[Rick Retirement Planner]

I’m non-technical and curious: I have customer feedback from surveys, support tickets, and a few interviews. I’d like to use AI to help turn those insights into a clear, prioritized product roadmap—but I don’t know where to start.

Could you share a simple, beginner-friendly workflow or checklist? Helpful points include:

• What data to collect and how to prepare it (easy formats or examples)
• Which AI tools or approaches work well for extracting themes and suggestions
• How to prioritize features and turn themes into roadmap items
• Ways to validate AI suggestions with customers or the team
• Common pitfalls and quick tips to avoid them

Please share simple templates, prompts, or short examples if you have them. I’m looking for practical steps I can try this week. Thanks!

[Jeff Bullas]

Nice starting point — focusing on turning customer insights into a roadmap is exactly the practical problem to solve.

AI doesn’t replace product judgment, but it accelerates the messy parts: cleaning feedback, finding themes, and proposing prioritized actions you can test fast. Below is a clear, do-first plan you can use today.

What you’ll need

• Collection of customer inputs (interviews, support tickets, NPS comments, survey answers).
• A simple spreadsheet or document to paste the raw text.
• An AI assistant (chat-based) you can paste text into — or a simple automation that uploads the spreadsheet.
• A prioritization framework you’re comfortable with (RICE, ICE, MoSCoW).

Step-by-step: from insights to roadmap

1. Gather and centralize. Paste customer quotes into one document or sheet. Expect duplicates and noise — that’s fine.
2. Clean & group with AI. Ask the AI to categorize comments into themes (e.g., onboarding, reliability, pricing). Output: labeled list or table.
3. Synthesize themes into problems. For each theme, ask the AI to write a one-line problem statement and the underlying customer need.
4. Generate solution ideas. For each problem, ask the AI for 3–5 potential features or experiments (keep them small and testable).
5. Prioritize. Score ideas with a simple framework (e.g., impact x confidence / effort). Use the AI to estimate effort and confidence based on your team size and velocity.
6. Build a 90-day roadmap. Place 3–6 prioritized items into quarters: Now (next 2 weeks), Next (month), Later (2–3 months). Include a clear metric and experiment to validate each item.
7. Validate fast. Run quick experiments or smoke tests and feed results back into the AI for re-prioritization.

Copy-paste AI prompt (primary)

Paste this exactly into your chat with an AI assistant:

“I will paste a list of customer comments. Please:

• Group them into themes with short labels (3–6 themes max).
• For each theme, write a one-line problem statement and the customer need it implies.
• Propose 3 short, testable solutions (experiments or small features) for each problem.
• Estimate effort for each solution on a 1–5 scale (1 = <1 week, 5 = >2 months) and suggest the one metric to measure success.

Here are the comments: [paste comments]”

Prompt variant — prioritization

Use after you have solution ideas:

“I have these proposed solutions with estimated efforts. For each, score impact (1–5) and confidence (1–5). Then calculate a priority score = (impact x confidence) / effort and return a ranked list with brief reasoning.”

Example (quick)

Comments: “Onboarding is confusing”, “I can’t find the pricing page”, “Feature X is slow”. AI groups into Onboarding, Pricing, Performance. It proposes: guided tour, clearer pricing CTA, performance profiling. You prioritize guided tour first as high impact, low effort.

Mistakes & fixes

• Waiting for perfect data — fix: start with what you have, iterate.
• Overbuilding features from single comments — fix: require at least two signals or a testable hypothesis.
• Letting AI decide priorities without your context — fix: review and adjust scores based on team reality.

Action plan (next 48 hours)

1. Gather 50–200 customer comments into a doc.
2. Run the primary prompt above to get themes and solutions.
3. Pick one experiment to run this week and define its success metric.

Small steps, tested quickly, build confidence and momentum. Use AI to do the heavy lifting on grouping and idea generation — you add judgment and constraints. Iterate every sprint.

[Becky Budgeter]

Nice work — this plan is practical and actionable. One small correction: instead of pasting a single rigid prompt verbatim, tell the AI what you want in plain language and include your team context (team size, sprint length, current scope). AI is great at grouping and idea generation, but it often needs that context to give realistic effort and confidence estimates.

