Cell phone Sales & Technology Support Service Center
Lear, Jack, Evan, Eric Department of Industrial Engineering and Engineering Management
National Tsing Hua University Hsinchu (300), Taiwan
ABSTRACT
“Manufacturing oriented” has gone into an end. Instead, “Customer oriented” just begins now. At the same time, it’s very important to build a long-term royal relationship between enterprises and customers. However, enterprises could not improve market share any more by getting new customers only. According to market statistics, the cost of obtaining a new potential customer is about five to eight times of retaining an old one. Therefore, enterprise’s responsibility is to retaining old customers and enforcing their royalty. In order to reach the goal, enterprises start to emphasize on “Customer Relationship Management”. Telecom Industry is the most popular and hit industry in Taiwan right now. Because of over competing, every telecom could provide any better price rate to appeal customers as soon as possible. It destroys royalty between enterprises and customers. Therefore, the most important of all is to strengthen customers’ royalty. After interview of the telecom, the case research shows how “Customer Relationship Management” has been implemented.
In our research, we proposed a Cell phone Sales & Technology Support Service Center. This system will not only sell the product but also supply repairing service for customers. For the diversity requirement of cell phone in addition, the customers really want to know more information about the cell phone. We use descriptive statistics to find the potential customer, use RFM model to obtain customer segmentations, use ANOVA Test to analyze the relationship between RFM & customer attribute, and use Neural Network to predict how long customers change their cell phone. The CRM system helps us to have more efficient marketing strategies, to improve customer satisfaction, and to improve customer retention. Keywords: Cell phone, CRM
1. CASE INTRODUCTION
Opened the liberalization along with the telecommunication market after 1997, telecommunication industry competition day by day intense, in under the high liberalized competition result, in the short ten years, the Taiwan mobile phone user number growth scope was astonishing. In the recent years, increasing numbers of the enterprises are investing in customer service implementation strategies and practices. The increase or lose customers is normal during the enterprise development. Depend on the research that the cost of finding a new customer is much more than keeping the old ones. It goes without saying that CRM is very important. The core is that we should change the sales model from transaction-oriented into customer-oriented. For example, the call center is thus to be more emphasized. From the early way of waiting customer inbound calls to the more proactive outbound call, the enterprise can hold them together
and raise the customer satisfaction. The call center combines information technology such as telephone, computer and Internet. In other words, it is a kind of system consists of strategy, people, process and technology that integrate the whole resource of the enterprise.
Hence the call center plays an important role in an interactive channel between the enterprises and their customers. The importance of a call center has been raised to a strategic level of the modern enterprises. The original purpose of a cell phone service center is to resolve the breakdown of products they have sold, however, along with the huge calls will import the IVR system that can transfer inbound calls to the right person. It is reported that the former three questions asked by Customers are the majority. They always ask where I can buy the cell phone? If the product goes wrong, can I find the repairing point? How to use the distinctive functions….etc. We therefore try to setup a prototype CTI system about sales and technology support to provide an easier way for customers who can buy cell phone or asking for
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help. This system will not only sell the product but also supply repairing service for customers. When customers call, they may want to buy cell phone collocated with phone number or just want to know where they can find the repairing service point. For the diversity requirement of cell phone in addition, the customers really want to know more information about the cell phone. The call center can classify the different cluster and suggest the suitable one for them.
1.1 SYSTEM ANALYSIS
For cell phone products, customers usually need some service including: (1) Product Information Inquiry. (2) Sales Service. (3) Maintenance and After Sales Service. This system intends to provide a Call Center to integrate customer requirement. In this call center, customers only need to call the Call Center, and then they will obtain service they need. We illustrate the services which the system provides within three stages (Pre-sales, Sales and After-sales): (1) Pre-sales:
Customers can call the Call Center to obtain the product information. They can inquire the product information by function, price level or style etc. For this reason, customers can select products which meet their requirements through this inquire function. Besides, the Call Center provides the inquire about sales points, so customers can go to the sales points to view the physical cell phone.
