Amit Fisher Segev Wasserkrug Dr. Opher Etzion

1

Amit Fisher

Segev Wasserkrug

Dr. Opher Etzion

2

Outline

Motivation Introduction to Web Services Introduction to CLV RFM Variables Customer Relationship as Markov Chains Experimental Simulation Future Work

3

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

4

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

5

Solution in the Conventional Market

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Introduction to Web ServicesArchitectural EvolutionArchitectural Evolution Thin Clients interact

with a Main Frame

IBM

Main Frame

7

Introduction to Web Services Architectural EvolutionArchitectural Evolution 2 Tier – PC interacts

with DB (transaction management, SQL)

Data Base

2 Tier

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Introduction to Web Services Architectural EvolutionArchitectural Evolution 3 Tier (Client-Server) –

PC interact with a Server. Server interacts with DB (CS protocols, LAN, server aplications…)

Data Base

3 Tier

9

Introduction to Web Services Architectural EvolutionArchitectural Evolution Web – URL “is a”

server. Transparent routing.

Web Server

Web

Internet

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Introduction to Web Services Architectural EvolutionArchitectural Evolution N Tier. URL “is a” set

of different Servers that interact with each other.

Web Server

N Tier

Internet

ApplicationServer

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Introduction to Web Services Architectural EvolutionArchitectural Evolution Web Services

Web Server

Internet

ApplicationServer

ApplicationServer

Web Server

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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.

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Introduction to Web Services

XMLXML

ProgrammabilityProgrammabilityConnectivityConnectivity

HTMLHTML

PresentationPresentation

TCP/IPTCP/IP

Technology

Technology

Innovation

Innovation

FTP,FTP, E-mail, Gopher

E-mail, GopherWeb Pages

Web Pages

Browse Browse the Webthe Web

Program Program the Webthe Web

Web Services

Web Services

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Web Services-Closer Look

Web Server

Application Server

DB Server

ComputerComputer Computer Computer

InternetFirewall

15

C


A B

Incoming Messages

FD E

16


CA B D

CA B D

Hard Drive

CPU

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Web Services-The Main Problem

All Queues are FCFS!

What Happens when:

And Then...

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Web Services-Our Solution

Preferred Customers must be served first. Who is preferred customer? CLV can differentiate between customers.

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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.

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Introduction to CLV

Prospects Customers

$ $ $ $

Discount Factor

Divide by Number of Initial Customers

= Customer Lifetime Value

RetainedCustomers

RetainedCustomers

RetainedCustomers

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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

Introduction to CLV

n

ii

in

oii

i

d

rM

d

rGCCLV

15.0

1

)1()1(

Berger and Nasr(1998)

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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.

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RFM Variables – Why is it so Popular?

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Number of purchases per year

1 2 3 4 5

Years as a customer

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$0

$10

$20

$30

$40

$50

$60

$70

Average Purchase

Price

1 2 3 4 5

Years as a customer


25


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Percentage Retained

from Previous

Year

1 2 3 4 5

Years as a customer

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0%

10%

20%

30%

40%

50%

60%

70%

Costs as a % of

revenue

1 2 3 4 5

Years as a customer

First year costs are often high

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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

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29


30


Baesens, Viaene, Van denPoel, Vanthienen, Dedene(2002)

RFM variables

Buy / No Buy

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Customer Relationship as Markov Chains

r, f, mr-1, f, m r+1, f, m

1, f+1, m’

End of time Period

A Purchase

Pfeifer and Carraway (2000)

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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.

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Cognitive Response Time RT(1)=t(1) RT(i+1)=a*t(i+1)+(1-a)*RT(i)

Responce time weight

00.10.20.30.40.50.60.70.80.9

0 1 2 3 4 5 6 7 8 9 10 11sessions number

we

igh

t

0.8t(i+1)+0.2RT(i) 0.5t(i+1)+0.5RT(I)0.2t(i+1)+0.8RT(i) 0.4t(i+1)+0.6RT(i)

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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

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Session with a purchase

Session without a purchase

End of time periodstart

(0,0,1,0,1)

(1,1,1,1,1)

(1,1,1,1,2)

(1,1,1,1,3)

(1,1,1,2,1)

(1,1,1,2,2)

(1,1,1,2,3)

(1,1,1,3,1)

(1,1,1,3,2)

(1,1,1,3,3)

(Rbuy, Fbuy, Fs, M, RT)

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(Rbuy, Fbuy,fs,m,rt)

(1, Fbuy +1,fs+1,1,1)

(1, Fbuy +1,fs+1,2,1)

(1, Fbuy +1,fs+1,3,1)

(1, Fbuy +1,fs+1,1,2)

(1, Fbuy +1,fs+1,2,2)

(1, Fbuy +1,fs+1,3,2)

(1, Fbuy +1,fs+1,1,3)

(1, Fbuy +1,fs+1,2,3)

(1, Fbuy +1,fs+1,3,3)

(Rbuy +1, Fbuy,fs,m,rt)

(Rbuy, Fbuy,fs+1,m,1)



(Rbuy-1,Fbuy,fs,m,1)

(Rbuy -1, Fbuy,fs,m,2)

(Rbuy -1, Fbuy,fs,m,3) Rbuy!=1

(Rbuy, Fbuy,fs-1,m,1)



Exception:if rb == 0, rb stay 0



End of time period

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(Rbuy,3,fs,m,rt)

Rbuy!=3

(Rbuy’,3,fs’,m’,rt’)

If Rb=“Max_Rb” than it stay “Max_Rb” in all next states.

