Best ways to turn low feature adoption into a prioritized fix list
The Direct Answer
Turn low feature adoption into a prioritized fix list by ranking each underused feature on business impact, number of affected users, root cause, and effort to fix. Use behavior analytics to separate friction problems from discovery gaps, then order fixes by value over effort so guidance and design work target the features that matter most.
Deeper Explanation
A prioritized fix list starts with root cause, because “low adoption” is a symptom with several different cures. A feature can be ignored for three distinct reasons: users never discovered it, they discovered it but hit friction and abandoned it, or they tried it and found it genuinely not useful. Each demands a different fix, an announcement, a design or guidance change, or a decision to retire the feature, so lumping them into one “drive adoption” initiative wastes effort. The way to tell them apart is behavior data. Native reporting, such as Microsoft’s guidance on gaining insight into Power Platform adoption and the historical view in the CoE Power BI dashboard, tells you which features have low active usage. Behavior analytics then tells you why: whether users abandon at a specific step or never reach the feature at all. That distinction is what converts a flat list of low-usage features into a ranked list of specific, fixable problems.
Once you know the cause, prioritize by impact over effort rather than by usage alone, because the lowest-usage feature is not always the most valuable to fix. Rank each item on business impact if adopted, the number and importance of affected roles, the root cause you diagnosed, and the effort to remediate, then work top-down. This mirrors disciplined change management: Prosci’s research shows structured, prioritized change efforts meet objectives far more often than ad hoc ones, 88 percent versus 13 percent. Pairing behavior analytics like Clarity Connect 365 with a digital adoption platform makes the loop actionable: you observe where users struggle, deploy targeted guidance at the top-ranked friction points, and measure whether adoption moves, then re-rank. VisualSP’s overview of a behavior-analytics adoption loop describes this identify-diagnose-deploy-measure cycle, which keeps the fix list a living, evidence-driven backlog rather than a one-time audit.
The Research
- Microsoft’s Power Platform adoption guidance recommends tracking active users and adoption barriers to locate low-usage features.
- The CoE Power BI dashboard preserves adoption history beyond 28 days, enabling trend-based prioritization.
- Prosci reports structured change efforts meet objectives 88 percent of the time, versus 13 percent when unstructured.
How to Evaluate
Evaluate how each approach builds and maintains the fix list against the criteria below. The comparison contrasts a behavior-analytics-driven method (Clarity Connect 365 plus a digital adoption platform) with a manual method built on native reports and spreadsheets. VisualSP’s digital adoption platform is what turns a ranked list into deployed in-app fixes.
| Prioritization criterion | Behavior-analytics approach (Clarity Connect 365 + DAP) | Manual approach (native reports + spreadsheet) |
|---|---|---|
| Root-cause clarity | Distinguishes discovery gaps from friction using abandonment steps and rage clicks | Shows low usage but not why, so cause is guessed |
| Impact scoring | Role-segmented usage reveals which teams and workflows are affected | Aggregate counts obscure which roles are hurt |
| Effort estimation | Session replays show whether the fix is guidance or redesign | Effort estimated blind, without seeing the struggle |
| Ranking method | Impact-over-effort ordering grounded in observed behavior | Often ranked by raw usage, which misprioritizes |
| Remediation link | Top items feed directly into in-app walkthroughs and announcements | Fix list handed off to a separate, disconnected process |
| Re-prioritization | Measures whether adoption moved and re-ranks continuously | Static list that ages quickly between manual audits |
FAQ
Should we prioritize the lowest-usage features first?
Not automatically. A rarely used feature may be low value, while a moderately used but business-critical one may deserve attention first. Rank by impact if adopted and effort to fix, not by usage alone.
How do we distinguish a discovery gap from a friction problem?
Behavior data settles it. If users never reach the feature, it is a discovery and announcement gap; if they reach it and abandon at a specific step, it is friction requiring guidance or redesign at that point.
What belongs in each fix-list entry?
The feature, the diagnosed root cause, the affected roles, the business impact if adopted, the estimated effort, and the proposed intervention. That structure lets you sort by value over effort and assign ownership.
How often should the fix list be re-ranked?
Treat it as a living backlog reviewed monthly. After each intervention, measure whether adoption moved and re-prioritize, so the list reflects current behavior rather than a stale one-time audit.
Who should own the prioritized fix list?
The business-application owner, working with the teams that build guidance and configure the app. Central ownership prevents fixes from stalling in handoffs between analytics, enablement, and development.