Do / Do-not checklist

• Do centralize raw feedback (even messy) and add basic context (who the customer is, how often this hits).
• Do ask AI to group themes, write short problem statements, and suggest small, testable experiments.
• Do validate with at least two signals before building a big feature.
• Do-not let AI’s effort/confidence numbers be final — use them as a starting point that you adjust with team facts.
• Do-not wait for perfect data; start small and iterate.

What you’ll need

• A folder or sheet with customer comments (50–200 is a useful target, but fewer is fine).
• A short note about your team: size, sprint length, and what you can ship in 2 weeks.
• An AI chat or simple automation to help group and draft ideas.
• A prioritization rule you’ll actually follow (simple formula or rank).

Step-by-step: how to do it and what to expect

1. Gather. Dump comments into one doc with a column for source and customer segment. Expect duplicates and noise.
2. Ask for grouping. Tell the AI: “Group these into 3–6 themes and label them.” Give team context so suggested effort is realistic. Output: a labeled list you can edit.
3. Synthesize problems. For each theme, have the AI write a one-line problem and the underlying customer need.
4. Generate experiments. For each problem, get 2–4 small, testable ideas (no big projects). Keep them scoped to one sprint where possible.
5. Prioritize. Use a simple score (e.g., impact x confidence / effort). Adjust AI numbers with your team realities and pick 1–2 to test now.
6. Run & learn. Run quick tests (A/B, smoke test, manual concierge). Feed results back in and re-prioritize.

Worked example (quick)

• Raw comments: “Onboarding confusing”, “Can’t find pricing”, “App slow on login”, “I gave up during signup”.
• AI groups into: Onboarding, Pricing, Performance.
• Onboarding problem: “New users drop off before they see value.” Experiment ideas: a one-step guided tour (1–2 week effort), and a simplified signup flow (2–4 week effort). Measure: conversion from signup to first key action.
• Prioritize: guided tour = high impact, low effort (test first); simplified signup = medium impact, medium effort (next).

Tip: start with one quick experiment you can measure in two weeks — small wins build momentum and give clearer data for the next prioritization round. Would you like a short checklist to paste into your document to guide the AI sessions?

[aaron]

Smart correction — include team context. That’s the difference between plausible ideas and ones you can actually ship.

Problem: customer feedback sits in silos, themes aren’t prioritized, and product teams end up building features that don’t move KPIs.

Why it matters: turning raw insights into a testable roadmap shortens time-to-value, reduces wasted development, and raises retention and conversion — measurable business outcomes.

Lesson from practice: AI accelerates synthesis and idea generation, but you must force the output into measurable experiments tied to team capacity. Treat AI as a rapid analyst and idea factory — you provide constraints and judgment.

What you’ll need

• 50–200 customer comments (support tickets, NPS verbs, interview quotes) in one sheet.
• A 2–3 sentence team context note: team size, sprint length, what you can ship in 2 weeks.
• AI chat tool or simple automation to paste the data into.
• A prioritization rule (impact x confidence / effort) you’ll follow.

Step-by-step (do this now)

1. Centralize: paste comments into one doc with source and segment columns.
2. Run the AI grouping prompt (below) including your team context.
3. Synthesize: convert each theme into a 1-line problem + suggested metric (one metric only).
4. Generate 2–4 small experiments per problem — scope each to <= 2 sprints where possible.
5. Prioritize with your formula; adjust AI scores against your team reality.
6. Pick 1 high-priority experiment, set success criteria, run a 2-week test, record results, then re-run prioritization with outcomes.

Copy-paste AI prompt (primary)

Paste this exactly into your AI chat and replace bracketed items:

“I will paste a list of customer comments. Team context: [team size], sprint length [weeks], typical sprint capacity [e.g., 2 engineers + 1 designer can deliver X]. Please:

• Group comments into 3–6 themes and label each.
• For each theme, write one-line problem + the customer need.
• Propose 3 testable experiments (small, shippable in 1–2 sprints) with effort 1–5 (1=<1 week, 5=>8 weeks).
• For each experiment, suggest the single best metric to measure success and an expected direction (increase/decrease).
• Return a table I can paste into a spreadsheet.

”

Prioritization prompt (variant)

After you have ideas, paste this:

“Here are proposed experiments with effort estimates. Score each impact 1–5 and confidence 1–5 based on our team context. Calculate priority = (impact x confidence) / effort and rank. Provide one-sentence rationale per item.”

