1. CRM System for 3D Apparel Design Service Group Members: David(d937801)Wendy (g936456) Michelle (g936361)Jim (g933829) Magic (g933842)

2. 2/37 Outline 1.Case Introduction 1.1 Motivation 1.1 Motivation 1.2 Behavior Model 1.2 Behavior Model 1.3 Service Model 1.3 Service Model 1.4 IVR Process 1.4 IVR Process 2.Implementation 3.Data Analysis & Data Mining 4.Conclusion

3. 3/37 1.1 Motivation With the improvement of 3D measurementand technique, a 3D body scanner walks into our life.With the improvement of 3D measurement and technique, a 3D body scanner walks into our life. To meet customer’s individual needs.To meet customer’s individual needs. Special purposes for particular persons. (i.e. sports, female underwear)Special purposes for particular persons. (i.e. sports, female underwear) Case Introduction Implementation Data Analysis & Data Mining Conclusion

4. 4/37 1.2 Behavior Model Case Introduction Data Analysis & Data Mining Conclusion Case Introduction Implementation Data Analysis & Data Mining Conclusion

5. 5/37 1.3 Service Model Pre-sales CRM:Pre-sales CRM: –The purpose of this service –How this service is performed –Enter the zip code for more retailers’ information Case Introduction Data Analysis & Data Mining Conclusion Sales CRM:Sales CRM: –On-line shopping After-sales CRM:After-sales CRM: –Order query –Payment query –Modification –Complaint Second-hand market CRM:Second-hand market CRM: –Offer an platform with 3D techniques Conclusion Case Introduction Implementation Data Analysis & Data Mining Case Introduction Implementation Data Analysis & Data Mining Conclusion

6. 6/37 1.4 IVR Process Case Introduction Data Analysis & Data Mining Conclusion Case Introduction Implementation Data Analysis & Data Mining Case Introduction Implementation Data Analysis & Data Mining Conclusion

7. 7/37 Outline 1.Case Introduction 2.Implementation 2.1 CTI Implementation 2.1 CTI Implementation 2.2 Web Implementation 2.2 Web Implementation 3.Data Analysis & Data Mining 4.Conclusion

8. 8/37 2.1 CTI Implementation Group settingGroup setting Case Introduction Implementation Data Analysis & Data Mining Conclusion

9. 9/37 2.1 CTI Implementation Extension setting for daytime.Extension setting for daytime. Case Introduction Implementation Data Analysis & Data Mining Conclusion

10. 10/37 2.1 CTI Implementation Function key setting for daytime.Function key setting for daytime. Case Introduction Implementation Data Analysis & Data Mining Conclusion

11. 11/37 2.1 CTI Implementation Busy or nobody process setting for daytime.Busy or nobody process setting for daytime. Case Introduction Implementation Data Analysis & Data Mining Conclusion

12. 12/37 2.1 CTI Implementation IVR setting for daytime.IVR setting for daytime. Case Introduction Implementation Data Analysis & Data Mining Conclusion

13. 13/37 2.2 Web Implementation User managementUser management Case Introduction Implementation Data Analysis & Data Mining Conclusion Administrator Agents Customers

14. 14/37 2.2 Web Implementation Historical inbound call listHistorical inbound call list Case Introduction Implementation Data Analysis & Data Mining Conclusion Test example

15. 15/37 2.2 Web Implementation ScreenPopScreenPop Case Introduction Implementation Data Analysis & Data Mining Conclusion Customer data Classify the calling reason Service agent Remarks

16. 16/37 2.2 Web Implementation Online chat functionOnline chat function Case Introduction Implementation Data Analysis & Data Mining Conclusion Dialogue frame Type messages

17. 17/37 Outline 1.Case Introduction 2.Implementation 3.Data Analysis & Data Mining 3.1 Descriptive Statistics 3.1 Descriptive Statistics 3.2 ANOVA Test 3.2 ANOVA Test 3.3 Neural Network 3.3 Neural Network4.Conclusion

18. 18/37 3.1 Descriptive Statistics Randomly we generated two thousand customers’ data. Customers’ basic data and R,F,M are included.Randomly we generated two thousand customers’ data. Customers’ basic data and R,F,M are included. Case IntroductionImplementation Data Analysis & Data Mining Conclusion

19. 19/37 Attribute - Job JobPopulation Service industry 665 Industrial_worker497 Student468 Teacher_or_Public official_or_Soldier 181 Retired_or_None92 Others91 Farmer6 Case Introduction We randomly generate two thousand customers’ data. Case IntroductionImplementation Data Analysis & Data Mining Conclusion

