1) to understand the value of personalization to customers and IBM and 2) to develop the strategy for bringing personalization to the ibm.com public web site which ensures that the top-priority goals of customers and the business are met. The strategy was formulated by conducting literature reviews and worldwide brainstorming sessions in the Research
Division; executing heuristic usability evaluations of ibm.com and competitor sites; and employing user-centered design methods to understand customers' views on the value of personalization of the ibm.com site as well as IBM's business requirements. Customers participated in three iterations of user studies (group and individual usability sessions) that investigated potential personalization features and their relative value to site visitors. Low and mid-level fidelity prototypes were developed to illustrate these candidate personalization features and evaluate them in the context of user tasks regarding the purchase and support of desktop and notebook systems, servers, and personal computer options and accessories. The research illustrates that personalizing interactions for e-business requires more than implementing a single function; it involves the development of a collection of functions that together achieve the larger goal. The personalization strategy and the set of 12 identified personalization features with high value to customers and the business are described. A Personalization Value Model outlining the value of personalization to customers and the business was created and validated through contextual analysis and affinity diagrams of data collected from ibm.com customers and stakeholders in the business.
Human-computer interaction (HCI) will change when the systems with which we interact make broad use of personal information about users. Information about a user can be either explicitly gathered or implicitly obtained. We define the use of information about a user to alter the content and functionality of the user experience Personalizing Interaction
. While there has been a fair amount of research aimed at enabling systems to tailor interaction based on some understanding of the user, prior work has examined fairly narrow contexts. Examples of this research on techniques or methods to infer user goals include click-stream analysis , collaborative filtering [3,12,13], and data mining of web user logs [6,8,10,14]. Newer techniques include using pattern classification and developing recommender systems [4,11], combining historical profile data and online visitation patterns  and online heuristic decision-making based on flowchart and rule-based constructs . In general
, these methods attempt to predict user interests or goals and automatically personalize or adapt the presentation of information. Traditionally, most interactions with computers take place between a system that understands little of the particular user (i.e., they have no or a very limited user model) and individuals who have limited understanding of the system or application (i.e., they have a limited conceptual model of the system). Over the last few decades
, the general population has developed more sophisticated conceptual models of the technology they use, while the technology has made relatively small advances in understanding the human it serves. We view a future in which human-computer interaction is greatly enhanced through advances in the ability of technology to employ personal information about users to realize better
, more valuable interactions for users and providers alike. Although computer systems are often seen as entities in and of themselves, in e-commerce and many other domains they are really a set of tools which facilitate business transactions . This research begins to provide a better understanding of the context in which users will provide various kinds of information to systems so that the systems can provide value to the interaction between humans who communicate and interact with each other through the technology.
We define personalizing a website
to mean using personal information about an individual to tailor the experience for that individual on the site. We consider personal information as including a very broad range of elements - from basic identifying information such as age and income to information we are just beginning to be able to collect such as intention or emotional state. We will use the terms "personalize" and "personalization" here primarily because these terms are most commonly used in current Web applications and research. The terms "adaptive", "context-aware", and "tailored experience" have also been used to describe the elements we are addressing. Further, we define a personalization policy
as a decision made by an e-commerce company involving the handling of personal data on the company’s website. A personalization feature
is a method for collecting and using personal information in order to tailor a website visitor’s experience on the website. A personalization policy applies to the whole website
, while a feature provides functionality for a particular task on the site. Examples of personalization policies include the degree of visibility and control over personal data that is given to website visitors. Examples of personalization features include collaborative filtering and adaptive navigation.
We view personalization for e-commerce as involving an exchange between at least two parties. In general there are two roles in the interaction - that of customer
and that of provider
of the product or service. Any interaction in which information about the parties involved is used to adapt the interaction
, can be said to be "personalized."
Second, we believe the essential goal of personalization is to provide increased value to both parties though the use of personal information [5,9]. Most research to date has focused on personalization as involving just the user (customer) of a system. The basic model is that a person divulges information in return for some promised benefit. This exchange can be viewed as involving a value proposition in which the value to the customer is a function of the costs of divulging information and the perceived benefits of doing so. We extend this notion of a value proposition for personalization to include consideration of the provider's value proposition - that is, the value of any personalization feature to the organization responsible for developing the system is a function of the cost of implementation and the benefits obtained from doing it. Thus, for the Customer, the value of personalization = f ( cost of divulging, perceived benefits) and for the Provider, value is a function of (cost of gathering information, perceived value). For the Provider's Value Proposition (PVP), costs and benefits can generally be expressed in monetary units. For the Customer's Value Proposition (CVP), costs and benefits are more complex, and can involve other factors. Specifically, we suggest that the costs and benefits must be viewed within a framework of human values that extends beyond simple economic benefit and includes concepts of security, privacy, trust, and business relationships. For example, to go one level deeper in our framework, we view Customer Cost for a personalization feature to be a function of the information requirements of the feature (e.g., explicit or implicit information), the context of the interaction (e.g., for one-time visit or long-term relationship), customer trust in the provider (e.g., well known or new contact), privacy (how much control does the user have over access to and use of their personal information), and personal predispositions to divulge information (e.g., no fear or generally wary).