(2) Sales:
When customers call the Call Center, their calls will be transferred to salesperson directly. Salesperson will provide the sales service for the customers. Moreover, salesperson provides some related information, such as delivery date and delivery place etc to the customers.
(3) After sales:
Call Center provides customers with technical support service which includes troubleshooting, function inquiry and maintenance point inquiry. In addition, customers can also inquire the maintenance progress, price and some other information when their cell phones are maintained.
Figure.1 Architecture of system requirement
The Service Process of our Call center is shown
in Figure2 and describe below: 1. Customers call the Call Center 2. Call Center provides service items for
customers 3. Customers select the service type
according to the voice service item from Call Center
4. (a)If the customer service is to inquire the information only, then Call center will provide the information which they need by the voice service. If the information is enough for the customers, then the service is completed. (b)If agent service is needed for customers, the Call Center will transfer the call to the corresponding agent.
5. The agents will provide the one-to-on service to complete the customer service.
Figure.2 Service Process of our call center
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2. CRM SYSTEM
Oh~ I need
????
Cell phone Purchase Problems
Cell phone Repair Problems
Cell phone Technical Support
2.1 SYSTEMS DESIGN AND ANALYSIS
The digital cell phone service model provides
an easy way for people to purchase cell phone or repair cell phone than current system by integration with several modern technologies. But a service model will not work if there doesn’t exist a supporting system to support customers’ needs. In order to increase the customer satisfaction the call center will play an important role to deal with customers needs about the cellphone service. The customer’s need can be defined into three major category which shown in Figure 3.
Figure.3 The customer needs for Cell phone customer
service.
Cell phone repair problems: The customer may encounter some problems
when using cell phone. Most of time people will send back to maintain stores, but sometimes customer want to know which time can take back or repair status. People will always concern about how the fee will pay or upgrade and component fee of cell phone which they want to buy.
The three major categories are described as
following: Cell phone purchase problems:
Most of time people will purchase cell phone in stores, but sometime they want to buy cell phone on-line or other ways. Especially for old people, they don’t know how to use internet or hard to go to stores, it is convenient for them to buy cell phone by telephone.
Cell phone technical support: Sometimes people may want to know how to
use their cell phone or some simple problems of cell phone. They can use our system for technical support.
The three categories can be handled by the CTI process flow shown in Figure 4.
Figure.4 The cell phone service for CTI process.
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Since the CTI process was defined in Figure 4 the
2.2 IMPLEMENTATION
epartment Group:
ll phone Sales & Technology Supp
following section will describe the implementation detail of the logical process into the CRM system.
2.2.1 CTI SETTING
DBecause the Ce
ort Service system has to provide a 24-hours service, the day-time setting groups and the night-time setting groups will be the same. Both of them have four groups to response for different service from customers. The group managing Figure is shown in Figure 5. Group03 is directly selling the mobile phone to the customer, and Group05 response to the problems about querying maintain status, maintenance fee and upgrade cost when customers use the Cell phone Sales & Technology Support Service system.Group07 supports all problems about technique, such that introduction the mobile function and simple troubleshooting. GroupOPERA takes care of the problem which can not be solved by the above three groups. According to one to one contact, the agent will request quickly to the customer’s problem.
Figure.5 Department group
Just like the above paragraph describe, the
system
Exten n Setting: an setup extension information
includ
have to provide an all-day service, so the day-time setup will be the same with the night-time setup. Thus, the detail setting has been omitted.
sioThe system cing extension number, agent ID and name, and
belonging group and name. As Figure 6 shown, the system has four groups. There is one agent in the GroupOPERA for operator, two agents in the Group07 for support and three agents in Group05 for repair.
