K nowledge E ngineering

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, Taiwan3

Background (1/1) 餐飲服務業競爭激烈

餐飲資訊取得迅速 業界競爭激烈顧客選擇多

客戶忠誠度低 業者不易掌握顧客消費習性 服務無法滿足顧客需求

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

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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

NTHU IEEM, Taiwan27

Conclusion 類神經分類模式結論 類神經模組未來發展

NTHU IEEM, Taiwan28

The End

NO Q &A , and thank you for your listening.