1. Analytic CRM for Virtual Entertainment Service
Group 2
Elson, Ronald, Selby, Tony, Torre

2. Agenda Business/Service Model Introduction
Analytic CRM System Schema Design
Data Analysis
Conclusion

3. Business Model Introduction
Purchasing Scenario

4. Business Model Introduction
E-Ticket Usage Scenario
E-Ticket Sensor

5. VTS Process Model Analysis

6. Sales Force Automation
VTS Service Model
Done in the mid-term
Focus on the following
Presentation
The Needs of Customer C R M
IVR
Customer Service
Management
Data Mining
Data Warehousing
CTI
Sales Force Automation
B2C

7. Analytic Process 1 Data Schema Design 2 Collection Data 3 Apply Data
Analyses
Methods 4
Review the
Result

8. Data Schema Design Products (Show) information Product Pricing Info
Analysis
Target
Customer Info

9. Data analyze process
In our data analyze process, we use statistic method to test customer attributes, find out which attribute is really important. By using neuro network method, we adopt Fuzzy ART model to forecast customer’s RFM type. In addition, we conduct correlation analyze to understand whether there is any relationship between our service. Then, we use these information to develop marketing strategies. After carrying out the strategy, we can do follow-up survey and provide information feedback to revise our data, and do better analyze. All of these form a continuously improve cycle.

10. Customer information
The figure is total customer’s data collected in a time period, include customer ID, name, birthday, gender, , phone number, income etc.

11. Population ration figure
When we do some simple population statistics, we find an interesting situation:
For all of our customers in five cities, hsingchu has the most percentage.
Therefore, we want to pay more attention on it, do more detail analysis to understand why people there use our service. And hope to apply the survey result to other cities in the future.

12. Customer information (Hsingchu)
According to the reasons mentioned above, we pick up customer’s data who live in Hsingchu.
Include customer ID, name, birthday, gender, , phone number, income, job etc.

13. Job ratio figure
Base on the customer’s data, we can analyze their job composing.
And we find that most of our customer in Hisingchu are engineers and students.

14. Gender ratio figure
From the data, we can see most of our customers are male,

15. Salary distribution figure
Here shows the customers’ salary distribution.
Our customer’s salary uniform distribute in each group.

16. Age distribution figure
In the age distribution figure, we can see most people concentrate in the left side.
This situation means that most of our customer are young people.
Next step we’ll use the RFM index to test and verify customer’s important attribute.

17. Customer RFM information
This figure shows the customer’s RFM table, include many columns such as recency, frequency, monetary, and other related data.

18. RFM Definition
If we want to do any kind of data analyze, we have to transfer the data into numeric form.
In our case, frequency and monetary is already numeric data, so we only have to transfer recency data.
We define the settlement time is two thousand four, twelve. Therefore If recency month is November, it’ll be transferred to one; if recency month is May, it’ll be transferred to seven.

19. Frequency VS. Age
Then, in order to test which attribute is important, we conduct one-way ANOVA (Analysis-of-Varinnce ) analysis. We use a commercial software “minitab” to conduct this test. Take Age as a factor, frequency as the response, and set alpha as 0.1. We can obtain a result table. under the circumstances, p-value is zero obviously smaller than alpha, so we conclude that age significantly affects the frequency.

20. Normality test
When conducting statistical test, there is a basic hypothesis that residuals should be structureless. If not, it means the test result is unreliable. Therefore, we need to do residuals’ test, to see whether the residuals is normal., Also, we use “minitab” to conduct Normality test. Because the p-value is 0.053, which is smaller than alpha, so we conclude that residuals is normal.

21. Recency VS. Age
We following the same process to test each attribute. P-value is 0.797, bigger than alpha, so we conclude that age doesn’t significantly affects the recency.

22. Monetary VS. Age
P-value is 0.052, so we reject H0 and conclude that age significantly affects the monetary.

23. Statistical test result
All of the attributes doesn’t affect the recency
Salary doesn’t significantly affects the frequency
After conducting twelve times test, we obtain the result of statistic test table like above shows.
We can see that all attributes have no affect on recency. Salary doesn’t significantly affects the frequency.

24. Annual Statistical Data
We try to use the customer call annual data to cluster the customers. This is the annual statistical data of customer call. It include five columns. These columns will be our input to segment the customer

25. Fuzzy ART Method Analog or binary input patterns.
Normalize input data (0~1).
Using Fuzzy ART to cluster the customers.
Tell apart each customer group belonging to which RFM type at next year.
Loyal customer
input
Average Grades By Customer
Average Service Time (min)
Average Queue Time (sec)
Question Solved Rate
Abandon Rate
Vulnerable customer
New customer

26. Correlation analysis
Eg. Ronald bought 2 movie tickets, 1 baseball game ticket and 1 concert ticket.
1837/4208

27. Marketing strategy Tickets package strategy Discount strategy
Target promotion strategy

28. Correlation analysis (After Marketing )

29. Analysis Conclusion Recency analysis Frequency analysis
Monetary analysis
Cross-sell analysis
Customer segment
Forecast (prediction) analysis
We use anova to analyze the RFM, we can understand which attribute will influence RFM performance ,and focus these attribute to do the market strategies. And according to the correlation analysis ,we can know whether there is any relationship exiting ,we can do the cross sell, for example when we see a relationship exiting between sport game and movie, we can sell a package tickets including movie and sport game tickets. At last we use the data of customer call to segment the type of customer , we can also use these weights to forecast other customer.

30. Conclusion
The virtual tickets service model provides an easy way for people to purchase the entertainment tickets than current system
The virtual ticket service is a new service mode
Smart mobile device
Smart playbill
Web service system
Customer relation system

31. Conclusions cont
We present the idea of the virtual ticket service system
The customer supporting service design.