It's my research as a graduate student and it's been submitted and accepted by the 12th FSKD (International Conference on Fuzzy Systems and Knowledge Discovery).

1. Prediction of Online Consuming
Behavior for Noncontractual Customers
Evidence from a shoe manufacturer
Presenter: Hsieh, Shu-Jeng
Advisor: Bo, Hsiao
Date: 12th, June, 2015
1
預測非合約客戶線上持續消費行為
－以台灣某一鞋業製造公司

2. 2
Outline
• Introduction
• Literature Review
• Research Method
• Empirical Study
• Discussion
• Conclusion

3. 3

4. =
4

5. Structure of Clientele
One-to-one
marketing
Relationship
marketing
Targeted
marketing
Interactive
marketing
5

6. Disaggregability
Environment
Technology
Customer
Life
Value
6

7. 7

8. 8

9. Literature
9

10. Literature
10
Unlimited
areas
Low-establishing
cost
Search
convenience
Information
availability
Geographical
ubiquitousness

11. Literature
11
7% 7.9%4%
2 0 1 4

12. Literature
12
221%

13. Literature
13
2012：231 billion
2017：370 billion

14. Literature
14
2011：53%
2016：58% increase

15. Literature
15

16. Literature
16

17. Literature
17
attitudes
risk
perception
previous
experience
price
consciousness

18. Literature
18

19. Assumptions
 Pr[ ]
!
x
T T
X x e
x
 
 
  0,1,2,...x 
When customers are active, they purchase according to
19

20. Assumptions
Their leave is distributed with
[ ] e T
f 
   
 0 
20

21. Assumptions
The heterogeneity of purchase rates is distributed with
 
 
1
,
r
r
j r e
r

    


, , 0r  
21

22. Assumptions
The heterogeneity of attrition rates is distributed with
 
 
1
,
s
s
k s e
s

    


, , 0s  
22

23. Assumptions
θ: the average dollar volume per re-purchase
Zi: the dollar value of record i, i=1,…, X, of a customer
• The set of Zi is normally distributed with mean θ and variance
• The average dollar spent per purchase across customers is
normally distributed with mean E[θ] and variance .
2
w
2
A
23

24. Assumptions
• Rates λ and μ don’t affect each other.
• The distribution of the average dollar amount is independent of the
distributions of the purchase rate and attrition rate.
24

25. Key formulae
25
  
 
1 1 1 1
1
1 1 1 1
, , , ,t,T
1 , ; ;
, ; ;
r x s
s
P T r s X x
s T T
F a b c z t
r x s t t
T
F a b c z T
t
  
 
 




     
       
      
       
  
       
1a r x s   1 1b s  1 1c r x s     1z y
+y
 



Case 1：α > β

26. Key formulae
26
  
 
2 2 2 2
1
2 2 2 2
, , , ,t,T
1 , ; ;
, ; ;
r x s
r x
P T r s X x
s T T
F a b c z t
r x s t t
T
F a b c z T
t
  
 
 





     
     
             
  
       
Case 2：α < β
2a r x s   2b r x  2 1c r x s     2z y
+y
 




27. Key formulae
27
1
, , , ,t,T 1 1
r x s
s T
P T r s X x
r x s t

  


     
                  
Case 3：α = β

28. Key formulae
28
* *
* * * * *
, ,s, , , , ,
, , , , , , , ,s , ,
E X r X t T T
P Active r s X t T E X r T
 
   
 
 
      

29. Key formulae
29
 
* *
2 2
* *
2 2 2 2
, , , , , , ,
, , , , , , ,A W
A W A W
E V r s X t T T
X
Z E E X r s X t T T
X X
 
 
  
   
 
 
    
              

30. Data processing
30
Identity of customer First transaction Last transaction
77 2013/4/5 2013/4/5
127 2013/4/11 2013/4/11
274 2013/4/28 2013/4/28
393 2013/5/8 2013/5/8
848 2013/6/30 2013/10/12

31. Data processing
31
Identity of customer Period
77 0
127 0
274 0
393 0
848 4

32. Data processing
32
Identity of customer Period
77 12
127 12
274 12
393 11
848 10

33. Data processing
33
Identity of customer Repurchasing number
77 0
127 0
274 0
393 0
848 2

34. Data processing
34
Identity of customer Average repurchasing money
volume
77 NT$1,572.88
127 NT$1,572.88
274 NT$1,572.88
393 NT$1,572.88
848 NT$1,190.00
AVAC VVPC VVAC
NT$1,572.88 NT$89,308.55 NT$1,624,013.72

35. Data processing
35
α β r s
6.52 32.52 1.04 10.38

36. Data processing
36

37. Data processing
37

38. Case I Background
• Online retailing: A shoe manufacturer
• Time period: 2013/3/28－2014/11/30
 Base: A year
 Validation: Subsequent 8 months
• Records: 3,180
 2,222 customers
38

39. Case I
Transactional Numbers
Actual Predicted Accuracy
37 32.45 87.71%
57 57 100.00%
68 75.72 88.65%
86 90.1 95.24%
113 101.2 89.57%
130 109.9 84.52%
141 116.7 82.74%
156 122 78.21%
Aggregate performance
37
57
68
86
113
130
141
156
32
57
76
90
101
110
117
122
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8
Actual Predicted
39

40. Case I Aggregate performance
87.71%
100.00%
88.65%
95.24%
89.57%
84.52% 82.74% 78.21%
1 2 3 4 5 6 7 8
ACCURACY
40

41. Case I
Money Volume
Actual Predicted Accuracy
79486 52918.84 66.58%
104816 92970.10 88.70%
126676 123519.95 97.51%
154144 146994.57 95.36%
198624 165158.46 83.15%
220574 179305.96 81.29%
243173 190394.20 78.30%
267471 199136.39 74.45%
Aggregate performance
79486
104816
126676
154144
198624
220574
243173
267471
52918.84
92970.10
123519.95
146994.57 165158.46
179305.96
190394.20
199136.39
0
50000
100000
150000
200000
250000
300000
1 2 3 4 5 6 7 8
Actual Predicted
41

42. Case II Aggregate performance
66.58%
88.70%
97.51% 95.36%
83.15% 81.29% 78.30% 74.45%
1 2 3 4 5 6 7 8
ACCURACY
42

43. Case I Individual performance
Transactional Numbers
Period Accuracy
1 82.84%
2 82.27%
3 82.02%
4 81.73%
5 81.39%
6 81.07%
7 80.79%
8 80.53%
82.84%
82.27%
82.02%
81.73%
81.39%
81.07%
80.79%
80.53%
79.00%
79.50%
80.00%
80.50%
81.00%
81.50%
82.00%
82.50%
83.00%
83.50%
1 2 3 4 5 6 7 8
43

44. Case I Individual performance
Money Volume
Period Accuracy
1 89.55%
2 88.94%
3 88.61%
4 88.25%
5 87.81%
6 87.43%
7 87.12%
8 86.85%
89.55%
88.94%
88.61%
88.25%
87.81%
87.43%
87.12%
86.85%
85.50%
86.00%
86.50%
87.00%
87.50%
88.00%
88.50%
89.00%
89.50%
90.00%
1 2 3 4 5 6 7 8
44

45. 45
Discussion
6 months
2 years

46. 46
Discussion
years

47. 47
Discussion
Customer

48. 48
Discussion
Parameters

49. 49
Discussion
Time Unit

50. 50
Discussion

51. 51
Conclusion

52. 52
Conclusion

53. 53