Figure.6 Extension setting
Function Ke
The system can set up the function key for re 7 shown below, the agents
can p
y Setting:
agents to use. Like Figuress “0” to connect to the operator directly, press
“9” to dial outbound calls and press “1” to listen to the messages that customer leaves. The system can also allow transferring to another extension line. The user just has to press “flash” first, then dial other extension number.
Figure.7 Function key setting
Busy or No
When the extensi n is busy or nobody t up some process for
users
body Response: o
response, the system can seto obey. Like Figure 8 shows below, when
nobody responses lasting 10 seconds, users can press “1” to leave their messages or press “2” to continue waiting or press "0” to connect to the operator or press ”4” to hang up. The process rules in the situation of nobody responding and busy are the same.
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Figure.8 Busy or nobody response
Outbound Setup:
In the setup, we can determine the extension’s limits. In our system, all extension (201~206) can be allowed to call outside, mobile phone and international phone. The Figure is shown as Figure 9.
Figure.9 Outbound function
Voice Process: The most important part of the setup of the CTI
is to setting the voice process. Like Figure 10 shown, the system’s voice process has to been set according to the IVR Figure. Then when customers call in the Cell phone Sales & Technology Support Service system, they can follow this setting using the system.
2.2.2 WEB IMPLEMENTATION System administrator can manage all member
account include the agents and the customers. He can increase an account like Figure 11 shown below. And he can also query all the member accounts by input some inquiry terms, like Figure 12 shown.
Figure.10 Voice process
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Figure.11 Increase a new member account
Figure.12 Query all member account
Service Record:
We can check the detail service record by use this function as Figure 13.
Figure.13 Service record
3. DATA ANALYSIS AND DATA MINING
3.1 DESCRIPTIVE STATISTICS In our database, we found some characteristics
about our customers. First of all, we have about 80% customers are male, that is an obvious difference between two gender to use our system. Thus, the target in our service should be focus on these potential customers. The Figure 14 shows the proportion.
Female
20%
Male
80%
Figure.14 Gender proportion
And then, we observed that there are about half
customers living in north and center. According the data, we can focus on east and south area. We can set more service and sell points in these areas, thus we can increase more customers. We could pay more attention to where the customers living and provide higher service level by regional service. The Figure 15 shows the region.
South
26%
Center
30%
East
6%
North
38%
Figure.15 Region
Furthermore, there are more than 65%
customers are between 0 to 29 years old in our database. Since most of our customers are young people, the marketing device should target on these young people, and our service should provide more fashion and young styles to promote more purchases as Figure 16 show.
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0~19
29%
20~29
36%
30~39
18%
40~
17%
Figure. 16 Age range
Besides, the salary distribution, it can show us
that our customers perhaps can pay a different
amount of money for purchasing cell phone. For
instance, a customer with 0 to 20,000 salary may not
purchase a $20,000 cell phone, and a customer with
more than 70,000 salary will has a bigger probability
to buy the $30,000 cell phone. The Figure 17 shows
the salary.
0
100
200
300
400
500
600
700
0~20000 20000~30000 30000~50000 50000~70000 70000~
Figure. 17 Histogram for salary
Finally, in our database, we can found that
“W800i” and “W900i” are more popular. There are
more than 51% customers to buy them as Figure 18
showing. And other cell phone are 3%, thus we can to
bring up an idea to plan a promotion to sell those cell
phone, such as special price with phone number etc.
W800i
28%
W900i
23%
K608i
21%
J200i
11%
W810i
8%
P900
6%
other
3%
Figure. 18 Cell phone distribution
3.2 RFM ANALYSIS Segmentation by product usage uses a method
called RFM analysis, which segments customers
based on recency, frequency, and monetary values.
Thus we should define our RFM factors as Table 1
showing.
Table 1 RFM definition Index Definition Recency (R) The last date which custom bought
product through our system. It is counting by month.
Frequency (F) How many times the customer bought product in the system.
Monetary (M) Total amount of money that customer spent in our system.
