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K nowledge E ngineering. Develop a Personalized Service Platform With Automatic Customer Catagorization Capability To Enhance Customer Satisfaction And Loyal Customer Retention. 9534533 陳孟鈺 、 9634521吳昌儒 National Tsing Hua University (NTHU), Industrial Engineering - PowerPoint PPT Presentation
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Knowledge Engineering
Develop a Personalized Service Platform With Automatic Customer Catagorization Capability To Enhance Customer Satisfaction And Loyal Customer Retention
9534533 陳孟鈺 、 9634521 吳昌儒National Tsing Hua University (NTHU), Industrial Engineering
& Engineering Management (IEEM), Taiwan
NTHU IEEM, Taiwan2
Outline Introduction
Background Current Services Model
Research Objectives System Framework and Customer
Categorization Method Case Example and Experiment of Customer
Analysis Conclusion
NTHU IEEM, Taiwan4
Current Services Model(1/1)
訂位服務 迎賓帶位 點菜服務餐前服務
餐中服務飲料服務
上菜服務餐後服務
買單送客
客戶1. 我要點啥菜2. 今天情人節ㄝ3. 這個服務生好笨點菜服務服務生1. 要幫忙推薦嗎 ?2. 今天哪道菜不錯 ?3. 找最貴那道好了
NTHU IEEM, Taiwan5
Outline Introduction Research Objectives
Research Objectives
System Framework and Customer Categorization Method
Case Example and Experiment of Customer Analysis
Conclusion
NTHU IEEM, Taiwan6
Research Objectives (1/1) 建構智慧型顧客服務平台
顧客過去消費記錄以及其個人資料之收集 提供服務人員辨識顧客並提供顧客服務 提供顧客高效率之客製化服務
提高顧客服務滿意度 依顧客之過去歷史消費記錄,使用類神經分類模組進行顧客之喜好進行顧客分類,然後給予套餐之推薦,以提高顧客之服務滿意度
NTHU IEEM, Taiwan7
Outline Introduction Research Objectives System Framework and Customer
Categorization Method Functional modules of the platform Automatic customer categorization
Case Example and Experiment of Customer Analysis
Conclusion
NTHU IEEM, Taiwan8
Functional modules of the platform(1/4)
System framework
Customer Service Platform
Customer registration and identification module
Customer service and ordering support module
Automatic customer categorization
Knowledge Base
POS System ERP System
Historical Data
Online customer registrationRFID identification technologyPDA ordering enhancement
Customer service guidanceReal-time cooking statusCustomer satisfaction survey
CTI System
NTHU IEEM, Taiwan9
Functional modules of the platform(2/4)
Start
End
Customer registration
Is customer valid?
Application Form
Customer notification
Online Application
Customer categorization
Customer member card preparation
Yes
No
CustomerRegistration
Module
NTHU IEEM, Taiwan10
Functional modules of the platform(3/4)
Start
End
Customer arrivalIs customer a
member?
Customer service
Yes
No
Customer with member
card?
Yes
Customer recognition by identification
number
No
Customer service with
guidance
Customer recognition by card
Automatic customer
categorization
CustomerRecognition
Module
NTHU IEEM, Taiwan11
Functional modules of the platform(4/4)
Start
End
Customer ordering
Update database
Customer service
Customer profile, preference on
PDA
Customer satisfaction survey and profile
update
Is customer a member?
