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Customer Lifetime Value in E-Business. Amit Fisher Segev Wasserkrug Dr. Opher Etzion. Outline. Motivation Introduction to Web Services Introduction to CLV RFM Variables Customer Relationship as Markov Chains Experimental Simulation Future Work. Motivation.

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amit fisher segev wasserkrug dr opher etzion

Customer Lifetime Value

in E-Business

Amit Fisher

Segev Wasserkrug

Dr. Opher Etzion

outline
Outline
  • Motivation
  • Introduction to Web Services
  • Introduction to CLV
  • RFM Variables
  • Customer Relationship as Markov Chains
  • Experimental
  • Simulation
  • Future Work
motivation
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
solution in the conventional market
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
introduction to web services architectural evolution
Introduction to Web ServicesArchitectural Evolution
  • Thin Clients interact with a Main Frame
introduction to web services architectural evolution1
Introduction to Web ServicesArchitectural Evolution
  • 2 Tier – PC interacts with DB (transaction management, SQL)
introduction to web services architectural evolution2
Introduction to Web ServicesArchitectural Evolution
  • 3 Tier (Client-Server) – PC interact with a Server. Server interacts with DB (CS protocols, LAN, server aplications…)
introduction to web services architectural evolution3
Introduction to Web ServicesArchitectural Evolution
  • Web – URL “is a” server. Transparent routing.
introduction to web services architectural evolution4
Introduction to Web ServicesArchitectural Evolution
  • N Tier. URL “is a” set of different Servers that interact with each other.
introduction to web services
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.
introduction to web services1

XML

HTML

Technology

TCP/IP

Presentation

Programmability

Connectivity

FTP, E-mail, Gopher

Innovation

Web Pages

Web Services

Browse the Web

Program the Web

Introduction to Web Services
web services closer look

DB Server

Web Server

Application Server

Web Services-Closer Look
web services closer look1
Web Services-Closer Look

Incoming Messages

A

B

C

D

E

F

web services closer look2

A

A

B

B

C

C

D

D

Web Services-Closer Look

Hard Drive

CPU

web services the main problem
Web Services-The Main Problem
  • All Queues are FCFS!

What Happens when:

And Then...

web services our solution
Web Services-Our Solution
  • Preferred Customers must be served first.
  • Who is preferred customer?
  • CLV can differentiate between customers.
introduction to clv
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.
introduction to clv1
Introduction to CLV

Prospects

Customers

RetainedCustomers

RetainedCustomers

RetainedCustomers

$

$

$

$

Discount Factor

Divide by Number of Initial Customers

= Customer Lifetime Value

introduction to clv2
Introduction to CLV
  • 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)

rfm variables
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.
rfm variables why is it so popular2

1

2

3

4

5

RFM Variables – Why is it so Popular?

“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

rfm variables why is it so popular5

Buy / No Buy

RFM variables

RFM Variables – Why is it so Popular?

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

customer relationship as markov chains
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)

customer relationship as markov chains1
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.
customer relationship as markov chains2
Customer Relationship as Markov Chains
  • Cognitive Response Time
  • RT(1)=t(1)
  • RT(i+1)=a*t(i+1)+(1-a)*RT(i)
customer relationship as markov chains3
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
customer relationship as markov chains4

Session with a purchase

Session without a purchase

End of time period

Customer Relationship as Markov Chains

(Rbuy, Fbuy, Fs, M, RT)

customer relationship as markov chains5

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

(Rbuy

-

1,Fbuy,fs,m,1)

Session with a purchase

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

(Rbuy

-

1, Fbuy,fs,m,2)

Rbuy!=1

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

(Rbuy

-

1, Fbuy,fs,m,3)

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

(Rbuy, Fbuy,fs,m,rt)

Session without a purchase

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

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

(Rbuy, Fbuy,fs

-

1,m,1)

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

(Rbuy, Fbuy,fs

-

1,m,2)

End of time period

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

(Rbuy, Fbuy,fs

-

1,m,3)

(1

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

(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)

Customer Relationship as Markov Chains
customer relationship as markov chains6

Session with a purchase

Session without a purchase

End of time period

Customer Relationship as Markov Chains

(Rbuy, Fbuy, Fs, M, RT)

customer relationship as markov chains7

(0,0,1,0,1)

(0,0,2,0,1)

start

(0,0,2,0,1)

(1,1,3,2,3)

(1,1,3,2,3)

(1,2,3,3,1)

(2,1,3,2,3)

(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)

Session without a purchase

(3,1,2,3,3)

“Lost customer”

End of time period

Customer Relationship as Markov Chains
customer relationship as markov chains8
Customer Relationship as Markov Chains
  • NC – Net contribution
  • E – Expense per time period
  • V(T) - expected value vector expected after T time period
experimental
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.
experimental1
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

time

Retrieve clients states

experimental conclusions
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
experimental conclusions1
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
simulation assumptions2
Simulation - Assumptions
  • Let be a set of that represent all the bets in the dataset. Let be a set of where all bets are winning bets.
simulation model

Abandonment

Exit

Buy

Browse

Bet

B

B

Bet_Think

Browse_Think

Simulation Model
  • Customer (CBMG)
simulation model1

Idle

Request’s queue

On request

Busy

Simulation Model
  • Server
simulation conclusions
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.

future work
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.
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