Customers who reside in segment R↓F↑M↑
can be considered ad loyal ones who are frequent and
big shoppers (Loyal segment). Customers who
belong to segments R↑F↑M↑ or R↑F↓M↓ are
much likely to become vulnerable customers, based
on above-average value in recency (Vulnerable
segment). Segments F↓R↓M↓ can represent new
customers, given below-average values in recency
and frequency (Newcomer segment). According these
rules, we analyze our data and segment our customer
into three segmentation, including “Newcomer,
Vulnerable, and Loyal”.
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As Figure 19 shows, we can see that 9.1% are
newcomer customers and 26.9% are vulnerable
customers and 15.3% are loyal customers. Then, we
can try to make more strategy of holding on loyal
customers, and keep newcomer customer. We also
can do best to make vulnerable customers into loyal
customers.
Figure.19 The RFM analysis
3.3 ANOVA OF RFM ANALYSIS The customer data in our database have four
attribute (gender, age, area and salary). We want to
use ANOVA to analysis the difference between the
four factors (gender, age, area and salary) according
to the performance of recency or frequency or
monetary.
ANOVA test:
1.Setup the significant level (α)=0.05
2.After calculating, there are existed two statistics
value (P-value and F-value)
3.P-value < α or F-value > Critical value, if one of
the two conditions is existed, it means that the
hypothesis is significant.
3.3.1 GENDER VS. RECENCY
The original data of man and female according
to the performance of recency is been shown in Table
2. And the result of ANOVA test is shown in Figure
20. We can see that the p–value > α , so the
hypothesis is non-significant. In the other words, we
don’t have enough evidence to say that there is a
difference between man and female according to the
performance of recency.
Table 2 Data of Gender
Figure.20 The result of Gender ANOVA test
3.3.2 AGE VS. RECENCY The original data of under 19, 20~29, 30~39
and over 40 according to the performance of recency
is shown in Table 3. And the result of ANOVA test is
shown in Figure 21. We can see that the p–value <α,
so the hypothesis is significant. In the other words,
we have enough evidence to say that there is a
difference between four levels of ages according to
the performance of recency. We can do the further
data analysis to find which level of age is more
contribution in our system.
Because people at different age buy cell phone
for different reason according to their needs: Young
people tend to buy cell phone for fashion while old
people only needs basic functions such as dialing and
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answering. So the result of ANOVA test is
reasonable.
Table 3 Data of Age
Figure.21 The result of Age ANOVA test
3.3.3 AREA VS. RECENCY The original data of North, Center, South and
East according to the performance of receny is shown
in Table 4. And the result of ANOVA test is shown in
Figure 22. We can see that the p–value <α, so the
hypothesis is significant. In the other words, we have
enough evidence to say that there is a difference
between four areas according to the performance of
recency. We can do the further data analysis to find
which area is more contribution in our system.
Because people in the north and center are
more sensitive than people in the east and south area.
They have higher acceptance to information products.
So the result of ANOVA test is reasonable.
Table 4 Data of Area
Figure.22 The result of Area ANOVA test
3.3.4 SALARY VS. RECENCY The original data of four levels of salary
according to the performance of recency is shown in
Table 5. And the result of ANOVA test is shown in
Figure 10. We can see that the p–value <α, so the
hypothesis is significant. In the other words, we have
enough evidence to say that there is a difference
between four levels of salary according to the
performance of recency. We can do the further data
analysis to find which area is more contribution in
our system.
Because higher salary indicates higher
consumption level and more frequent changing cell
phones. So the result of ANOVA test is reasonable.
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Table 5 Data of Salary
Figure.23 The result of Salary ANOVA test
3.3.5 CONCLUSION OF ANOVA TEST
We have four factors (Gender, Age, Area and
Salary) and three performances (Recency, Frequency,
and Monetary). So we must do the ANOVA test
twelve times, but the previous page only lists Gender
vs. Recency, Age vs. Recency, Area vs. Recency and
Salary vs. Recency. We omit the ANOVA of
frequency and monetary because the process is the
same with recency. ANOVA test result is shown in
Table 6.
