Transcript Slide 1

Amit Fisher
Segev Wasserkrug
Dr. Opher Etzion
1
Outline
 Motivation
 Introduction to Web Services
 Introduction to CLV
 RFM Variables
 Customer Relationship as Markov Chains
 Experimental
 Simulation
 Future Work
2
Motivation
 Many Suppliers with similar offerings in E-
Markets.
 Customer will choose the organization that
gives the better service
 It is impossible to give the best service for all
of customers all of the time (limited
resources).
 QoS refer to Response Time (RT) and
availability
3
Solution in the Conventional Market
 CRM (Customers Relationship Management):
is a comprehensive approach which provides
seamless integration of every area of
business that touches the customer - namely
marketing, sales, customer service and field
support - through the integration of people,
process and technology.
 Implement techniques to give preference to
valuable customers
4
Solution in the Conventional Market
5
Introduction to Web Services
Architectural Evolution
 Thin Clients interact
with a Main Frame
6
Introduction to Web Services
Architectural Evolution
 2 Tier – PC interacts
with DB (transaction
management, SQL)
7
Introduction to Web Services
Architectural Evolution
 3 Tier (Client-Server) –
PC interact with a
Server. Server
interacts with DB (CS
protocols, LAN, server
aplications…)
8
Introduction to Web Services
Architectural Evolution
 Web – URL “is a”
server. Transparent
routing.
9
Introduction to Web Services
Architectural Evolution
 N Tier. URL “is a” set
of different Servers
that interact with each
other.
10
Introduction to Web Services
Architectural Evolution
 Web Services
Internet
Application
Server
Web Server
Application
Server
Web Server
11
Introduction to Web Services
 A Web Service is a URL-addressable software
resource that performs functions (or a function).
 Web Services communicate using standard protocol
known as SOAP (Simple Object Access Protocol).
 A Web Service is located by its listing in a Universal
Discovery, Description and Integration (UDDI)
directory.
12
Introduction to Web Services
Connectivity
Presentation
Programmability
Browse
the Web
Program
the Web
13
Web Services-Closer Look
Firewall
Web
Server
Application
Server
DB
Server
14
Web Services-Closer Look
Incoming
Messages
A
B
C
D
E
F
15
Web Services-Closer Look
A
B
C
D
Hard Drive
A
B
C
D
CPU
16
Web Services-The Main Problem
 All Queues are FCFS!
What Happens when:
And Then...
17
Web Services-Our Solution
 Preferred Customers must be served first.
 Who is preferred customer?
 CLV can differentiate between customers.
18
Introduction to CLV
 CLV-projection of future cash flows for a
customer across all product holdings and
discounting these to get an "embedded
value" of the customer.
19
Introduction to CLV
Prospects
Customers
$
Retained
Customers
$
Retained
Customers
Retained
Customers
$
$
Discount Factor
Divide by Number of Initial Customers
=
Customer Lifetime Value
20
Introduction to CLV
i
i 1
n
n




r
r

 

CLV  GC  
 M  
i 
i 0.5 
(
1

d
)
(
1

d
)




i

o
i

1

 

