7.1 Metrics Dashboard — Daily · Weekly · Monthly
7.2 Feedback Loops — Signal to Decision
7.3 Iteration Framework — Data to Action
What the Dashboard Looks Like — Live View
RouCN Metrics Dashboard · Sample State · Month 8 (Post-Launch)
Tracking Cadence — What Gets Checked When
🔴
Daily
Takes 10 minutes. Every morning.
Lagging
Shopify dashboard. Count + revenue. Top SKU. Anomaly check: zero orders = site issue?
Lagging
Shopify reviews + Myntra seller panel. Any <3★ review = read immediately + respond within 2 hours.
Leading
Instagram DMs, comments on Reels, support email. Sentiment check. Any sizing complaints, quality issues, shipping queries.
Fulfillment status
0 delays
Lagging
Shiprocket/Delhivery panel. Any stuck shipments (NDR). Customer notified proactively before they ask.
Leading
If any size drops to ≤5 pairs: activate notify-me. If total <20: trigger Batch 2 planning conversation.
🟠
Weekly
Friday. 30-minute review.
Lagging
Units sold this week by SKU and colorway. Trending up/flat/down? Which colorway is pulling ahead?
Return rate (rolling)
<12%
Lagging
Returns this week + reason codes. Size? Quality? Change of mind? Pattern detection: same size returning = size guide problem.
Instagram metrics
+100 flw/wk
Leading
Follower delta, Reel views top 3 posts, Story reach, saves-to-followers ratio. High saves = content worth reposting.
Leading
Shopify analytics: sessions vs orders. If CVR drops without traffic drop: something changed on the site or in reviews. Investigate.
Leading
ReferralCandy dashboard. How many referred orders this week? Which referrers are most active? Reward top referrers with early access.
🟢
Monthly
1st of each month. Full review.
NSM — Leading
Cohort analysis: of all customers who bought 90+ days ago, what % has bought again? This is the single most important number each month.
Lagging
Recalculate per pair after actual COGS, logistics, GST, PG fees this month. Is margin improving with batch scale? Alert if <45% guardrail.
Leading
Total marketing spend ÷ new customers acquired. Split by channel: D2C organic, paid, Myntra, referral. Is paid CAC creeping up?
Leading
Rolling LTV (GM × avg purchases) ÷ blended CAC. If ratio is declining, community + retention is not offsetting rising acquisition costs.
Leading
Post-purchase Day 7 survey via Klaviyo. % promoters (9–10) minus % detractors (0–6). Below 35 = experience problem. Read all detractor verbatims personally.
Leading vs Lagging — The Critical Distinction
📈 Leading Indicators — Act on These
Instagram saves/Reel — predicts purchase intent 3–5 days ahead of orders. If saves spike, orders follow.
D2C site CVR — predicts revenue this week. Drop in CVR = something broke in the funnel.
Email open rate — predicts community health and repeat purchase rate. Declining opens = disengagement creeping in.
Waitlist signups/week — predicts launch day demand. 200+ signups before drop = confident launch.
Referral link shares — predicts organic CAC reduction. More shares = community is evangelical.
📊 Lagging Indicators — Report on These
Revenue (MRR) — tells you what happened, not what will happen. Healthy revenue can hide broken unit economics.
Product rating — reflects product quality decisions made 3 months ago. Can't fix fast once it drops.
Return rate — reveals sizing or quality problems already shipped. Too late to fix Batch 1, critical for Batch 2.
Units sold (cumulative) — the scoreboard, not the game. Tracks progress against OKRs.
NPS score — reflects experience 7–30 days ago. Leading for churn prediction, lagging for experience diagnosis.
