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

1

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

2

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.

3

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.

4

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

5

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.

6

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”.

7

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

8

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.

9

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

10

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

11

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

12

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

13

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

14

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.

15