  Capturing users engagement is crucial for gathering feedback about the
features of a software product. In a market-driven context, current approaches
to collect and analyze users feedback are based on techniques leveraging
information extracted from product reviews and social media. These approaches
are hardly applicable in bespoke software development, or in contexts in which
one needs to gather information from specific users. In such cases, companies
need to resort to face-to-face interviews to get feedback on their products. In
this paper, we propose to utilize biometric data, in terms of physiological and
voice features, to complement interviews with information about the engagement
of the user on the discussed product-relevant topics. We evaluate our approach
by interviewing users while gathering their physiological data (i.e.,
biofeedback) using an Empatica E4 wristband, and capturing their voice through
the default audio-recorder of a common laptop. Our results show that we can
predict users' engagement by training supervised machine learning algorithms on
biometric data, and that voice features alone can be sufficiently effective.
The performance of the prediction algorithms is maximised when pre-processing
the training data with the synthetic minority oversampling technique (SMOTE).
The results of our work suggest that biofeedback and voice analysis can be used
to facilitate prioritization of requirements oriented to product improvement,
and to steer the interview based on users' engagement. Furthermore, the usage
of voice features can be particularly helpful for emotion-aware requirements
elicitation in remote communication, either performed by human analysts or
voice-based chatbots.
