [8]. It should
be noted that the field where Data Mining is used the most is
CRM [9].
In this paper, the researchers used one type of
Classification Data Mining Technique: Logistic Regression.
Logistic Regression attempts to predict the probability that a
certain binary event will happen. Logistic Regression enables
a statistical analysis to overcome restrictions of linear
regression. For example, in Logistic Regression, the
dependent and independent variables can have no linear
relationship and that the dependent variable and the error
terms need not to be normally distributed [10]. Logistic
Regression is similar to a linear regression except that it
predicts a dichotomous dependent variable instead of a
continuous one. Logistic Regression was used to predict the
service life or the time before churn of each subject customer.
The researchers also used one type of Regression
Data Mining Technique: Artificial Neural Network (ANN).
Neural Network, or more properly known as Artificial Neural
Network, is based on the structure of a mammalian cerebral
cortex where a neuron cell is stimulated by several inputs and
can be activated by an outside process [11]. The overall
process creates a predictive model that can be used even if
linear relationship between variables are not present. Also,
ANN has less assumptions and restrictions for data compared
to other Data Mining Models. In this paper’s context, the
ANN, specifically Multilayer Perceptron, was used to predict
future customer contribution.
Multilayer Perceptron (MLP) is the most famous
ANN architecture as it can approximate any function. MLP
has three distinct layers: the input layer, the hidden layers, and
the output layer. It uses neurons that are capable of processing
data using weights and activation functions, send data to the
succeeding neuron, and propagating the error of preceding
neurons, or back propagation [12]. MLP is an example of
supervised training and its most common training algorithm is
back propagation [13].
III. METHODOLOGY
This research used a quantitative research design to
solve the research questions presented. To solve the research
problems, statistical techniques was applied, and thus required
a quantitative design approach to the problem.
The researchers used a correlational research design
to determine the factors that affect customer churn and profit
contribution.
The first sampling design the researchers used was
convenience sampling, to find the company that the
researchers will conduct its study. The researchers sought the
help of a leading direct selling company (Company A) in the
Philippines to gather data to be used in the study. The
researchers then used stratified random sampling where only
the direct sellers transacting in Company A with at least 3
years in the business will be subjected for the study. The
computed sample size is 51 participants. The 3 years
transaction and demographic data of the 51 randomly chosen
direct sellers were then provided to the researchers which
totals to 4661 transactions.
The researchers used software applications,
specifically, Microsoft Excel and SPSS (Statistical Product
and Service Solutions) to analyze and interpret data.
The following are the research procedure that lead to
answering the research questions.
A. Data Gathering
The researchers collected a 3-year transaction and
demographic data of the randomly selected direct sellers.
B. Estimation of Service Length through Churn Prediction
Possible factors are extracted from the transaction
and demographic data collected and were translated into
variables. These variables were used as independent variables
in the logistic regression analysis.
A churn criteria was established to determine the
churn status of the customer. The churn criteria was
established by the subject company. The churn status was used
as the dependent variable in the logistic regression analysis.
Let Cs be the churn status of the customer where Cs = 0
indicates a not-churn customer and Cs = 1 indicates a churn
customer
The factors to be tested were divided into transaction
and demographic data. The factors were the independent
variable for the test while the Churn Status (Cs) was the
dependent variable. The statistical software SPSS was used to
aid the analysis. The researchers used the following
parameters for the Logistic Regression Analysis in this study:
CI for exp(B) is still 95%, Probability for Stepwise Entry =
0.05 and Removal = 0.10, Classification cutoff = 0.5, and
maximum iterations = 20.
The logistic regression equation model based on the
analysis was then created and the model’s accuracy was
tested. Then, the probability Pci that the churn status is equal
to 1 for each of the selected direct sellers was computed using
the formula
Pci = eYi / (1 + eYi)
(1)
Where Yi is the Y score computed using the logistic
regression generated. The mean churn probability Pc of all the
customers was then computed. The probability of churn in a
logistic regression follows a geometric distribution. The
expected length L of observations before the first churn
customer occurs is expressed by the formula
L = 1 / Pc
(2)
C. Estimation of State Transition Probability
From the transaction and demographic data, factors
that can affect customer profit contribution prediction are
identified. The factors were then translated into variables.
The variables for significance in predicting profit
contribution using multiple linear regression analysis were
tested. The Net Sales was the dependent variable for the
equation. The confidence intervals level under Regression
Coefficients is 95% and the Probability of F under Stepping
Method Criteria was used where Entry = .05 and Removal =
.10.
Using the significant variables identified, the possible
customer states and next states were determined. Based on
past transactions, the transition probabilities are computed and
then a transition probability matrix P was created.