 GC- Yearly gross contribution margin per
customer
 M- Promotion costs per customer (can refer
to other costs as well)
 n- Length, in years, of the period over which
cash flow are projected.
 r- Early retention rate
 d - Early discount rate
Berger and Nasr(1998)
21
RFM Variables
 Recency – the most recent date that the
customer has requested for a change in his
service (usually a purchase, but not always)
 Frequency – the number of time the
customer has made a purchase.
 Monetary – the monetary amount is the
total dollar amount that a customer has
spent.
22
RFM Variables – Why is it so Popular?
3.0
2.5
2.0
Number of
purchases 1.5
per year
1.0
0.5
0.0
1
2
3
4
5
Years as a customer
23
RFM Variables – Why is it so Popular?
$70
$60
$50
Average $40
Purchase
Price $30
$20
$10
$0
1
2
3
4
5
Years as a customer
24
RFM Variables – Why is it so Popular?
90%
80%
70%
Percentage
Retained
from
Previous
Year
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
Years as a customer
25
RFM Variables – Why is it so Popular?
First year costs are
often high
70%
60%
50%
Costs as 40%
a % of
revenue 30%
20%
10%
0%
1
2
3
4
5
Years as a customer
26
RFM Variables – Why is it so Popular?
1
2
3
4
5
“The people most likely to respond to a new offer are those people who have made
a purchase from you most recently”, Arthur Middleton Hughes
27
RFM Variables – Why is it so Popular?
28
RFM Variables – Why is it so Popular?
29
RFM Variables – Why is it so Popular?
Buy / No Buy
RFM variables
Baesens, Viaene, Van denPoel, Vanthienen, Dedene(2002)
30
Customer Relationship as Markov Chains
1, f+1, m’
A Purchase
r-1, f, m
r, f, m
r+1, f, m
End of time Period
Pfeifer and Carraway (2000)
31
Customer Relationship as Markov Chains
 State=(Rbuy, Fbuy, Fs, M, Rbet, Fbet, Mbet, RT)
 Rbuy Represents the time that have passed since the last purchase







the customer had made at the site.
Fbuy represents the total number of customer’s purchases at the
site.
Fs represents the total number of customer’s sessions at the site.
M Represents the total amount spent by the customers at the site.
Rbet represents the time that had passed since the last auction that
the customer had participate at.
Fbet Represents the total number of auctions that the client had
participated at.
Mbet Represents the total amount of money the customer bet on.
RT Represents the history of response time that the customers
experienced while interacting with the site.
32
Customer Relationship as Markov Chains
 Cognitive Response Time
 RT(1)=t(1)
 RT(i+1)=a*t(i+1)+(1-a)*RT(i)
weight
Responce time weight
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
3
0.8t(i+1)+0.2RT(i)
0.2t(i+1)+0.8RT(i)
4
5
6
7
sessions number
8
9
10
0.5t(i+1)+0.5RT(I)
0.4t(i+1)+0.6RT(i)
11
33
Customer Relationship as Markov Chains
 For simplicity, let the state space be:
(Rbuy, Fbuy, Fs, M, RT)
 Rbuy:
0…3
 Fbuy:
0…3
 Fs:
1…3
 M:
1…3
 RT: 1…3
34
Customer Relationship as Markov Chains
(1,1,1,3,3)
(1,1,1,3,2)
Session with
a purchase
(1,1,1,3,1)
(1,1,1,2,3)
(1,1,1,2,2)
Session without
a purchase
(1,1,1,2,1)
start
(1,1,1,1,3)
End of time
period
(1,1,1,1,2)
(1,1,1,1,1)
(0,0,1,0,1)
(Rbuy, Fbuy, Fs, M, RT)
35
Customer Relationship as Markov Chains
(1, Fbuy +1,fs+1,1,1)
(Rbuy-1,Fbuy,fs,m,1)
(1, Fbuy +1,fs+1,2,1)
(Rbuy -1, Fbuy,fs,m,2)
(Rbuy -1, Fbuy,fs,m,3)
Rbuy!=1
(Rbuy, Fbuy,fs,m,rt)
(1, Fbuy +1,fs+1,3,1)
(1, Fbuy +1,fs+1,1,2)
(1, Fbuy +1,fs+1,2,2)
(Rbuy, Fbuy,fs -1,m,1)
(1, Fbuy +1,fs+1,3,2)
(Rbuy, Fbuy,fs -1,m,2)
(1, Fbuy +1,fs+1,1,3)
(Rbuy, Fbuy,fs -1,m,3)
Session with
a purchase
(1, Fbuy +1,fs+1,2,3)
(1, Fbuy +1,fs+1,3,3)
Session without
a purchase
End of time
period
(Rbuy, Fbuy,fs+1,m,1)
(Rbuy, Fbuy,fs+1,m,2)
(Rbuy, Fbuy,fs+1,m,3)
Exception:if rb == 0, rb stay 0
(Rbuy +1, Fbuy,fs,m,rt)
36
Customer Relationship as Markov Chains
(3,Fbuy,fs,m,rt)
start
Customer was “lost for
good” and made a new
purchase or a session, so
our CLV consider him as a
new customer
Session with
a purchase
Rbuy!=3
(Rbuy’,3,fs’,m’,rt’)
(Rbuy,3,fs,m,rt)
If Rb=“Max_Rb”
than it stay
“Max_Rb” in all next
states.
Session without
a purchase
End of time
period
Rbuy!=3
(Rbuy’, Fbuy’,3,m’,rt’)
(Rbuy, Fbuy,3,m,rt)
If Rs=“Max_Rs” than
it stay “Max_Rs” in
all next states.
(Rbuy, Fbuy, Fs, M, RT)
37
Customer Relationship as Markov Chains
start
(0,0,1,0,1)
(0,0,2,0,1)
(0,0,2,0,1)
(1,1,3,2,3)
(1,1,3,2,3)
(2,1,3,2,3)
(1,2,3,3,1)
(2,2,3,3,1)
start
New session for a “lost customer”
(3,2,3,3,1)
(0,0,1,0,3)
“Lost customer”
(0,0,1,0,3)
(1,1,2,3,3)
Session with a
purchase
Time periods
(2,1,2,3,3)
(3,1,2,3,3)
“Lost customer”
Session without a
purchase
End of time
period 38
Customer Relationship as Markov Chains
 NC  E