Metric Ownership — Who Owns What (Year 1: All Founder)
| Metric | Cadence | Owner | Tool | Alert Threshold |
|---|
| RPR @ 90 Days (NSM) |
Monthly |
Founder |
Shopify cohort report |
<8% = STOP gate triggered |
| Orders (daily/weekly/monthly) | Daily + Weekly + Monthly | Founder | Shopify dashboard | <5 orders/week for 2 weeks = pivot review |
| Product Rating | Daily check + weekly trend | Founder | Shopify reviews + Myntra panel | Any single review <3★ = immediate personal response |
| Return Rate | Weekly | Founder | Shopify returns + Shiprocket | >12% = guardrail breach → root cause |
| Gross Margin % | Monthly | Founder | Manual spreadsheet + CA | <45% = guardrail breach → stop discounting |
| Blended CAC | Monthly | Founder | Marketing spend ÷ Shopify orders | >₹1,200 = paid ads paused, organic doubled down |
| D2C Site CVR | Weekly | Founder | Shopify Analytics | <1.5% for 2 weeks = landing page A/B test |
| Email Open Rate | Per campaign | Founder | Klaviyo | <20% = subject line and send-time review |
| Instagram Followers + Reel Views | Weekly | Founder | Instagram Insights | 2 weeks no Reel >5K views = content strategy review |
| NPS Score | Monthly | Founder | Klaviyo survey flow | <25 = experience full audit |
🔬 PM Hypothesis H18 — The Dashboard Must Be Boring to Be Useful
A metrics dashboard that requires 2 hours to review weekly will not be reviewed. RouCN's dashboard must be buildable in under 30 minutes every Friday — 3 tabs: Shopify, Instagram Insights, Klaviyo. No complex BI tool needed in Year 1. The value is in the consistency of review, not the sophistication of the tool. If a metric can't be checked in 60 seconds, it won't be checked in a crisis.
Evidence: 80% of early-stage D2C brand failures involve delayed signal detection — not wrong strategy. (a16z consumer brand playbook, 2024). The founder who checks Shopify every morning catches a fulfilment failure before the customer tweets about it.
The RouCN Feedback Engine — Continuous Loop
👟
Customer Buys
D2C or Myntra
→
📦
Experience
Unbox · Wear · Review
→
📡
Signal Emitted
Review · DM · Return · NPS
→
🔍
Founder Reads
Every signal, every time
→
⚙️
Decision Made
Product · GTM · Ops
→
🚀
Next Drop
Better than the last
Signal Types — Source, Flow, Action
↩️Returns
Source: Shopify returns portal + Shiprocket reverse logistics
Every return has a reason code: Size too small / Size too large / Quality issue / Changed mind / Wrong item. Reason codes are not bureaucracy — they're the most honest product feedback RouCN will receive. A customer who returns and explains why is giving the brand a free QC report.
→ If size code >40% of returns: size guide rewritten + fit video added to PDP. If quality code >3 returns from same batch: escalate to factory with photo evidence within 72 hours.
⭐Reviews
Source: Shopify Reviews, Myntra seller panel, Google Business
Every review read by founder personally in Year 1. Not delegated. Not read as an aggregate — each one read as a customer conversation. 5★ reviews reveal what to double down on (what they mention = what matters). 1–2★ reviews reveal what's broken (before it compounds). Pattern across 10+ reviews = product signal, not noise.
→ Review themes feed directly into Batch 2 spec: if 5 reviews mention "wide toe box is great" — that's a design strength to protect. If 3 reviews mention "heel slips" — tech pack correction for Batch 2.
📊NPS Survey
Source: Klaviyo Day 7 post-purchase automated survey
Single question: "On a scale of 0–10, how likely are you to recommend RouCN to a friend?" With one follow-up: "What's the main reason for your score?" Promoters (9–10): ask them to leave a review and join the referral program. Passives (7–8): ask what would make it a 10. Detractors (0–6): founder calls them directly within 48 hours.
→ Detractor verbatim responses are the single richest source of unfiltered product truth. Every NPS below 6 triggers a founder call — not a templated email. These conversations build the product.
💬Social Comments & DMs
Source: Instagram comments, Reels, Stories replies, DMs
The most unfiltered signal source. Customers say on Instagram what they won't write in a review. Size frustrations, color requests, "when is Drop 002" pressure, style suggestions. Comments on influencer Reels are especially valuable — these are prospects, not customers. What's stopping them from buying? Their objection is in the comment.
→ Weekly DM/comment synthesis: top 3 product questions, top 3 praise themes, top 3 objections from non-buyers. This directly informs FAQ updates, product page copy, and next drop content.
📏Size Complaints
Source: Returns data + DMs + reviews + WhatsApp community
Size issues are the #1 return driver for online footwear. For RouCN, Indian feet are wider than international standard — the last (mould) must be right from Batch 1. Size complaints split into two types: 1) True sizing (shoe runs small/large vs standard) — fix the last or update size guide. 2) Width issues (shoe too narrow) — widen the toe box in next spec.