Table 6 ANOVA test Result
3.4 NEURAL NETWORK 3.4.1 METHODOLOGY
Neural network computing is an approach that
attempts to mimic certain processing capabilities of
the human brain. Since 1980s, the drastic
breakthrough of the computing technology has led to
an increasing amount of neural network research on a
wide variety of business functional applications. It
has been applied across a broad range of industries,
from identifying clusters of valuable customers to
fraudulent credit card transactions. Prediction
produced by neural networks are often referable that
means it has business value. In many cases that is a
more important feature than providing an
explanation.
Neural networks have the ability to learn by
example in much the same way that human experts
gain from experience. Recently research findings
pointed out that neural networks technology could be
successfully used in customer relationship
management. In general, learning is the process by
which the neural network adapts itself to a stimulus,
and eventually it produces a desired response. It is
also a continuous refine process of input stimulus,
when a stimulus appears at the network, the network
recognizes it or it develops an appropriate estimation
mechanism.
Neural networks are good for prediction and
estimation problems. A good problem usually has the
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following characteristics. First, the inputs are well
understood and the output is well understood. Besides,
there should be plenty of examples where both the
inputs and the output are known that will help to train
the network. The process of training the network is
actually the process of adjusting weights inside it to
arrive at the best combination of weights for making
the desired predictions. The network starts with a
random set of weights, so it initially performs very
poorly. However, by reprocessing the training set
over and over and adjusting the internal weights each
time to reduce the overall error, the network
gradually does a better and better job of
approximating the target values in the training set.
When the approximations no longer improve, the
network stops training.
A neural network is only as good as the training
set used to generate it. The model is static and must
be explicitly updated by adding more recent
examples into the training set and retraining the
network (or training a new network) in order to keep
it up-to-date and useful. It is composed of layers and
each layer consists of neurons or processing elements
and connections. The first layer called the input layer
contains neurons that stand for the set of input
variables. The output layer contains neurons that
stand for the output variables. The middle layer
called the hidden layer helps in extracting higher
level features and facilitates generalization.
Connections between nodes have numerical weights
associated with them. Besides, the weights are
adjusted in the training process by repeatedly feeding
examples from the training set.
Another important feature of the artificial
neuron is the activation function. The most common
activation functions are based on the biological
model where the output remains very low until the
combined inputs reach a threshold value. When the
combined inputs reach the threshold, the unit is
activated and the output is high. The activation
function has two parts. The first part is the
combination function that merges all the inputs into a
single value. The most common combination
function is the weighted sum, where each input is
multiplied by its weight and these products are added
together. The second part of the activation function is
the transfer function, which gets its name from the
fact that it transfers the value of the combination
function to the output of the unit. Sigmoid functions
are S-shaped functions, of which the two most
common for neural networks are the logistic and the
hyperbolic tangent.
The major difference between them is the range
of their outputs, between 0 and 1 for the logistic and
between –1 and 1 for the hyperbolic tangent. For
prediction problems, a sigmoid transfer function is
typically used to transform the inputs into outputs.
The sigmoid transfer function is represented by
The range of the transfer function is between 0
and 1. Although we recommend that inputs be in the
range from 0 to 1, this should be taken as a guideline,
not a strict rule. For instance, standardizing variables,
subtracting the mean and dividing by the standard
deviation, is a common transformation on variables.
This results in small enough values to be useful for
neural networks.
The back-propagation algorithm, especially,
has emerged to be the most popular learning
mechanism for prediction and classification problems
in commercial fields so far. There are two important
parameters associated with using the generalized
delta rule. The first is momentum, which refers to the
tendency of the weights inside each unit to change
the “direction” they are heading in. That is, each
weight remembers if it has been getting bigger or
smaller, and momentum tries to keep it going in the
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same direction. A network with high momentum
responds slowly to new training examples that want
to reverse the weights. If momentum is low, then the
weights are allowed to oscillate more freely. On the
other hand, the learning rate controls how quickly the
weights change.
