Feature Request Template for B2C Products
B2C feature requests come from thousands of users across dozens of channels — support tickets, app store reviews, NPS verbatim, social media, community forums, and user research sessions. The challenge is not collecting requests; it is aggregating signals from noisy, high-volume, often contradictory sources and making a prioritization decision that serves the entire user base rather than the most vocal minority.
This template standardizes the analysis required for B2C feature prioritization. It covers how to aggregate multi-channel signals, which user segments are asking and why their requests may differ from the broader population, how to estimate the engagement and retention impact of building the feature, and how to assess the viral and growth potential that makes some B2C features more valuable than their request volume alone would suggest. Use it for consumer apps, social platforms, consumer fintech, health and fitness products, and any B2C digital product.
Why B2C feature request prioritization is harder than it looks
The loudest B2C users are rarely the most representative. Power users who file support tickets, leave app store reviews, and post on Reddit are typically in the top 5–10% of engagement. They are valuable contributors, but their requests often reflect advanced use cases that the median user never encounters. Building for the most vocal minority while ignoring the silent majority is one of the most common B2C product mistakes.
Conversely, the silent majority is not silent for lack of opinions — they are silent because they lack the motivation or the channel to express preferences. User surveys, in-app prompts, and A/B tests reveal the silent majority's preferences in a way that public forums cannot. The synthesis of public signal (loud, biased) and research signal (representative, requires effort to collect) is where good B2C prioritization happens.
The other uniquely B2C challenge is the gap between stated and revealed preferences. Users will enthusiastically vote for a feature in a survey and then not use it when it ships. "We want offline mode" gets hundreds of upvotes; offline mode ships; 15% of users ever use it. Revealed preference — what users actually do in A/B tests and early access — is far more predictive of adoption than stated preference from surveys or feature requests.
Template sections
5 sections covering the complete feature request workflow.
Multi-channel signal aggregation
Aggregate the request signal from every channel where it appears: support tickets, app store reviews, social media mentions, NPS verbatim, community forum votes, and user research sessions. Show the total volume and the channel breakdown — the distribution tells you whether this is a surface-level social media complaint or a deep operational pain point.
Signal aggregation for "Offline mode" feature request: | Channel | Volume | Date range | Notable | |---|---|---|---| | Support tickets | 847 | Last 6 months | 3rd most common support category | | App store reviews mentioning "offline" | 234 reviews (1.8% of all reviews) | Last 3 months | Average rating of reviewers mentioning offline: 2.3 stars | | Reddit mentions | 89 posts/comments | /r/AppName | 67% positive framing ("I wish"), 33% negative ("I left because") | | In-app survey (n=2,400) | 34% ranked "offline access" as most-wanted feature | Last quarter | — | | Twitter/X mentions | 156 mentions | Last 90 days | Includes 12 from accounts with >10K followers | | NPS verbatim (detractors) | 23 mentions in detractor responses | Last quarter | 8% of detractor responses mention offline | Total signal strength: HIGH — consistent across channels, appearing in detractor NPS responses (churn signal)
Tips
- Cross-channel consistency is the key signal quality indicator — a feature that appears in support tickets AND app store reviews AND NPS verbatim AND social media is a genuine pain point
- App store review sentiment matters: a feature mentioned in 2-star and 3-star reviews is a churn-risk signal; the same feature in 5-star reviews is a delight opportunity
- The NPS detractor verbatim is the most actionable signal — features mentioned by detractors are directly correlated with churn risk
User segment analysis
Break down who is requesting the feature. B2C users are not monolithic — power users, new users, paying users, and churned users have different preferences and different influence on the aggregate metrics. A feature loved by power users but irrelevant to new users may improve retention for the top 10% while doing nothing for the 90%.