Customer registration
Yes
No
Real time cooking status on PDA
Guidance of customer service
on PDA
CustomerService and Ordering
Module
NTHU IEEM, Taiwan12
Method 1
Automatic customer categorization(1/3)
Output layer
Hidden layer
Input layer
X
菜餚推薦清單1. 影系列套餐2. 一般套餐3. 特選套餐4. 素食套餐
Call Center
性別個性職業
月收入消費金額消費頻率
各餐點消費頻率
性別個性職業
月收入消費金額消費頻率
各餐點消費頻率
NTHU IEEM, Taiwan13
Method 2
Automatic customer categorization(2/3)
累積消費頻率累積消費金額
累積消費頻率累積消費金額
顧客重要性顧客重要性
顧客重要性指標
NTHU IEEM, Taiwan14
Automatic customer categorization(3/3)
Output layer
Hidden layer
Input layer
X
菜餚推薦清單1. 影系列套餐2. 一般套餐3. 特選套餐4. 素食套餐
Call Center
性別個性職業
月收入顧客重要性指標各餐點消費頻率
性別個性職業
月收入顧客重要性指標各餐點消費頻率
NTHU IEEM, Taiwan15
Outline Introduction Research Objectives System Framework and Customer
Categorization Method Case Example and Experiment of Customer
Analysis Case discussion Construct and train the BPN model
Conclusion
NTHU IEEM, Taiwan16
Case discussion(1/1)
來源 :台北某高級日本料理餐廳
資料 : 顧客基本資料
性別 職業 個性 月收入
顧客消費記錄 累積消費金額 個人累積點餐次數 個人各餐點點餐次數
NTHU IEEM, Taiwan17
Construct and train the BPN model(1/9)
Attribute ValueGender 0: Female, 1: Male
Occupation 1: Student, 2: Government Service, 3: Financial Services, 4: Technology Industry 5: Others
Monthly income (NTD)
1: Under $10,000, 2: $10,001 to 50,000, 3: $50,001 to 100,000, 4: Above $100,001
Cumulative expenditure
1: Under 50,000 2: $50,001 to 100,000 4: $100,001 to 150,000 5: Above $150,001
Personality type 1: Quite, 2: Normal, 3: Assertive and outspoken
Preference (outcome)
A: First Class, B: Economic Class, C: Vegetarian Food, D: Seasonal Specialty
NTHU IEEM, Taiwan18
Construct and train the BPN model(2/9)
Confusion matrix
System inferred
Customer classified into
category i
Customer classified into
other categories
ActualCustomer in category i a b
Customer in other categories c d
Recalla
a b
Precision
a
a c
NTHU IEEM, Taiwan19
Construct and train the BPN model(3/9)
Model
Parameters1 2 3 4 5 6 7 8
Training epochs 10 150 40 51 48 47 48 48
Learning rate 0.3 0.3 0.3 0.3 0.3 0.3 0.35 0.26
Momentum 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Method 1
NTHU IEEM, Taiwan20
Construct and train the BPN model(4/9)
68.7%
87.9% 88.5% 88.7% 88.9% 88.7% 87.8% 89.0%
0.0%
10.0%20.0%
30.0%40.0%
50.0%
60.0%70.0%
80.0%90.0%
100.0%
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Method 1
Method 1
NTHU IEEM, Taiwan21
Predicted valueActual value A B C D Precision Recall
A 122 10 15 0 0.782 0.83
B 18 112 14 0 0.783 0.778
C 16 21 150 6 0.824 0.777
D 0 0 3 447 0.987 0.993
Average 0.89 0.89
Construct and train the BPN model(5/9)
Method 1Model 8
NTHU IEEM, Taiwan22
Model
Parameters 1 2 3 4 5 6 7 8
Training time 10 150 40 51 48 47 46 47
Learning rate 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.35
Momentum 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Construct and train the BPN model(6/9)
Method 2
NTHU IEEM, Taiwan23
68.09%
90.79% 90.79% 90.79% 90.79% 91.01% 90.90% 90.15%
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
1 2 3 4 5 6 7 8
Method 2
Construct and train the BPN model(7/9)
Method 2
NTHU IEEM, Taiwan24
Construct and train the BPN model(8/9)
Method 2Model 6
Predicted valueActual value A B C D Precision Recall
A 126 8 8 0 0.829 0.887
B 11 113 22 0 0.85 0.774
C 15 12 162 9 0.835 0.818
D 0 0 2 446 0.98 0.996
Average 0.91 0.91
NTHU IEEM, Taiwan25
Construct and train the BPN model(9/9)
Method Precision Recall1 0.89 0.89
2 0.91 0.91
3-1 0.84 0.84
3-2 0.9 0.9
NTHU IEEM, Taiwan26
Outline Introduction Research Objectives System Framework and Customer
Categorization Method Case Example and Experiment of Customer
Analysis Conclusion
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