Metrics to track

• Primary experiment metric (e.g., signup → key action conversion rate)
• Secondary: retention at 7/30 days, NPS verbatim change, support ticket volume for the theme
• Velocity: % of planned work delivered this sprint

Mistakes & fixes

• Relying on AI effort/confidence as final — fix: adjust with team capacity and historical velocity.
• Building from single-signal comments — fix: require 2 signals or an experiment before shipping.
• Not defining a single metric — fix: one clear success metric per experiment.

1-week action plan

1. Collect 50–200 comments into a sheet and write the 2–3 sentence team context.
2. Run the primary prompt and paste AI output into a spreadsheet.
3. Select the top experiment, define the single success metric and acceptance criteria, and start a 2-week test.

Your move.

[Jeff Bullas]

Yes — adding team context turns “good ideas” into shippable plans. Let’s level this up with an insider trick: use evidence-weighted prioritization and a two-speed roadmap (Discover vs Build) so your AI output maps to capacity, metrics, and decisions you can make fast.

Context in one lineAI is your rapid analyst. You provide constraints (team capacity, baselines, effort reality). Together you’ll turn messy comments into ranked experiments, then into a 90-day roadmap that actually moves a metric.

What you’ll need (beyond what you listed)

• 3 baseline metrics (e.g., activation %, checkout conversion, weekly active users).
• Simple segment weights (e.g., Enterprise = 1.5, SMB = 1.0, Free = 0.5).
• Evidence weights to score confidence: Anecdote = 1, Survey = 2, Support volume = 3, Usage data = 4, Experiment result = 5.
• Calendar slots: 30-min weekly triage and 45-min monthly roadmap review.

Step-by-step: from insights to a decision-ready roadmap

1. Set baselines. For each key metric, write the current number and a 30–90 day target (e.g., Activation 32% → 37%). Expectation: this prevents “metric soup.”
2. Tag comments. Add columns: theme, segment, frequency (how many mentions), severity (1–5). AI can draft these; you tidy up.
3. Compute impact rough-cut. Impact score = frequency x severity x segment weight. Keep it simple; you’re aiming for a directional rank.
4. Turn themes into problems. Convert each theme into a one-line problem and the customer job (why they care). Tie each to one metric.
5. Generate small experiments. 2–4 per problem, scoped to 1–2 sprints. Capture them in an Experiment Card (hypothesis, metric, baseline, target, effort, risks, stop/go rule).
6. Prioritize with evidence. Confidence score = highest evidence weight you have for that theme. Priority = (Impact x Confidence) / Effort. Sanity-check numbers against team reality.
7. Build a two-speed roadmap. Two lanes: Discover (tests, prototypes, surveys) and Build (shipping changes). Buckets: Now (2 weeks), Next (30 days), Later (60–90 days). One metric per item.
8. Set cadence and decisions. Weekly triage: add new feedback, re-score fast. Monthly review: kill, pause, or scale. Rule of thumb: scale an experiment when the primary metric beats target for two consecutive weeks.

Copy-paste AI prompt: Evidence-weighted prioritizer

Paste this into your AI and replace bracketed items:

“You are my product analyst. Team context: [team size], sprint length [weeks], capacity [what we can ship in 2 weeks]. Baselines: [Metric A = value], [Metric B = value], [Metric C = value]. Segment weights: [e.g., Enterprise 1.5, SMB 1.0, Free 0.5]. Evidence weights: Anecdote 1, Survey 2, Support volume 3, Usage data 4, Experiment 5.

• Group the comments I’ll paste into 3–6 themes with labels.
• For each theme, draft a one-line problem, the customer job-to-be-done, and the single metric to move.
• Propose 2–4 small experiments per theme (shippable in 1–2 sprints). Estimate Effort 1–5 (1=<1 week, 5=>8 weeks).
• Use my tags [frequency], [severity 1–5], and [segment] to compute Impact = frequency x severity x segment weight.
• Assign Confidence using the strongest evidence available for that theme (use the evidence weights).
• Calculate Priority = (Impact x Confidence) / Effort. Return a ranked table with: Theme, Problem, Metric, Experiment, Effort, Impact, Confidence, Priority, Lane (Discover/Build), and a 1-sentence rationale.

