MD850: e-Service Operations

1. MD850: e-Service Operations Analyzing Web Site Usage: Web Site Mining of Customer Information

2. Overview Background Positioning and Marketing of Electronic Services Approaches for Analyzing e-Service Operations Data Mining for Real-Time Marketing Conclusion

3. Background

4. Background Service-Products consist of Goods Services Information

5. Background Challenges in Traditional Services Marketing Problem: consumer behavior (which the Marketer would like to manipulate) is difficult to observe in services … Purchase and consumption of service occurs simultaneously Customer may be involved in creation and delivery of the service Consumer behavior during the service encounter is difficult to observe, and potentially extremely variable Goods: consumers can evaluate objective attributes before purchasing Services: difficult to evaluate before purchase, and thus can be based on observable attributes as well as experiences the consumer expects to have

6. Background Challenges in Traditional Services Marketing Market segmentation Determining groups that have as much similarity as possible (w.r.t. characteristics, needs, behaviors), and as much dissimilarity from other groups as possible Targeting Deciding which group or groups of customers the organization is equipped to serve and can do so profitably Positioning Position the services in the minds of current and potential customers … to differentiate itself and its services from the competition

7. Background Consumer market segmentation for services usually involves statistical analysis of a broad set of variables Geographic Demographic Psychographic Behavioristic

8. Background Targeting of services involves matching a customer segment to a certain service package Recommendations for appropriate targeting Segments should be consistent with the organization’s objectives and strategies Available resources and competencies (service process) should be able to serve chosen segments successfully Resources/competencies should provide a competitive advantage in each chosen customer segment If more than one segment will be served, the segments (and the service operations satisfying them) should be compatible If multiple segments are not compatible, (i) separate facilities should be used, or (ii) the different segments should be served at different times

9. Background The Problem? “Service professionals traditionally think about a new service product as something that a centralized marketing department creates for its customers on the basis of understanding a new customer need.” The success of this new service development approach depends upon … Accuracy of understanding customer needs Ability to create or customize the service to meet specific needs of customers Competitive speed at which the new service is delivered The traditional approach may take too long (i.e., many months to many years) Traditional approach cannot adapt quickly to changing needs of customers

10. Positioning and Marketing of Electronic Services

11. Positioning and Marketing of Electronic Services The Problem? “Technological advances in a number of fields, however, make it possible to develop new service products in real-time, that is, at the customers point of requirement (i.e. the customer’s place and time). Real-time products and services meet the needs of individual customers at the first time of use and also as those needs change over time.” The approach is known as real time because it involves adapting the service product dynamically to both The specific customer segment (i.e., customer space) The constantly evolving needs of the customer (i.e., customer time)

12. Positioning and Marketing of Electronic Services The Problem? Developing real-time service products involves Individualizing the service product Vesting the service product with the power to adapt itself to changing customer needs

13. Positioning and Marketing of Electronic Services 4 C’s of e-Service Marketing Communication A continual series of dialogues or conversations with customers Customization Tailoring individual offerings (goods, services and digital content) based on understanding the customer’s particular needs and behaviors, to build the perception that the firm sees him/her as an individual Collaboration Engaging the customer in the actual design and delivery of a product offering Clairvoyance “Reading the customer’s mind” or anticipating his/her needs regarding the firm’s products, and proactively offering those products

14. Positioning and Marketing of Electronic Services The Challenge … Real-Time Marketing Integrates and extends mass customization and relationship marketing The different and evolving needs and preferences of individual customers are satisfied over time Relationships with customers are managed at the customer level (instead of at the marketing department level) and are often contained within the good or service itself Decentralized intelligence is deployed … capable of anticipating or reacting to customer needs, either overtly or covertly, or to environmental changes

15. Positioning and Marketing of Electronic Services Real-Time Service Product An individually customized service product that Tracks changing customer needs continuously Updates itself to meet the customer’s personal needs through interaction with the customer and the environment The updating often occurs without conscious or overt action on the customer’s part Provides customers with unique benefits Service products are customized to customers’ needs at their first points of requirement Service products interact continuously with individual customers and cater to their post-purchase product and service requirements, usually without the need for reference to or contact with the original producer

