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

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

Business Model Introduction Purchasing Scenario

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

VTS Process Model Analysis

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

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

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

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.

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

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.

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, email, phone number, income, job etc.

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.

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

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

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.

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

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.

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.

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.

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.

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

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.

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

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

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

Marketing strategy Tickets package strategy Discount strategy Target promotion strategy

Correlation analysis (After Marketing )

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.

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

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