Here are the comments (with any tags I have): [paste data]”

Experiment Card template (use this prompt next)

Copy-paste:

“Create an Experiment Card for [theme/problem]. Include: Hypothesis, Primary metric (baseline → target), Success threshold, Minimal viable test, Effort (1–5) with assumptions, Risks and guardrails, Data to capture, Stop/Go criteria after 2 weeks, and the smallest follow-up if it works. Keep it one screen long.”

Worked example (concise)

• Theme: Onboarding. Baseline activation: 32% (signup → first key action). Target: 37% in 60 days.
• Impact inputs: frequency 28 mentions, severity 4, segment mix mostly SMB (1.0). Impact ≈ 112.
• Confidence: usage data shows 55% drop-off before first action → evidence weight 4.
• Top experiment: Guided first-run checklist (Effort 2). Metric: activation rate. Success: +3 points in 2 weeks.
• Priority ≈ (112 x 4) / 2 = 224 → goes into Build/Now. Backup: “skip email verification until after first action” as Discover/Now (Effort 1).

Mistakes to avoid (and quick fixes)

• Theme sprawl: Too many categories. Fix: force 3–6 max; merge the rest into notes.
• Metric soup: Multiple KPIs per item. Fix: one primary metric per experiment.
• Stale backlog: Old ideas linger. Fix: kill anything that misses target twice, unless you have new evidence.
• Over-trusting AI estimates: Fix: cap any Effort >3 to a discovery test first.
• No owner: Fix: assign a single DRI per experiment before it enters “Now.”

Action plan (next 7 days)

1. Today: Pick 3 baselines and set simple segment/evidence weights. Block your weekly triage and monthly review.
2. Day 2: Centralize 50–200 comments. Tag frequency, severity, segment for as many as you can.
3. Day 3: Run the evidence-weighted prioritizer prompt. Edit the top 5 items.
4. Day 4: Generate Experiment Cards for the top 2. Define success thresholds.
5. Day 5–7: Ship one Build item and one Discover test. Track the single metric daily; capture notes.
6. End of week: Quick review: kill, iterate, or scale. Re-run prioritization with the new data.

Final nudgeSmall, measured wins stack up fast. Let AI do the heavy lifting on grouping, math, and drafting. You supply the baselines, capacity, and the call. Ship, learn, repeat.

On your side, Jeff

[aaron]

Jeff nailed the two-speed roadmap and evidence-weighting. I’ll push it one step further: tie every AI-suggested item to a dollars-and-metrics efficiency score so you pick work that moves KPIs fastest with the team you have.

Hook Turn AI output into a ranked list you can defend in a board meeting: priority by metric delta per engineering day.

Problem Good themes and experiments still stall when leaders ask, “What moves the KPI next sprint?” Without a simple efficiency score, you debate opinions, not outcomes.

Why it matters This shifts prioritization from “sounds right” to “measurably best next.” Expect crisper trade-offs, fewer half-built features, and faster time-to-signal on the KPIs you care about.

Lesson from practice Evidence-weighted ideas win more often when you include one constraint: metric lift per day of effort. AI can calculate it; you sanity-check it.

Do / Do-not

• Do pick one primary KPI per theme (activation, checkout conversion, or WAU) and state the 60–90 day target.
• Do assign segment weights and evidence weights (as Jeff outlined) before you score anything.
• Do compute an efficiency score: Metric Delta per Day (MDD) = expected KPI lift (in percentage points) ÷ engineering days.
• Do set WIP limits: Build ≤2 concurrent items, Discover ≤3.
• Do-not accept uplift claims without a baseline and a stop/go rule.
• Do-not allow any item into “Now” without an owner and a single success metric.
• Do-not ship Build items with Effort >3 until they pass a Discover test.

What you’ll need

• 50–200 tagged comments (theme, segment, frequency, severity).
• Team context (size, sprint length, typical ship capacity in 2 weeks).
• Three baselines (e.g., Activation %, Checkout conversion %, WAU).
• Rough value model: optional but powerful (e.g., estimated value per 1 percentage point lift for your primary KPI).
• Owner per experiment and the earliest observable event to track.

