Web Mining-Based Objective Metrics for Measuring Website Navigatability

Web site design is critical to the success of electronic commerce and digital government. Effective design requires appropriate evaluation methods and measurement metrics. The current research examines Web site navigability, a fundamental structural aspect of Web site design. We define Web site navigability as the extent to which a visitor can use a Web site’s hyperlink structure to locate target contents successfully in an easy and efficient manner. We propose a systematic Web site navigability evaluation method built on Web mining techniques. To complement the subjective self-reported metrics commonly used by previous research, we develop three objective metrics for measuring Web site navigability on the basis of the Law of Surfing. We illustrate the use of the proposed methods and measurement metrics with two large Web sites

Feature performance metrics in a service as a software offering

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 46-47).Software as a Service (SaaS) delivery model has become widespread. This deployment model changes the economics of software delivery but also has an impact on development. Releasing updates to customers is immediate and the development, product and marketing teams have access to customer usage information. These dynamics create a fast feedback loop between developments to customers. To fully leverage this feedback loop the right metrics need to be set. Typically SaaS applications are a collection of features. The product is divided between development teams according to features and customers access the service through features. Thus a framework that measure feature performance is valuable. This thesis provides a framework for measuring the performance of software as a service (SaaS) product features in order to prioritize development efforts. The case is based on empirical data from HubSpot and it is generalized to provide a framework applicable to other companies with large scale software offerings and distributed development. Firstly, relative value is measured by the impact that each feature has on customer acquisition and retention. Secondly, feature value is compared to feature cost and specifically development investment to determine feature profitability. Thirdly, feature sensitivity is measured. Feature sensitivity is defined as the effect a fixed amount of development investment has on value in a given time. Fourthly, features are segmented according to their location relative to the value to cost trend line into: most valuable features, outperforming, under-performing and fledglings. Finally, results are analyzed to determine future action. Maintenance and bug fixes are prioritized according to feature value. Product enhancements are prioritized according to sensitivity with special attention to fledglings. Under-performing features are either put on "life-support", terminated or overhauled.by Avi Latner.S.M.in Engineering and Managemen