Website optimization with web metrics: a case study
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Web Analytics: An Hour a Day
Implicit measures of lostness and success in web navigation
Interacting with Computers
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
Practical guide to controlled experiments on the web: listen to your customers not to the hippo
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Undo and erase events as indicators of usability problems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
Effects of search success on search engine re-use
Proceedings of the 20th ACM international conference on Information and knowledge management
The effect of links on networked user engagement
Proceedings of the 21st international conference companion on World Wide Web
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More and more products and services are being deployed on the web, and this presents new challenges and opportunities for measurement of user experience on a large scale. There is a strong need for user-centered metrics for web applications, which can be used to measure progress towards key goals, and drive product decisions. In this note, we describe the HEART framework for user-centered metrics, as well as a process for mapping product goals to metrics. We include practical examples of how HEART metrics have helped product teams make decisions that are both data-driven and user-centered. The framework and process have generalized to enough of our company's own products that we are confident that teams in other organizations will be able to reuse or adapt them. We also hope to encourage more research into metrics based on large-scale behavioral data.