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IEEM 7103 Topics in Operations Research

IEEM 7103 Topics in Operations Research. Presentations. Using Data Mining Technology to Evaluate Customer ’ s Time-Variant Purchase Behavior and Corresponding Marketing Strategies. d923834 林彥伯. Paper:

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IEEM 7103 Topics in Operations Research

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  1. IEEM 7103 Topics in Operations Research Presentations Using Data Mining Technology to Evaluate Customer’s Time-Variant Purchase Behavior and Corresponding Marketing Strategies. d923834 林彥伯 1

  2. Paper: • Customer’s time-variant purchase behavior and corresponding marketing strategies: an online retailer’s case • Publish: • Computer & Industrial Engineering 2002 • Authors: • Sung Ho Ha • School of Business Administration, College of Economy & Commerce, Kyungpook National University. • Sung Min Bae, Sang Chan Park • Department of Industrial Engineering, Korea Advanced Institute of Science and Technology. 2

  3. Agenda • Abstract • Introduction • Literature review • Framework of analysis • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 3

  4. Abstract • 過去傳統的CRM (Customer Relationship Management)是在於特定時段中收集顧客的採購資料並從中獲得顧客採購行為知識，然後在將行銷資源導入到目標客戶中，以獲得利潤。 • 在以往的研究中，顧客採購行為模式 (buying-behavior-based CRM)已經被提出來並用在CRM很久了，但是動態的CRM的研究還不多。 • 此篇paper提出動態CRM的應用，並以實際線上零售商為例做詳細探討，其動態的CRM Model利用Data Mining 和 Monitoring Agent System (MAS)去分析顧客資料和顧客行為模式。 4

  5. Agenda • Abstract • Introduction • The Goal of CRM • Applications • The Lacks of Static CRM • Literature review • Framework of analysis • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 5

  6. Introduction • The maturity of the business-to-consumer (B2C) market. • A successful retailer must have provide a bundle of customized services. • Consumer markets characteristics: • Repeat-buying over the relevant time horizon. • A large number of customer. • A wealth of information detailer past customer purchase. 6

  7. The Goal of CRM • Identify the customer • Construct customer purchase data mart • Understand and predict the customer-buying pattern 7

  8. The Goal of CRM • Measure purchase behavior of customer • Recency：距離上次購買的時段有多久 • Frequency：在一段時間內採購的次數有幾次 • Monetary values：顯示顧客消費累計的金額有多少 • 這三類指標可以對顧客進行分群，以做不同策略。 8

  9. Applications • CRM applications • Short-range：哪些顧客應該直接做行銷，使得顧客很快的回來在購買商品。 • Intermediate-range：決定在保住既有顧客和吸引新顧客所要投資的金額。 • Long-range：分析目前的顧客在未來可以帶給企業多少價值。 • EC (electronic commerce) applications 顧客的採購行為模式會一直改變，因此在適用一段時間之後，就會因不能正確預測而遭淘汰，而動態CRM可以修正這個缺點。 9

  10. The Lacks of Static CRM • 要隔多久時間對已建立之規則重新建構以做更準確商業決策？ • 如何能衡量這些目標市場行銷或廣告的效益？ • 目前到底擁有多少忠實的顧客？數量是較之前多還是少？ • 該採取哪些市場策略來因應隨時間改變的顧客購買行為？ 10

  11. Agenda • Abstract • Literature review • Framework of analysis • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 11

  12. Literature Review (1/3) • Hughes, 1996 • CRM應該不只是用來分析顧客行為模式，應該能進一步分析出哪些顧客是相對於其他的邊際部分更有利潤的。 • Peppers, Rogers, & Dorf, 1999 • 提出要建立與顧客更親近、更深的的關係，有賴於當知道顧客想要什麼的時候，能不能配合顧客商業策略。 • Peppard, 2000 • 開發一個新的顧客成本，比維持目前的顧客成本還要多。因此，利用產品搭配，或是交叉銷售的方式，來延長顧客對企業的生命週期。 • Schafer, Konstan, & Riedl, 2001 • 定義了web marketing，為如何吸引顧客來瀏覽你的網站並且留住他們。 12

  13. Literature Review (2/3) • Technique for online marketing • Database marketing • 將顧客依照統計特性，例如：ZIP、income、職業…等等規則分成若干segment as a group。 • 企業在提供顧客更多個人化的服務。 • Ad targeting (offer targeting ) • 根據顧客之前的購買行為，定義對哪些顧客應該主動行銷。 13

  14. Literature Review (3/3) • One-to-one marketing (Peppard, Rogers, 1997a/b) • 資料分析層級較高，突破個人化行銷，以獲得更高獲利之商業策略。 • Content-based filtering system：所指的是提供同一顧客，與他購買的產品相關的週邊產品。 • Collaborative filtering system：所指的是以產品內容為主，推薦給與購買顧客相類似屬性的其他顧客，以增加銷售。 14

  15. Agenda • Abstract • Literature review • Framework of analysis • Data Mining Analysis and Technique • Monitoring Agent System • Dynamic CRM Model • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 15

  16. Data Mining Analysis and Technique • Time-variant Behavior Analysis • Markov Chain • Segmentation • Self-Organization Map (SOM) • Purchase Behavior Feature • R (Recency), F (Frequency), M (Monetary) • Classification • Decision Tree (C4.5) 16