20. 20/37 Attribute - Salary Case Introduction SalaryPopulation 0~20000567 20000~30000612 30000~50000289 50000~70000286 70000~246 Case IntroductionImplementation Data Analysis & Data Mining Conclusion

21. 21/37 Attribute - Region Case Introduction CityAmount Taipei948 Hsingchu173 Taichung207 Tainan152 Kaohsiung170 Others350 Case IntroductionImplementation Data Analysis & Data Mining Conclusion

22. 22/37 Attribute - Age Case Introduction AgeAmount Age0~1998 Age20~291010 Age30~39712 Age40~180 Case IntroductionImplementation Data Analysis & Data Mining Conclusion

23. 23/37 Summary According to the descriptive statistics data (showed above), we can know who could be our potential customers.According to the descriptive statistics data (showed above), we can know who could be our potential customers. Case Introduction Implementation Data Analysis & Data Mining Conclusion

24. 24/37 3.2 Method 1 - ANOVA Test RFM DefinitionRFM Definition IndexDefinition Recency(R) The last date which custom bought product via 3D apparel design 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. Case IntroductionImplementation Data Analysis & Data Mining Conclusion Recency (date)Recency (calculation) 2005/4/141 2005/2/123 2004/9/109 2005/6/20 2005/4/92

25. 25/37 RFM and loyal customer If a customer with low recency, high frequency,and large monetary, we considered that he is a good customer.If a customer with low recency, high frequency,and large monetary, we considered that he is a good customer. Case IntroductionImplementation Data Analysis & Data Mining Conclusion

26. 26/37 Gender VS. Recency Case IntroductionImplementation Data Analysis & Data Mining Conclusion

27. 27/37 Gender VS. Frequency Case IntroductionImplementation Data Analysis & Data Mining Conclusion

28. 28/37 Gender VS. Monetary Case IntroductionImplementation Data Analysis & Data Mining Conclusion

29. 29/37 Statistical test result Case IntroductionImplementation Data Analysis & Data Mining Conclusion

30. 30/37 Correlation analysis Case IntroductionImplementation Data Analysis & Data Mining Conclusion

31. 31/37 3.3 Method 2 - Neural Network KPI-neural network is used to train the weightsKPI-neural network is used to train the weights –First-time Resolution Rate –Response Time –Hang-up Rate –Average Processing Time –Service Level –Non-available Rate –Waiting Time Case IntroductionImplementation Data Analysis & Data Mining Conclusion

32. 32/37 Method 2 - Neural Network Questionnaire- ranking of customer satisfactionQuestionnaire- ranking of customer satisfaction –Very satisfied 8~10 –Satisfied 6~8 –Middle 4~6 –Unsatisfied 2~4 –Very unsatisfied 0~2 Case IntroductionImplementation Data Analysis & Data Mining Conclusion

33. 33/37 Method 2 - Neural Network Perceptron-weight training processPerceptron-weight training process –Initial weights are randomly selected, and the indicators are positive related to customer satisfaction, therefore weights are between 0~1 –Input training vector X ， the values of the input are between 0~10 –Calculate net= ， for example if the answer for the questionnaire is very satisfied, the value of Θ is 9 –Calculate △ Wi ， △ Wi=n*(net)*(Θ-Xi) –W inew =W iold + △ Wi –Repeat Case IntroductionImplementation Data Analysis & Data Mining Conclusion

34. 34/37 Neural network-Raw Data Case IntroductionImplementation Data Analysis & Data Mining Conclusion

35. 35/37 Neural network-input and results Input: some calculation form low dataInput: some calculation form low data Final output: weight of each vectors.Final output: weight of each vectors. Case IntroductionImplementation Data Analysis & Data Mining Conclusion

36. 36/37 Outline 1.Case Introduction 2.Implementation 3.Data Analysis & Data Mining 4.Conclusion

37. 37/37 4.Conclusion Mass customization becomes the major trend in apparel industryMass customization becomes the major trend in apparel industry Provide a new business model with 3D design techniquesProvide a new business model with 3D design techniques Design a CRM system to collect related and useful dataDesign a CRM system to collect related and useful data Use these data to do data analysis and data miningUse these data to do data analysis and data mining Find the customer segmentationFind the customer segmentation Have more efficient marketing strategiesHave more efficient marketing strategies Case IntroductionImplementation Data Analysis & Data Mining Conclusion