R( S )   E
0

if S .Rbuy  1
if 1  S .Rbuy " Max _ Rbuy"
if
S .Rbuy " Max _ Rbuy"
 NC – Net contribution
 E – Expense per time period
T
V (T )   [(1  d ) 1 P]t R
t 0
 V(T) - expected value vector expected after T time
period
lim
V 
V (T )  {I  (1  d ) 1 P}1 R
T 
39
Experimental
 Data were obtained from an E-commerce
company in Israel
 70,134 purchases (“auction wins”) and
253,736 bets took place, and the total amount
of 84,000,000 new Israeli shekels was spent.
40
Experimental
 Data split
Data
Used for CLV
prediction by our
model
time
Used to Calculate NPV for
retrieved states
Retrieve
clients states
 States were attributed into several groups, according
to number of customer observations at each state
when data was split
41
Experimental
iteration
Rbuy
Fbuy
M
Rbet
Fbet
Mbet
total correlation
1
10
1
5
10
3
5
0.67
2
10
2
10
10
3
10
0.61
3
10
5
10
5
5
0.6
4
10
5
10
5
10
0.6
5
10
5
10
5
3
0.6
6
15
3
10
15
5
7
10
2
5
10
3
8
20
5
20
5
9
15
5
15
5
5
0.55
10
10
10
10
10
5
0.55
11
10
5
10
10
5
0.55
12
8
5
8
5
5
0.55
13
10
10
10
10
14
5
5
5
5
5
0.57
5
0.56
0.55
0.53
5
0.512
42
Experimental
Rbuy=10,Fbuy=1,M=5,Rbet=10,Fbet=3,Mbet=5
60
1.00
0.8
0.78
50
0.76
40
0.6
30
0.46
0.4
20
0.32
0.2
0
10
0.00
over 1000
100-1000
10-100
3-10
2
1
2
11
30
54
12
3
Client's Percent
47%
42%
7%
3%
0%
0%
correlation
1.00
0.78
0.76
0.46
0.32
0.00
number Of States
Number of States
Correlation/Client percent
1
0
State Group
43
Experimental
Number of States and Correlation for Different Groups
120
1
0.9
100
0.8
0.7
0.6
60
0.5
0.4
cprrelation
number of states
80
40
0.3
0.2
20
0.1
0
0
over 1000
number of states - 9
number of states - 5
correlation - 13
correlation - 2
100-1000
10-100
number of states - 13
number of states - 2
correlation-4
correlation - 1
group
3-10
number of states-4
number of states - 1
correlation - 3
2
1
number of states - 3
correlation - 9
correlation - 5
44
Experimental - Conclusions
 High correlation is achieved for state groups
where the number of observation per state is
high
 Criteria for evaluating the model must be
defined in order to evaluate the iterations
results
 A. Total correlation
 B. Correlation between most popular states
 C. Group’s correlation with reference to number of states in each
group
45
Experimental - Conclusions
 Model must be fitted for additional different
domains.
 Using visualization techniques and “data
cleaning” can help finding the accurate
parameters for the model.
 Problem: No Data for validating RT and Fs
variables.
Solutions: Simulation
46
Simulation - Assumptions
Abondonment Probability
1
P(abn)
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Response Time (sec)
47
Simulation - Assumptions
Time Period Between Arrivals as Function of RT
300
F(RT) (days)
250
200
150
100
50
0
0
2
4
6
RT (sec)
8
10
12
48
Simulation - Assumptions
Bet Pr ice
 