An Identity matrix It that will be used as a matrix
multiplier to indicate the current state of the direct seller was
also created.
D. Prediction of Customer Contribution
Multilayer Perceptron Neural Network was then run
using Net Sales as the dependent variable and the identified
significant factors be the covariates. One hidden layer was
used and the activation function used was identity type. 70%,
20%, and 10% was used as training, test, and hold out set for
the model, respectively.
The researchers used SPSS to perform Multilayer
Perceptron Neural Network. The researchers chose to
randomly assign cases based on relative numbers of cases for
Partition Dataset. The number of hidden layer is one which
has a Hyperbolic Tangent activation function. Under Output
Layer, activation function is Identity and the rescaling of scale
dependent variables is standardized. In this study, the type of
training for MLP is online and the optimization algorithm is
gradient descent. Under SPSS MLP options, user- missing
values is excluded, maximum steps without a decrease in error
is 1, the data to use for computing prediction error and
maximum training epochs is automatic, the maximum training
time is 15 minutes, the minimum relative change in training
error is 0.0001, the minimum relative change in training error
ratio is 0.001, and the maximum cases to store in memory is
1000.
Based on the resulting Network diagram and synaptic
weights, the Neural Network model equation Zt was
established. The model’s accuracy was also tested.
E. CLV Prediction Model
The prevailing discount factor d in the market was
determined. Using the Service Lifetime (L) computed in
equation (2), Initial State Vector (It), Initial State Transition
Probability (P), and Neural Network Model Equation (Zt), a
model that will compute Customer Lifetime Value was then
created.
IV.
DATA AND RESULTS
A. Data Gathering
The transaction data used are Dealer Code,
Transaction Number, Transaction Month, Transaction Day of
the Month, and Transaction Day of the Week, Gross Sales per
Transaction, Discount, Returns, and Net Sales per Transaction.
The demographic data gathered includes the Position, Age,
and Gender of the direct sellers subject for this study.
B. Estimation of Service Length through Churn Prediction
The subject company consider their dealers as
“churn” once they didn’t pay their debts within the allotted
due date (within 40 days of the specified due date).
Based on the Binomial Logistic Regression Analysis,
the Average Expense per Visit and Average Return per Visit
are significant in determining the likelihood that a customer
will churn or not. The Nagelkerke R2 value or the variation of
the dependent variable in the model is 62%. Overall, the full
model can correctly predict 94.1% of the cases.
Based on the average Pci, the model predicted that the
churn probability Pc of a customer is 0.0892. The Estimated
Service Length L of the customers is therefore 12 years. This
means that the average length of time before a customer stops
transaction is 12 years.
C. Estimation of State Transition Probability
Through Multiple Linear Regression Analysis, the
adjusted R² of the regression model is 0.995 with the R² =
.995. This implies that the independent variables explain
99.5% of the variation in the dependent variable, Net Sales.
Position, Frequency of Visit per Month, Expense per Visit,
Discount per Visit, and Return per Visit are statistically
significant since their p-value < .05 (alpha level). The factors
Position, Frequency of Visit per Month, Expense per Visit,
Discount per Visit, and Return per Visit, were subjected to
state transition probabilities. Since Position depends on the
accumulated gross sales he/she made during 2 consecutive
months. Only the variables Average Expense per Visit,
Average Discount per Visit, Average Return per Visit, and
Frequency of Visit per month were used to define customer
states.
This means that there are 4 dimensions for each
State. To reduce the number of possible states, the continuous
variables are further divided into classes. This results to a total
of 672 possible customer states. An Initial State Vector It
needs to be set to indicate at what state the customer is during
time t. The Estimated Service Lifetime of the customers is 12
years which means that there are 672 different It. The
customer states identified will then be subjected to Markov
Chain analysis to determine the customer state transition
probability P.
D. Prediction of Customer Contribution
SPSS software was also used to aid the analysis of
Multilayer Perceptron Neural Network. The significant factors
that affect customer profit contribution are the Independent
Variables while the Net Sales is the Dependent Variable.
A network diagram was generated by the Neural
Network analysis. There are 6 sources of input (Bias, Position,
Frequency of Visit, Ave. Expense per Visit, Ave. Discount per
Visit, and Ave. Return per Visit) which will be activated by
the 4 hidden layer nodes through a Hyperbolic tangent
activation function along with the assigned synaptic weights.
The results of each the 4 hidden nodes are subjected to an
identity function and the output (Net Sales) are generated
through the sum of these resulting values.
Table (1) shows the synaptic weights of each of the
nodes from the input layer to the hidden layer and from hidden
layer to the output layer. From the network diagram, the
formula is generated, shown on equation (3).