→ If >5 complaints of "shoe runs small" in first 30 days: add half-size-up recommendation to size guide within 48 hours. If width complaints emerge: tech pack revision for Batch 2. Ship a corrected pair to every complainant who didn't return.
🗣️Customer Interviews
Source: Proactive founder outreach — 5 calls/month
The highest-signal feedback channel. Not a survey — a 20-minute conversation. Questions: What made you finally buy? What almost stopped you? What do you tell friends about the brand? What would you change about the shoe? What other brands do you wear? This is qualitative PM research happening in real-time with real customers, not a focus group.
→ 5 interviews/month = 60 interviews in Year 1. Every insight logged. Themes reviewed quarterly. These conversations will contain the idea for Silhouette 002, the next colorway, and the GTM angle for Drop 002 — if the founder listens.
Feedback Processing Cadence — Signal to Log to Action
| Signal Type | Collection Frequency | Processing | Time to Action | Action Owner |
|---|
| 1–2★ Review | Real-time (alert) | Founder reads + responds publicly within 2 hours | <2 hours response; root cause within 7 days if pattern | Founder |
| Return + reason code | Daily check | Reason code logged in spreadsheet; weekly pattern review | Size guide update: 48 hours. Quality escalation: 72 hours. | Founder |
| NPS detractor (<6) | Day 7 post-purchase | Founder calls within 48 hours | 48 hours to contact; resolution documented | Founder |
| Instagram DMs | Daily | All read; sentiment tagged (positive/question/complaint) | Response same day. Product insight logged weekly. | Founder |
| Size complaints (pattern) | Weekly review | Aggregate: how many, which size, which direction | Size guide update: 48 hours. Last spec change: Batch 2 tech pack. | Founder |
| WhatsApp community polls | Per poll (bi-weekly) | Results reviewed same day | Colorway or feature decision within 7 days of poll close | Founder |
| Customer interviews (qualitative) | 5/month | Notes logged; themes reviewed monthly | Quarterly product brief update based on themes | Founder |
🔁
The core principle: Data without a decision is just noise. Every metric in Block 7.1 and every signal in Block 7.2 must have a defined path to a concrete action. The iteration framework is the bridge between "we observed X" and "therefore we will do Y by date Z." Without this bridge, the dashboard becomes a vanity exercise.
The 5-Step Iteration Cycle — Runs Every 30 Days Post-Launch
1
Observe — What Did the Data Say This Month?
Pull the full dashboard: NSM (RPR), orders, rating, return rate, CAC, NPS, channel split. Review all customer interview notes. Read every review verbatim from the month. Synthesise: what 3 things worked, what 3 things didn't, what 1 thing was surprising.
Example: "RPR at 11% (below 15% target). Rating 4.4★. Returns: 8% but 60% are size-related. Instagram saves up 40% — demand is there. Conversion is the bottleneck."
2
Hypothesise — Why Did This Happen?
For each observation, write a hypothesis about the root cause. Not a guess — a testable belief. Frame it as: "We believe [X is happening] because [Y is the cause]. We will know this is true if [Z changes when we fix Y]." This forces precision and prevents random thrashing.
Example: "We believe RPR is below target because customers who received size-mismatched pairs didn't come back. We'll know this is right if fixing the size guide reduces size-related returns and RPR climbs over the next 60 days."
3
Decide — What Changes Next?
Based on hypotheses, make one or two product or GTM decisions. Not ten. One product iteration at a time — otherwise you can't know what worked. Decisions fall into four buckets: (a) Product spec change, (b) GTM/channel change, (c) Experience change, (d) Story/positioning change.
Example: Decision A — Update size guide with "we recommend sizing up if your foot length is above 265mm" and add fit video to PDP. Decision B — Add size-exchange pre-paid option in packaging insert.
4
Act — Ship the Change
Execute the decisions within 14 days of the monthly review. Faster than 14 days for anything customer-facing (size guide, PDP copy, shipping SLA update). Batch 2 spec changes require 30 days before factory order placed. GTM changes (new channel, new influencer tier) require 14–30 days to show results. Set a clear owner and deadline for every action.
Example: Size guide updated by Day 3. Fit video filmed and uploaded by Day 10. Batch 2 tech pack revised with wider last by Day 14, sent to factory by Day 20.
5
Measure — Did It Work?