The best approach for the learning rate is to
start big and decrease it slowly as the network is
being trained. Initially, the weights are random, so
large oscillations are useful to get in the vicinity of
the best weights. However, as the network gets closer
to the optimal solution, the learning rate should
decrease so the network can fine-tune to the most
optimal weights.
3.4.2 BACK-PROPAGATION NETWORK
It is not surprising to find that there are many
applications in prediction area, since a neural network
is an appropriate technology for learning, recalling,
classifying and comparing new information with
existing knowledge. The basic version
back-propagation algorithm minimizes the squared
error cost function and uses the three-layer
elementary back-propagation topology. It is also
known as the generalized delta rule. The BP
algorithm is used to train neural networks that contain
multiple layers. At the heart of back propagation are
the following three steps: First, the network gets a
training example and, using the existing weights in
the network, it calculates the output or outputs.
Second, back propagation then calculates the error by
taking the difference between the calculated result
and the expected (actual result). Third, the error is fed
back through the network and the weights are
adjusted to minimize the error, hence the name back
propagation because the errors are sent back through
the network.
The back propagation algorithm measures the
overall error of the network by comparing the values
produced on each training example to the actual value.
It then adjusts the weights of the output layer to
reduce, but not eliminate the error. However, the
algorithm has not finished. It then assigns the blame
to earlier nodes the network and adjusts the weights
connecting those nodes, further reducing overall error.
Back propagation uses a complicated mathematical
procedure that requires taking partial derivatives of
the activation function. The advantages of
back-propagation learning include its ability to store
many more patterns than the number of input
dimensions and its ability to acquire arbitrarily
complex non-linea mappings. Unfortunately it also
has an extremely long training time, which must be
done offline.
3.4.3 ARCHITECTURE OF BP NETWORK
This project used back propagation network
consisting of four nodes in the input layer along with
one hidden layer and has connections to an output
unit in the output layer. Hidden layer is connected
neither to the inputs nor to the output of the network.
Each unit in the hidden layer is typically fully
connected to all the units in the input layer. Since this
network contains standard units, the units in the
hidden layer calculate their output by multiplying the
value of each input by it corresponding weight,
adding these up, and applying the transfer function.
A neural network can have any number of
hidden layers, but in general, one hidden layer is
sufficient. The wider of the layer will make the
greater the capacity of the network to recognize
patterns. This greater capacity has a drawback,
though, because the neural network can memorize
patterns-of-one in the training examples. The goal of
the network is to generalize on the training set, not to
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memorize it. To achieve this, the hidden layer should
not be too wide.
The BP network is composed of an input layer,
an output layer, and one or more hidden layers
located between the input and output layers. The
layers are typically fully connected, with every
neuron in one layer connected to every neuron in an
adjacent layer. The values associated with each input
neuron are fed forward into each neuron in the first
hidden layer. They are then multiplied by an
appropriate weight, summed, and passed through a
transfer function to produce an output. The outputs
from the first hidden layer are then feed forward into
either the next hidden layer or directly into the output
layer in networks with only one hidden layer. The
output layer's output is that of the network. The
number of neurons in the hidden layer is determined
through experimentation.
For any nonlinear problem such as prediction
of stock or commodity prices, the network needs at
least one hidden layer. In addition, the transfer
function should be a nonlinear, continuously
differentiable function that allows the network to
perform nonlinear statistical modeling. The back
propagation learns a predefined set of output example
by using a two phase propagate adapts cycle. After an
input pattern has been applied as a stimulus to first
layer of network units, it is propagated through each
upper layer until an output is generated.
