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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. 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

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  1. Customer Lifetime Value in E-Business 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

  6. Introduction to Web ServicesArchitectural Evolution • Thin Clients interact with a Main Frame

  7. Introduction to Web ServicesArchitectural Evolution • 2 Tier – PC interacts with DB (transaction management, SQL)

  8. Introduction to Web ServicesArchitectural Evolution • 3 Tier (Client-Server) – PC interact with a Server. Server interacts with DB (CS protocols, LAN, server aplications…)

  9. Introduction to Web ServicesArchitectural Evolution • Web – URL “is a” server. Transparent routing.

  10. Introduction to Web ServicesArchitectural Evolution • N Tier. URL “is a” set of different Servers that interact with each other.

  11. Introduction to Web ServicesArchitectural Evolution • Web Services

  12. 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.

  13. 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

  14. DB Server Web Server Application Server Web Services-Closer Look

  15. Web Services-Closer Look Incoming Messages A B C D E F

  16. A A B B C C D D Web Services-Closer Look Hard Drive CPU

  17. Web Services-The Main Problem • All Queues are FCFS! What Happens when: And Then...

  18. Web Services-Our Solution • Preferred Customers must be served first. • Who is preferred customer? • CLV can differentiate between customers.

  19. 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.

  20. Introduction to CLV Prospects Customers RetainedCustomers RetainedCustomers RetainedCustomers $ $ $ $ Discount Factor Divide by Number of Initial Customers = Customer Lifetime Value

  21. 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)

  22. 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.

  23. RFM Variables – Why is it so Popular?

  24. RFM Variables – Why is it so Popular?

  25. RFM Variables – Why is it so Popular?

  26. RFM Variables – Why is it so Popular?

  27. 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

  28. RFM Variables – Why is it so Popular?

  29. RFM Variables – Why is it so Popular?

  30. Buy / No Buy RFM variables RFM Variables – Why is it so Popular? Baesens, Viaene, Van denPoel, Vanthienen, Dedene(2002)

  31. 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)

  32. 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.

  33. Customer Relationship as Markov Chains • Cognitive Response Time • RT(1)=t(1) • RT(i+1)=a*t(i+1)+(1-a)*RT(i)

  34. 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

  35. Session with a purchase Session without a purchase End of time period Customer Relationship as Markov Chains (Rbuy, Fbuy, Fs, M, RT)

  36. (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

  37. Session with a purchase Session without a purchase End of time period Customer Relationship as Markov Chains (Rbuy, Fbuy, Fs, M, RT)

  38. (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

  39. Customer Relationship as Markov Chains • NC – Net contribution • E – Expense per time period • V(T) - expected value vector expected after T time period

  40. 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.

  41. 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

  42. Experimental

  43. Experimental

  44. Experimental

  45. 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

  46. 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

  47. Simulation - Assumptions

  48. Simulation - Assumptions

  49. 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.

  50. Simulation - Assumptions

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