Unveiling the Life Cycle of User Feedback: Best Practices from Software Practitioners
Bibliographic record
Abstract
User feedback has grown in importance for organizations to improve software products. Prior studies focused primarily on feedback collection and reported a high-level overview of the processes, often overlooking how practitioners reason about, and act upon this feedback through a structured set of activities. In this work, we conducted an exploratory interview study with 40 practitioners from 32 organizations of various sizes and in several domains such as e-commerce, analytics, and gaming. Our findings indicate that organizations leverage many different user feedback sources. Social media emerged as a key category of feedback that is increasingly critical for many organizations. We found that organizations actively engage in a number of non-trivial activities to curate and act on user feedback, depending on its source. We synthesize these activities into a life cycle of managing user feedback. We also report on the best practices for managing user feedback that we distilled from responses of practitioners who felt that their organization effectively understood and addressed their users' feedback. We present actionable empirical results that organizations can leverage to increase their understanding of user perception and behavior for better products thus reducing user attrition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".