Segment breakdown for offline mode requests: | Segment | % of requests | Segment % of total users | Over/under index | |---|---|---|---| | Power users (daily active, 12+ months) | 52% | 15% | 3.5x over-indexed | | Commuters (inferred from usage pattern: mobile, morning/evening) | 28% | 22% | 1.3x over-indexed | | New users (< 30 days) | 8% | 35% | 0.2x under-indexed | | Paying users | 34% | 18% | 1.9x over-indexed | | Users who churned (via exit survey) | 19% of exit survey respondents | — | Key churn driver |
Tips
- Calculate the over/under index (% of requests vs. % of user base) to identify whether the requesting population is representative or skewed
- Churned user exit survey data is the highest-quality signal for churn-related feature requests — users who left because of the missing feature are the most motivated to be specific about why
- New user segment under-indexing is common for power features — it does not mean the feature is unimportant, but it does mean it should not be in the core onboarding flow
Engagement and retention impact estimation
Estimate the impact on engagement and retention if this feature ships. Use cohort analysis on existing behaviors to find a proxy: users who already use offline workarounds (downloaded files, cached views) may have different retention than those who do not. This reveals whether the feature would change behavior or simply formalize something users already do.
Engagement impact estimation for offline mode: Proxy analysis: Users who access the app in airplane mode (inferred from network data) currently have 18% lower D30 retention than connected users (28% vs. 34%). These users represent 8% of the user base. Hypothesis: Offline mode would eliminate the friction for these users and improve their D30 retention closer to the connected-user baseline. If we close the gap by 50% (from 28% to 31%), the D30 retention improvement for the total user base would be +0.24pp. Session length proxy: Users who access the app during commute hours (6-9am, 5-8pm) have 40% shorter sessions than mid-day users — consistent with connectivity limitations shortening usable session time. Estimated session length impact: +15% for the commuter segment (22% of users) → +3.3% total session length improvement
Tips
- Proxy analysis using existing behavioral data is more reliable than survey estimates for predicting engagement impact
- Find the "offline workaround" user cohort — users who go to unusual lengths to access content offline likely represent the most motivated adopters and will show the highest engagement lift
- Be conservative in impact estimates — features rarely lift all metrics by the full amount the proxy analysis suggests, because adoption is rarely 100%
Community sentiment and app store impact
Assess the community sentiment dynamics around this feature. For consumer apps, app store ratings and community health directly affect discoverability, organic growth, and the viral loop. A feature that resolves a common complaint can produce a measurable improvement in app store rating within 2–4 weeks of launch if users update their reviews.
Community sentiment analysis: App store review analysis: - Reviews mentioning "offline": 234 reviews, average rating 2.3 stars vs. 4.1 star overall average - Estimated uplift from resolving this complaint: if 30% of offline-mentioning reviewers update their review positively, weighted average could improve by +0.08 stars - Current app store rating: 4.1/5.0 stars — a +0.08 uplift would help maintain position above the 4.0 threshold Social media dynamics: - 12 accounts with >10K followers have mentioned offline as a pain point - If any of these announce the feature at launch, potential organic reach: 500K+ - Community forum vote count: 847 upvotes (top 5% of all feature requests) Community velocity: The offline mode request has been growing 15% per quarter in total mentions — accelerating, not stable.
Tips
- App store ratings below 4.0 cause significant discoverability drops — features that address the most common low-rating complaints directly affect organic growth
- Track community request velocity (is the signal growing or shrinking?) — accelerating requests signal a market gap that competitors may fill if you do not
- Plan a release announcement strategy that re-engages the users who left negative reviews about this feature — a targeted push notification can drive review updates
Growth and viral potential
Assess whether this feature has inherent growth properties: is the feature shareable, collaborative, or does it create distribution through user activity? B2C features with viral potential are worth more than their direct engagement impact suggests — they create organic growth loops that compound over time.