(3)
Where ri is the input of factor i, wi is the synaptic weight
from input i to hidden layer, and vj is the synaptic weight from
hidden layer j to the output node.
The model has a Training Relative Error of 0.151, a
Testing Relative Error of 0.014, and a Hold out Relative Error
of 0.018. The values are a good indicator of model accuracy.
E. CLV Prediction
The customer lifetime value is the present worth of
all the predicted customer contribution in the future during
their service lifetime. The predicted Customer Lifetime Value
(CLV) is computed using equation (4)
Table 1
Synaptic Weights for Artificial Neural Network
(4)
Predicted
Predictor
Hidden Layer 1
H(1:1)
H(1:3)
(Bias)
-.121
-.534
-.690
Position
.133
-.043
.024
.085
.037
-.057
.695
.606
.635
1.212
-.246
-.226
-.205
-.132
-.173
Frequency
Input
of Visit
Layer
Expense
per Visit
Discount
per Visit
Return
per Visit
Hidden
H(1:2)
Output
Layer
Net
Sales
(Bias)
1.608
H(1:1)
.262
H(1:2)
1.423
H(1:3)
1.548
Layer
1
Where It is the Identity matrix used in time t, P is the
transition probability matrix, Zt is the predicted customer
contribution at time t, and d is the discount rate in the market.
V. CONCLUSION
With the use of Binomial Logistic Regression, the
researchers identified that the Average Expense per Visit and
Average Return per Visit are the significant factors that affect
customer churn in a direct selling industry. These are the only
factors associated with the likelihood of churn. Also, the
probability that a customer will churn using Binomial Logistic
Regression is 0.0892. The Binomial Logistic Regression
Analysis was found to have a good prediction accuracy in
classifying churn and not-churn customers which is 94.1%.
The model generated is therefore accurate and reliable.
Based on the computed churn probability, the
expected Service Lifetime L of the Customer is found to be
. This means that on average, a customer
will churn within 12 years of transaction with the company.
With the use of Multiple Linear Regression, the
researchers identified that Position, Frequency of Visit per
Month, Expense per Visit, Discount per Visit, and Return per
Visit are the significant factors that affect customer profit
contribution. The Multiple Linear Regression model generated
from the analysis was found to have a good fit of data.
Based on the significant factors determined, a Neural
Network was created to predict customer profit contribution.
Neural Networks are used because of its ease of use but an
accurate and reliable prediction can be obtained. The resulting
Neural Network model, based on a feed forward Multilayer
Perceptron Analysis with 1 hidden layer and a hyperbolic
tangent hidden layer function and an identity output function.
The model has a Training Relative Error of 0.151, a Testing
Relative Error of 0.014, and a Hold out Relative Error of
0.018 which indicates a good prediction model.
The Identity matrix is a 1 x 672 matrix that denotes
the state of a customer at time t. The transition probability
matrix is a 672 x 672 square matrix that shows the probability
that a customer will shift from one state to another for the next
year t. The discount factor is prevailing the interest rate of
money.
The predictive model can be used by the company to
compute for the Customer Lifetime Value of each customers.
[3]
[4]
[5]
VI. RECOMMENDATION
The computed CLV using the developed model
should be used and interpreted correctly by the company.
Otherwise, the CLV will not be useful at all. The company can
also use the results of the significance test to prevent
customers from the likelihood of churn and/or to improve the
customer’s profit contribution. The company’s marketing
strategy can be based on the computed CLV. The company
should also be able to improve the model through the
acquisition of more data, use of other techniques, increase of
sample size, and a larger time scope. For further studies, the
product details such as type of product purchased, quantity of
product purchased and its specifications can also be included
for additional significant factors to be tested. A deeper look at
the factors might also extract data that the Data Mining
techniques might find significant in both customer churn
status and profit contribution.
Determining possible customer states became
extensive with the use of Markov Chain analysis as the said
method accepts only one dimension variables. Multiple
variables are likely to be involved in representing a customer
state when this model is applied to other companies making
the Markov chain model unmanageable (four, in this
research’s case). In order to resolve this problem, System
dynamic or Petri net might be used to model a customer’s
future purchasing behaviour as proposed by Cheng et.al.
Also, the Data Mining techniques used in this study
such as Binomial Logistic Regression, Multiple Linear
Regression, and Multilayer Perceptron Neural Network can be
improved for more accurate results by integrating other data
mining techniques such as Support Vector Machine (SVM).
According to literatures, this type of data mining is a complex
algorithm and time consuming but if used correctly and if
given more time it will also give good prediction accuracy.
A bigger sample size and a larger time scope can be
used as input for the study to increase the accuracy of the
results of the model. The study can also include different
companies with similar line of business to make the result
generalized for the whole direct selling industry under the
garments line of products.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
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