  [Context and motivation] Quality requirements (QRs) are inherently diffi-cult
to manage as they are often subjective, context-dependent and hard to fully
grasp by various stakeholders. Furthermore, there are many sources that can
provide input on important QRs and suitable levels. Responding timely to
customer needs and realizing them in product portfolio and product scope
decisions remain the main challenge. [Question/problem] Data-driven
methodologies based on product usage data analysis gain popularity and enable
new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product
development. Can these be effi-ciently combined with established top-down,
forward-driven management of QRs? [Principal idea / Results] We propose a model
for how to handle decisions about QRs at a strategic and operational level,
encompassing product deci-sions as well as business intelligence and usage
data. We inferred the model from an extensive empirical investigation of five
years of decision making history at a large B2C company. We illustrate the
model by assessing two in-dustrial case studies from different domains.
[Contribution] We believe that utilizing the right approach in the right
situa-tion will be key for handling QRs, as both different groups of QRs and
do-mains have their special characteristics.