  17. Monitoring Agent System 17

  18. Dynamic CRM Model (1/10) • Assumed: the model has the Markovian property 此篇作者認為這種動態分析的CRM模式，能夠在市場行銷策略之預警與測量該策略之效益上，扮演著重要的角色，尤其是在市場策略改變與競爭兩方面。 A special kind of stochastic process. The process will evolve in the futuredepend only onthe present state of the process. 18

  19. Dynamic CRM Model (2/10) • Assumed: the model has the Markovian property (Cont.) 19

  20. Dynamic CRM Model (3/10) • States Transition Probability Matrix 20

  21. Dynamic CRM Model (4/10) • Example 21

  22. Dynamic CRM Model (5/10) • Stability of the matrix of transition probabilities • Example In the long run, the process usually approachesa steady state or equilibrium when the system’s state probabilities have not changed further so long as the matrix of transition probabilities remains the same. 22

  23. Dynamic CRM Model (6/10) • Example (Cont.) • Hypothetical Profit Rate: Segment A=15%, Segment B=25%, Segment C=40% Original: After Promotion: 23

  24. Dynamic CRM Model (7/10) • Evaluating alternative marketing strategies Original: Strage1 Strage2 24

  25. Dynamic CRM Model (8/10) • Evaluating alternative marketing strategies (Cont.) 25

  26. Dynamic CRM Model (9/10) • Monitoring the movements of segments 26

  27. Dynamic CRM Model (10/10) • Assumption Relaxation • Have New Customer • Have Defector Customer 27

  28. Agenda • Abstract • Literature review • Framework of analysis • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 28

  29. Case Study • 從韓國一家商店收集 (RFM) 資料，資料從1995年六月一日到1996年12月31日，總共收集到2036個客戶資料。 • 取一時間間隔單位 (一年、一個月或一個禮拜)，計算每時期之RFM值 。 29

  30. Rt: measures how long it has been since he or she made a last purchase during last observation period from time t. • Ft: measures how many times he or she has purchased products during that period. • Mt: measures how much he or she has spent in total. 30

  31. Customer Clustering • SOM • Training the SOM • Mapping input customer RFM patterns to output customer segments • Label of segments • If each average of segments is bigger than the overall mean, a character ‘h’ is given to that value. If the opposite case occurs, a character ‘l’ is given. 31

  32. Customer Segments and Corresponding Marketing Strategies at a Specific Time 32

  33. Changes of The Number of Customers in Each Segment 33

  34. The Matrix of Transition Probability • 觀察在某一時間的數據結果，由下圖可看出由其他類別轉換到RhFlMl類的顧客(0.05,27人)小於由RhFlMl類轉到其他類的顧客(0.062+0.115,66人)。而尤其他類別轉換到RlFhMh類的顧客(0.062+0.171,82人)大於由RlFhMh類別轉換到其他類的顧客(0.056,16人)。 • 這表示在這一段時間內的決策是有效果地，讓快走失的顧客類別人數越來越少，重要顧客類別的人數越來越多。 成熟客戶 將流失客戶 重點客戶 將流失客戶 重點客戶 成熟客戶 34

  35. Comparison of current strategy 35

  36. Agenda • Abstract • Literature review • Framework of analysis • Application of dynamic CRM to a retailer (Case Study) • Conclusion & Future work 36

  37. Conclusion & Future Work (1/4) • Conclusion • A cost-effective method for application of CRM should be done dynamically in time to solve management problems. • A method to discover potential customers. • Future work • The analysis of the outlier in loyal cluster. • Extend one-dimensional features to multi-dimensional features. 37

  38. Future Work (2/4) • To use a multi-channel contact center as the foundation to model customer interaction processes. • To develop contact center evaluation methods, and define key performance indicators (KPI) for continuously improving customer service quality. 38

  39. Future Work (3/4) Contact Centers • Contact centers are implemented to provide improved customer services (Rowley, Mostowfi, and Lees, 2002). • Contact centers not only integrate multi-channels and provide customer service supports, but also improve quality management, contact routing, and knowledge management. 39

  40. Future Work (4/4) - Multi-channels Contact Center 40

  41. Thank you for listening !!!. The End 41

  42. Contact Center Clustering Analysis • Sharma (1996) proposed the RMSSTD (Root Mean Square Standard Deviation) and RS (R-Squared) methods to evaluate the quality of non-hierarchical clustering (e.g. K-Means) result. 42

  43. Key Performance Indicators • Key Performance Indicators (KPIs) measure performance and improvements after companies have implemented new business processes. • The goal of implementing KPIs is to create professional contact centers and deliver high levels of service quality. 43

  44. Key Performance Indicators Cleveland (1996), Goodman, Ward, Segal and Cleveland (2000), Feinberg, Kim and Hokama (2000) and Grimm (2001) indicate the following important KPIs for contact center: • Average time of answering inquiries. • Customer queuing time • Percentage of callers who have satisfactory resolution of problems on the first call • Abandonment rate (the percentage of callers who hang up or disconnect prior to answers being provided) • Adherence (agents in their seats and working as scheduled) • Average work time after call (time needed to finish paper work and research after the call has initially been handled) • Percentage of calls blocked (percentage of callers who receive a busy signal and cannot get into the service queue) • Time before abandoning the call (average time caller waited before balking from the service queue) • Inbound calls per agent during the work shift • Agent turnover (the number of agent who leave employment during a period of time) • Total cost of delivering the contact center service • Service output level (total number of customers served in a period of time) • Revenue of the contact center • The difference between predicted and actual work load • The difference between predicted and actual agent demand • Agent idle time 44

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