Step-by-step

1. Set baselines and target. Example: Activation 32% → 37% in 60 days. Define the acceptable minimum detectable change (e.g., +1.5 pts in 2 weeks).
2. Prepare the dataset. Columns: Theme, Segment, Frequency, Severity (1–5), Evidence type, Problem statement, Metric, Proposed experiment, Effort (days), Owner.
3. Run AI scoring. Have AI compute Impact = frequency x severity x segment weight, Confidence from your evidence weight, then MDD = expected lift (pts) ÷ effort (days). Priority = (Impact x Confidence) x MDD. You edit the top 5.
4. Gate by lane and WIP. Highest Priority into Build (≤2). Next into Discover (≤3). Everything else waits.
5. Instrument. Add the single event/metric needed to validate the lift within 14 days. Define success threshold and stop/go criteria upfront.
6. Cadence. Weekly triage to re-score with new data; monthly review to kill, scale, or defer.

Copy-paste AI prompt (returns CSV you can drop into a spreadsheet)

Paste into your AI and replace brackets:

“You are my product analyst. Team: [size], sprint length [weeks], capacity in 2 weeks: [what we can ship]. Baselines: [Metric A=value], [Metric B=value], [Metric C=value]. Targets: [Metric A target in 60–90 days]. Segment weights: [e.g., Enterprise 1.5, SMB 1.0, Free 0.5]. Evidence weights: Anecdote 1, Survey 2, Support 3, Usage 4, Experiment 5. Value model (optional): [Estimated value per 1 percentage point lift for Metric A = $X].

• Input will be comments tagged with: Theme, Segment, Frequency, Severity (1–5), Evidence type.
• For each theme, produce 2–4 experiments with: one-line Problem, Metric to move (one only), Expected lift (percentage points over 2 weeks), Effort (engineering days), Confidence (use evidence weights), Impact (Frequency x Severity x Segment weight), MDD = Expected lift / Effort, Priority = (Impact x Confidence) x MDD, Lane (Discover if Effort >3 or Confidence <=2, else Build), Owner [blank].
• Output as CSV with columns: Theme, Problem, Metric, Experiment, ExpectedLiftPts, EffortDays, Impact, Confidence, MDD, Priority, Lane, SuccessThreshold, StopGoRule.

Here are the tagged comments: [paste rows]”

Metrics to track

• Primary KPI lift for each Build item (percentage points).
• Experiment hit rate: % of experiments that meet success threshold.
• Throughput: experiments completed per sprint.
• Efficiency: median MDD for shipped items.
• Cycle time: start-to-decision days per experiment.

Mistakes & fixes

• Inflated expected lift. Fix: cap to historical medians unless Discover tests prove higher.
• Chasing low-impact segments. Fix: enforce segment weights; re-check customer value.
• Vanity metrics creep. Fix: one primary KPI per item; kill items that can’t move it.
• No clean baseline. Fix: freeze a baseline window before testing; compare like-for-like cohorts.
• Orphaned experiments. Fix: assign a DRI before anything enters “Now.”

Worked example

• Baseline: Activation 32%. Target: 37% in 60 days. Team: 2 engineers, 1 designer; capacity ~10 engineer-days per 2 weeks.
• Theme: Onboarding (SMB). Frequency 28, Severity 4, Segment weight 1.0 → Impact 112. Evidence: usage data → Confidence 4.
• Experiment A (Build): First-run checklist. Expected lift 2.0 pts, Effort 4 days → MDD 0.5. Priority = 112 x 4 x 0.5 = 224.
• Experiment B (Discover): Skip email verification until after first action. Expected lift 1.5 pts, Effort 1 day, Confidence 2 → MDD 1.5. Lane Discover (Confidence ≤2). Priority = 112 x 2 x 1.5 = 336 (test quickly, then promote if it hits).
• Decision: Run B in Discover immediately (cheap signal); run A in Build concurrently. Success thresholds: A = +1.5 pts in 2 weeks; B = +1.0 pt in 1 week with neutral security complaints.

1-week action plan

1. Today: Lock baselines and targets; set segment and evidence weights; define WIP limits.
2. Day 2: Centralize comments with tags; fill missing tags via AI and quick review.
3. Day 3: Run the CSV prompt; sort by Priority and MDD; select ≤2 Build and ≤3 Discover items.
4. Day 4: Create one-page Experiment Cards with success thresholds, owners, and instrumentation.
5. Day 5–7: Ship one Build and one Discover test; track the primary KPI daily; document results.
6. End of week: Stop/Go decisions; promote any Discover winner to Build; re-run scoring with new evidence.

Your move.

[Author]

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