(Rbuy, Fbuy,3,m,rt)

Rbuy!=3

(Rbuy’, Fbuy’,3,m’,rt’)

If Rs=“Max_Rs” than it stay “Max_Rs” in all next states.

(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



End of time period

(Rbuy, Fbuy, Fs, M, RT)

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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)

(3,2,3,3,1)

“Lost customer”

start

(0,0,1,0,3)

(0,0,1,0,3) (1,1,2,3,3)

(2,1,2,3,3)

(3,1,2,3,3) “Lost customer”

New session for a “lost customer”

Time periods Session with a purchase


End of time period

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"_".0

"_".1

1.

)(

RbuyMaxRbuySif

RbuyMaxRbuySifE

RbuySifENC

SR

NC – Net contribution E – Expense per time period

RPdV tT

t

T ])1[(0

1)(

V(T) - expected value vector expected after T time period

RPdIVT

V T 11)( })1({lim

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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.

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Experimental

Data split

States were attributed into several groups, according to number of customer observations at each state when data was split

Data

Used for CLV prediction by our model

Used to Calculate NPV for retrieved states

Retrieve clients states

time

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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 0.57

7 10 2 5 10 3 5 0.56

8 20 5 20 5 0.55

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 5 10 10 0.53

14 5 5 5 5 5 0.512

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Experimental

Rbuy=10,Fbuy=1,M=5,Rbet=10,Fbet=3,Mbet=5

1.00

0.78 0.76

0.46

0.32

0.000

0.2

0.4

0.6

0.8

1

State Group

Co

rre

latio

n/C

lien

t pe

rce

nt

0

10

20

30

40

50

60

Nu

mb

er

of S

tate

s

number Of States 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

over 1000 100-1000 10-100 3-10 2 1

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ExperimentalNumber of States and Correlation for Different Groups

0

20

40

60

80

100

120

over 1000 100-1000 10-100 3-10 2 1group

nu

mb

er

of

sta

tes

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

cp

rre

lati

on

number of states - 9 number of states - 13 number of states-4 number of states - 3number of states - 5 number of states - 2 number of states - 1 correlation - 9correlation - 13 correlation-4 correlation - 3 correlation - 5correlation - 2 correlation - 1

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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

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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

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Simulation - Assumptions

Abondonment Probability

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Response Time (sec)

P(a

bn

)

48


Time Period Between Arrivals as Function of RT

0

50

100

150

200

250

300

0 2 4 6 8 10 12

RT (sec)

F(R

T)

(day

s)

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Let be a set of that represent all the bets in the dataset. Let be a set of where all bets are winning bets.

iceCata

iceBet

Prlog

Pr

bet buy

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BetPrice/CatalogPrice Distribution for Bets and Wining Bets

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

a a a abet buybet buy

P( buy ) = -29.495 6 + 98.089 5 - 117.32 4 + 56.562 3 - 7.1856 2 + 0.3463 - 0.0011

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P(buyClient’s group

Group Frequency at

Data

Group Frequency at Simulation

P( )(Transition

Probability from “Bet” to “Buy”)

Low Buyer

20% 20% 20% 7.50%

Average Buyer

38% 40% 35% 36.54%

High Buyer

23% 20% 45% 60.81%

Intensive Buyer

19% 20% 65% 93.58%

buy

52

Simulation Model

Browse Bet Buy

Browse_Think Bet_Think

B B

Abandonment Exit

Customer (CBMG)

53

Simulation Model

Server

Idle

Busy

On

re

qu

es

tRequest’s queue

54

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

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Simulation Results

Number of States and correlation for different groups

0

50

100

150

200

250

300

350

400

over 1000 100-1000 10-100 3-10 2 1

nu

mb

er

of

sta

tes

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

co

rre

lati

on

numberOfStates-1 numberOfStates-2 numberOfStates-3 numberOfStates-4numberOfStates-5 numberOfStates-6 numberOfStates-7 correlation-1correlation-2 correlation-3 correlation-4 correlation-5correlation-6 correlation-7

56

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.

57

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.

Documents

Amit Fisher Segev Wasserkrug Dr. Opher Etzion