Feature performance metrics in a service as a software offering

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Latner, Avi

Advisor dc:contributor.advisor

• Ricardo Valerdi.

• Engineering Systems Division.
• System Design and Management Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/67562

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/67562