Cata log Pr ice
 Let  bet be a set of

that represent all the
bets in the dataset. Let  buy be a set of 
where all bets are winning bets.
49
Simulation - Assumptions
BetPrice/CatalogPrice Distribution for Bets and Wining Bets
20%
100%
18%
90%
16%
80%
14%
70%
12%
60%
10%
50%
8%
40%
6%
30%
4%
20%
2%
10%
0%
0%
0

0.1
0.2
0.3
0.4
a  bet

0.5
0.6
0.7
0.8
0.9
a   bet
a  buy
1

a   buy
P(    buy ) = -29.495  + 98.089  - 117.32  + 56.562  - 7.1856  + 0.3463  - 0.0011
6
5
4
3
2
50
Simulation - Assumptions
Client’s
group

P(   buy )
Data
Group
Frequency at
Simulation
20%
20%
20%
7.50%
38%
40%
35%
36.54%
High
Buyer
23%
20%
45%
60.81%
Intensiv
e Buyer
19%
20%
65%
93.58%
Low
Buyer
Average
Buyer
Group
Frequency

 Pbuy
at
(
(Transition
Probability from
“Bet” to “Buy”)
51
Simulation Model
 Customer (CBMG)
Abandonment
Browse
Exit
B
Browse_Think
Buy
Bet
B
Bet_Think
52
Simulation Model
 Server
Idle
On
request
Request’s queue
Busy
53
Simulation Results
iteration
Rbuy Fbuy
M
Rbet Fbet Mbet
Fs
RT
total
correlation
1
3
10
0
3
3
0
14
0
0.4
2
3
6
4
3
10
0
14
0
0.68
3
3
6
4
3
10
0
0
8
0.72
4
3
6
3
3
8
3
14
8
0.65
5
2
2
2
2
4
2
4
0
0.76
6
3
6
0
3
0
5
14
8
0.69
7
3
0
4
3
10
0
14
8
0.66
54
Simulation Results
Number of States and correlation for different groups
400
1
0.9
350
0.8
300
250
0.6
200
0.5
0.4
150
correlation
number of states
0.7
0.3
100
0.2
50
0.1
0
0
over 1000
numberOfStates-1
numberOfStates-5
correlation-2
correlation-6
100-1000
10-100
numberOfStates-2
numberOfStates-6
correlation-3
correlation-7
3-10
2
numberOfStates-3
numberOfStates-7
correlation-4
1
numberOfStates-4
correlation-1
correlation-5
55
Simulation- Conclusions
 The model succeeds to predict the influence
of bad response time on customer’s value
 The CLV model gives better estimation for
customer behavior (and lifetime value) if
customer behavior is affected by server
performance.
56
Future Work
 Design a schedule mechanism for the site
infrastructure, based on CLV.
 Compare this mechanism to the basic FCFS
policy and to other priority based
mechanisms.
 Understanding the marketing outcomes result
from the changes in the scheduling policy.
57