16. Approaches for Analyzing e-Service Operations

17. Approaches for Analyzing e-Service Operations Data Mining Building Models Similar to conventional exploratory statistical methods Objective is to produce an overall summary of a set of data to identify and describe the main features of the shape of a statistical distribution Tools – Regression, Cluster Analysis, Classification Trees Pattern Detection Seeks to identify small – yet interesting and potentially important – departures from the norm (unusual patterns) Tools – Neural Networks, Bayesian Statistical Methods, Machine Learning

18. Approaches for Analyzing e-Service Operations Data Mining – Analysis Tasks Data Summarization descriptive statistical and visualization techniques Segmentation separates the data into interesting and meaningful sub-groups or classes Classification assumes that a set of objects – characterized by some attributes or features – belongs to different classes objective is to build a model that allows you to correctly identify an object as a member of a class Prediction objective is to build a model that allows one to forecast or predict a continuous value correctly Dependency Analysis finding a model that describes significant correlations, associations, or dependencies between data items or events

19. Approaches for Analyzing e-Service Operations

20. Approaches for Analyzing e-Service Operations Web Content Mining the discovery of useful information from web content, web data, and documents Web Structure Mining discovery of the model underlying the link structures of the web or a website Web Usage Mining click-stream analysis a process of applying data mining techniques for the discovery of usage patterns from web site data

21. Approaches for Analyzing e-Service Operations

22. Approaches for Analyzing e-Service Operations

23. Approaches for Analyzing e-Service Operations

24. Approaches for Analyzing Real-Time Service Products Some Analysis Approaches What are customers consuming? Product outcome oriented How are customers consuming? Which pages are they visiting? Process outcome oriented What causes customers to exit e-service? Where do they exit? Funnel mining

25. Reference Model for e-Business(Menasce and Almeida, Scaling for e-Business, 2000)

26. Reference Model for e-Business(Menasce and Almeida, Scaling for e-Business, 2000) Business Model situation, purpose, outcome, functions, resources, location Functional Model business processes and applications needed to accomplish the services and functions offered to customers Customer Model Customer Behavior Model Behavior-Based Workload Model Resource Model Architecture Functional Operational Infrastructure

27. Approaches for Analyzing Real-Time Service Products

28. Approach #1: Cross-Sectional Analysis of Service Product Attributes

29. Cross-Sectional Service-Product Analysis(Heim and Sinha 2000) e-Service Analysis Process Collected variables representing service-product attributes of 255 WWW sites Grouped services based on similarities in service-product attributes Statistical Technique: Cluster Analysis Analyzed relationship to customer satisfaction and customer loyalty

31. Cross-Sectional Service-Product Analysis (Heim and Sinha 2000) Advantages of Approach Can identify different e-Service service products Static content Dynamic content Can link service-product to customer ratings of satisfaction, quality, loyalty, etc. Validate that you are satisfying customers and that you have an effective business model

32. Service-Product AnalysisData Mining of Attribute Consumption Shortcomings of Approach Heim and Sinha (2000) approach mainly applicable for competitive analysis of different e-Service operations in similar industries Possible to extend this method to activities within a single site For each customer, tabulate all of the pages visited, content downloaded, etc. Group customers based on how much content they consume Relate customers to their perceptions about the quality of the e-Service

33. Service-Product AnalysisData Mining of Attribute Consumption

34. Approach #2: Analysis of Customer Behavior Inside the e-Service

35. Analysis of Customer Behavior Inside the e-Service e-Service Analysis Process Break an e-Service (web site) down into pages or page groups that have some business meaning Tabulated the frequency of visitors moving from page I to page J (i.e., clicking along each link) Calculate the probabilities, conditional upon being on a page, of moving to some other page (or page group) Use probabilities to identify … Frequent paths within the site Probability of customer following certain paths through the site Probability of checking out Probability of abandoning a shopping cart

37. Service-Product AnalysisData Mining of User Navigation Along Paths Within Site Page Network

38. Service-Product AnalysisData Mining of User Navigation Along Paths Within Site Page Network

39. Service-Product AnalysisData Mining of User Navigation Along Paths Within Site Page Network

40. Service-Product AnalysisData Mining of User Navigation Along Paths Within Site Page Network Advantages of Approach Reduces a complex web site down into a set of sub-sections related to business issues e-Service sub-sections can be related to other issues (i.e., server technology, capacity management)