Growth and viral potential analysis: Sharing potential: MODERATE - Users with offline content could share it with others who cannot access the app due to connectivity — e.g., "here, I downloaded this workout for you" - Offline sharing creates a natural social proof moment at a point where the sharing user is demonstrating value to a potential new user Collaborative potential: LOW - Offline mode by its nature is single-user — the content can be downloaded but not co-created offline Growth mechanism: INDIRECT - Commuters are socially connected clusters — coworkers ride the same subway, have the same connectivity problem, and influence each other. One satisfied commuter user can create 2-3 referrals by demonstrating the app during the commute. - Viral coefficient estimate: 0.08 (low direct virality but positive indirect effect via reduced churn in high-density social clusters)
Tips
- Even features with low direct viral potential can have indirect growth effects through reduced churn in high-referral user segments
- Look for "social proof moments" — points where using the feature is naturally visible to potential new users (working out in public, using the app on public transportation)
- Collaborative offline features (offline documents, shared downloads) have significantly higher viral potential than solo offline features
Copy-paste template
## B2C Feature Request **ID:** [FR-XXX] | **Date:** [Date] | **Submitted by:** [Name] **Priority recommendation:** [Critical / High / Medium / Low / Research needed] --- ### Feature Description **User request (summary):** [What users are asking for, in one paragraph] **Problem statement:** [What problem does this solve? Not the solution — the problem.] --- ### Multi-Channel Signal Aggregation | Channel | Volume | Date range | Key observation | |---|---|---|---| | Support tickets | [N tickets] | [Range] | [Pattern or notable data] | | App store reviews | [N reviews, avg rating of requesters] | [Range] | [Sentiment] | | Community forum votes | [N upvotes, rank in all requests] | [Range] | [Velocity trend] | | Social media mentions | [N mentions] | [Range] | [Notable accounts?] | | NPS verbatim | [N mentions] | [Range] | [Promoter / Passive / Detractor] | | User research | [N sessions] | [Range] | [Research finding] | **Signal strength:** [High / Medium / Low — reasoning] **Signal velocity:** [Accelerating / Stable / Declining] --- ### User Segment Analysis | Segment | % of requests | % of user base | Index | Insight | |---|---|---|---|---| | [Power users] | [X%] | [Y%] | [X/Y] | [Over/under-indexed] | | [New users] | [X%] | [Y%] | [X/Y] | [Over/under-indexed] | | [Paying users] | [X%] | [Y%] | [X/Y] | [Over/under-indexed] | | [Churned users (exit survey)] | [X% of exit responses] | — | — | [Churn driver signal] | **Segment conclusion:** [Which segment is this feature primarily for? Does serving them help or hurt other segments?] --- ### Engagement and Retention Impact **Proxy analysis:** [Describe a behavioral proxy in existing data that helps estimate the feature's impact] **Estimated impact:** - D30 retention change: [+/-X pp for Y segment → Z pp total user base impact] - Session frequency change: [Estimate] - Session length change: [Estimate] **Confidence level:** [High (based on strong behavioral proxy) / Medium (based on survey data) / Low (estimate only)] --- ### Community Sentiment and App Store Impact - Average rating of reviews mentioning this feature: [X/5 vs. overall X/5] - Estimated app store rating impact if resolved: [+/-X stars] - Community request rank: [#N of all feature requests by vote count] - Request velocity: [Accelerating / Stable / Declining — trend description] --- ### Growth and Viral Potential - **Sharing potential:** [High / Medium / Low — reasoning] - **Collaborative potential:** [High / Medium / Low — reasoning] - **Estimated viral coefficient:** [X — how many new users does one feature-using user generate?] - **Growth mechanism:** [Description of how this feature creates organic growth, if any] --- ### PM Assessment **Priority rationale:** [Why this is or is not a near-term priority] **Build vs. alternative:** [Build natively / Integrate partner solution / Workaround: describe] **Estimated impact vs. effort:** [High/Medium/Low impact, High/Medium/Low effort] **Recommendation:** [Ship in Q[X] / Validate with A/B test / Research needed / Decline — reasoning]
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