This output pattern is then compared to the
desired output, and an error signal is computed for
each output unit. The signals are then transmitted
backward from the output layer to each unit in the
intermediate layer that contributes directly to the
output. However, each unit in the intermediate layer
receives only a portion of the total error signal, based
roughly on the relative contribution the unit made to
the original output. This process repeats. Layer by
layer, until each unit in the network has received an
error signal that describes its relative contribution to
the total error.
3.4.3 STRUCTURE OF OUR NEURAL NETWORK
The purpose of using neural network approach
in our project is to know how long customers change
their cell phone. Therefore, we use neural model to
predict the interval time that customer will change
their cell phones. There are three layers in our
network which includes one input layer, one hidden
layer and one output layer. There are four input nodes
in the input layer; they are age, gender, monthly
salary , and average purchasing price. Interval time
that customers change their phones is only one output
node in the output layer. We use four customer’s
attributes to predict how long customers change their
cell phone. Besides, there are two hidden nodes in the
hidden layer. The structure of our neural network
model is shown in Figure 24.
Figure.24 Structure of our neural network
3.4.4 TRAINING AND TESTING DATA
Since we need to get the output data which is
historical data of the interval time that customers
change their cell phones, customers should buy cell
phones twice at least; thus we can get the interval
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time. Therefore, we have 3000 data in our database,
but not all of these data can be used as training and
testing data. After sieving out available data from
database, we obtain 1000 training and testing data
(shown in Figure 25); 80% of these data is used for
training, and other 20% for testing. In Figure 25, X is
input variable and Y is output variable of the network
and we explain these variables in detail below:
X1 is Customer’s age
X2 is the monthly salary of customer, and
the unit is NT dollars.
X3 is the average purchasing price of
customer, and the unit is NT dollars
X4 is the Gender. One means that the
customer is male and zero means the
customer is female.
Y1 is the Interval time that customer will
change their cell phone, and the unit is
months.
For example, the customer in the first column
in Figure 24 indicates that a man who is twenty years
old, his monthly salary is 7660, and the average
purchasing price is 5081. From the historical data we
obtain the average interval time that the man changed
cell phone is 24 month.
Figure.25 Training and Testing Data
3.4.5 WEIGHTS ON EACH CONNECTION
After using neural network tool to construct our
model and use the training data to train the model, we
can obtain the weights on each connection in the
network (shown in Figure 26). As we know the
weights on each connection and the transfer function
of each node, we can carry out the prediction.
Figure.26 Weights on each connections
3.4.6 EVALUATING THE MODEL AND PREDICTION
In order to evaluate the accuracy of the model,
we use 10 data to validate this model. From the Table
7, we can see that most of the prediction values are
closer to the actual values, therefore the performance
of the model is acceptable , and the model is suitable
for us to predict how long customer change their cell
phone.
After validating the model and confirm the
model is suitable for us, we use this model to predict
10 interval time that customer change cell phones.
From the result (shown in Table 8), we can know
when customer change cell phone and the result is
useful for salesperson. If they know when customer
change cell phones, they can send the promotion
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information to the customers and try to retain the
customers.
Table 7 Difference between prediction value and
actual values
Table 8 The result of predictions
4. CONCLUSION In our project, we develop a CRM system to
provide the pre-sales service, sales service, and
after-sales service. In the pre-sale stage we provide
product information query function, in the sales stage,
customer can buy cell phones through Call Center,
and in the after-sales stage, we provide maintenance
service. Therefore, we provide an integrated service
of cell phone product. We also Use this CRM system
to collect related and useful data, and use these data
to do data analysis and data mining. On Analytical
CRM, we use descriptive statistics to find the
potential customer, use RFM model to obtain
customer segmentations, use ANOVA Test to analyze
the relationship between RFM & customer attribute,
and use Neural Network to predict how long
customers change their cell phone. The CRM system
helps us to have more efficient marketing strategies,
to improve customer satisfaction, and to improve
customer retention.
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