41. Service-Product AnalysisData Mining of User Navigation Along Paths Within Site Page Network Shortcomings of Approach Difficult analysis … involves lots of data collection, cleaning, analysis Requires an analyst with a knowledge of queueing theory Works well for a single touchpoint of an e-Service (the web site), but maybe not for multiple touchpoints

42. Service-Product AnalysisData Mining of User Navigation … Implication of Peer-to-Peer Customer Service

43. Approach #3: Analysis of Exit Points

44. Analysis of Exit PointsMicrosoft’s “Funnel Mining” Microsoft’s MSN uses a data mining method that identifies where customers enter site where customers leave site Focus of the technique is on customer loyalty and customer retention Objective is to identify common places within e-service that drive customers away (cause them to abandon their shopping cart and exit the system) Results presented as a picture of a “funnel”

45. Analysis of Exit PointsMicrosoft’s “Funnel Mining”

46. Data Mining for Real-Time Marketing: Why You Should Design Your e-Service for Data Collection

47. Data Mining for Real-Time Marketing When designing an e-service, one must consider how to handle collection and analysis of data from the e-service In general, web server logs provide insufficient data “Many of the problems of dealing with web server log data can be resolved by properly architecting the e-commerce sites to generate data needed for mining.” (Kohavi, KDD2001, 2001)

48. Data Mining for Real-Time MarketingObjectives Objectives Targeting Personalization Association Knowledge Management Clustering Estimation Prediction Decision Trees Experimentation

49. Data Mining for Real-Time Marketing Identify business problem for e-service Data collection Feature selection Choose variables that are relevant for the business problem Variable transformation Sampling of observations Samples for building and testing models of the e-service problem Over-sampling of rare events that are important to the e-service problem Build model Analyze model relative to other models of e-service problem Deploy model into e-service Program it into an e-service module Test module for scalability – so it won’t affect e-service performance Deploy it inside e-service

50. Data Mining for Real-Time Marketing

51. Data Mining for Real-Time MarketingModeling Customer Behavior Modeling Behavior Category Modeling Have a set of known customer categories of interest Objective is to use data to classify individual customers within a category Use category membership to infer future customer behavior Cluster Modeling No known classes of customers Objective is to use data to statistically generate classes (“clusters”) of customers (often via “cluster analysis”) Use customer clusters to infer future customer behavior

52. Data Mining for Real-Time MarketingBlue Martini Architecture for Data Mining

53. Data Mining for Real-Time MarketingDan Greening’s Architecture

54. Data Mining for Real-Time MarketingWhat Not to Do Do not plan to use data collected from the Web Server Web server logs do not identify sessions or users Web server logs need to be reconciled/linked together with transactional data Web server logs lack critical business events Web server logs do not store form information Web server logs contain URLs, but no semantic information about what the URLs mean Web server logs lack information about modern sites that generate dynamic content Web server logs are flat files on multiple file systems, possibly in different time zones Web server logs contain redundant information Web server logs lack important information that can be collected using other means

55. Data Mining for Real-Time MarketingWhat Not to Do Do not collect data from a network monitor (a “packet sniffer”) Has all of the same problems as collecting data at the Web Server

56. Data Mining for Real-Time MarketingWhat to Do Correct approach Architect site to include data collection Collect data at the application server (eCommerce server) level Resolves all of the problems of the packet sniffer approach and the web server approach

57. Data Mining for Real-Time MarketingWhat to Do Runner-Up Approach (for Legacy Websites) Use a solution that embeds JavaScript codes within existing web pages JavaScript code generates messages to a data collection server each time some business activity is performed on a page on the site Many problems with data collection still remain with this approach Additional work to add lots of codes to each page Browser compatibility issues Privacy concerns raised by consumers seeing JavaScript in their browser Data is collected by a third system, which must then be reconciled with data from other parts of the system Events at the application server cannot be logged by this system

58. Summary

59. Summary When designing your e-service, consider the role of data you may need or want to collect Design your e-service for data collection Determine how you will analyze the data Decide how you plan to make use of the insights gathered from the data mining exercises