2026-09-19T17:24:10Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/675622026-06-06T01:05:15Zcom_1721.1_7582com_1721.1_7581com_1721.1_80738com_1721.1_1773col_1721.1_131023col_1721.1_80739
Ricardo Valerdi.
Latner, Avi
System Design and Management Program.
System Design and Management Program.
Massachusetts Institute of Technology. Engineering Systems Division
2011-12-09T21:24:06Z
2011-12-09T21:24:06Z
2011
2011
http://hdl.handle.net/1721.1/67562
761702455
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 Management
47 p.
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
System Design and Management Program.
Feature performance metrics in a service as a software offering
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="eb4bbcb4-d2f3-413b-af70-3989b8b9a16e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Feature performance metrics in a service as a software offering</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2011</PublicationDate>
<Authors>
<Author>
<DisplayName>Latner, Avi</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Engineering Systems Division.</Keyword>
<Keyword>System Design and Management Program.</Keyword>
<Abstract>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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>