30 days after the change: did the hypothesis test out? Did return rate drop? Did RPR improve? Did CVR increase after the PDP update? If yes: double down — do more of this. If no: hypothesis was wrong — go back to Step 2 with new data. Every iteration generates a learning, regardless of outcome.
Example: 30 days after size guide update: size-related returns dropped from 60% of returns to 35%. RPR trending upward. Hypothesis confirmed. Add foot-width measurement to size guide as next iteration.
Data → Next Drop Decisions — What Batch 1 Data Tells Batch 2
📦 Batch 2 Size Split
Data input: Batch 1 sell-through by size
Decision rule: Allocate Batch 2 proportionally to Batch 1 sell-through rate per size, not to original split
Example: UK 8 sold out in Week 2 (100% sell-through) → UK 8 allocation jumps to 30% of Batch 2. UK 6 still has 4 pairs remaining → UK 6 allocation drops to 5%.
🎨 Batch 2 Colorway Split
Data input: Sell-through rate + DM/comment demand by colorway
Decision rule: If SF sold out in 48 hrs → SF gets 25% of Batch 2 (up from 15%). If BB is slowest seller → BB drops to 15%, Noir Chrome holds at 60%.
Wildcard rule: If WhatsApp community poll shows 60%+ demand for a new colorway → introduce it as 10% of Batch 3, replacing slowest existing colorway.
📡 Channel Weight
Data input: D2C vs Myntra orders split + CAC per channel
Decision rule: Channel with higher contribution margin AND lower CAC gets more stock priority in next batch
Example: If Myntra drives 40% of orders but CAC is ₹1,100 vs D2C at ₹600 → Myntra stock capped at 25% of Batch 2. Push traffic to D2C via paid ads instead.
👟 Silhouette 002 Input
Data input: Customer interview themes, Instagram comment requests, NPS promoter verbatims
Decision rule: Silhouette 002 brief is written when 3+ data sources converge on the same gap
Example: 8 interviews mention "I'd love a mid-top for colder months" + 3 Instagram DMs requesting it + NPS promoter says "wish you had more options" → Mid-Top canvas confirmed as Silhouette 002.
Iteration Sprint Calendar — What Changes When (Year 1 Post-Launch)
| Sprint | Time Window | Primary Focus | Key Decisions | Data Required |
|---|
| Sprint 0 |
Launch week |
Observe everything, change nothing |
No product changes in launch week — too noisy. Just collect data. |
Every signal collected, none actioned yet |
| Sprint 1 |
Day 8–30 |
Experience fixes (fast-turnaround) |
Size guide update, PDP copy tweaks, FAQ additions, email subject line tests |
Return reason codes, DMs, first reviews |
| Sprint 2 |
Day 31–60 |
Channel optimisation |
Myntra launch, stock split decision, paid ads test (₹20K), referral program activation |
D2C vs Myntra CAC, CVR by channel, sell-through rate |
| Sprint 3 |
Day 61–90 |
Batch 2 spec + retention |
Batch 2 colorway split, size allocation, any tech pack corrections, WhatsApp inner circle launch |
Full Batch 1 sell-through, NPS, customer interviews |
| Sprint 4 |
Day 91–120 |
NSM validation + community |
RPR measurement, referral program performance, IGCF expansion, email list monetisation |
90-day cohort RPR, LTV:CAC ratio, email revenue |
| Sprint 5 |
Day 121–180 |
Batch 2 launch + Year 2 brief |
Batch 2 drop mechanics, Silhouette 002 brief, Amazon India initiation, pop-up feasibility |
Batch 2 sell-through, community size, revenue vs scenario |
| Sprint 6 |
Day 181–365 |
Year 2 foundation |
SKU 002 tech pack, Year 2 OKRs, first investor outreach if targets met |
Full Year 1 P&L, NPS trend, RPR at Year 1 close |
🔬 PM Hypothesis H19 — The Founder Who Measures Daily Outlasts the Founder Who Plans Monthly
At RouCN's stage, the competitive advantage is speed of learning, not depth of strategy. A founder who reads every review, responds to every DM, and runs a monthly iteration cycle will build a brand that adapts faster than any incumbent can copy. The PM path (all 7 blocks, 30 sections) is not a plan to execute once — it is a living system that gets smarter every time the feedback loop completes.
Evidence: Paul Graham's "do things that don't scale" — reading every customer review personally is not scalable, but it's exactly what builds the unscalable early foundation that makes everything later scale. The D2C brands that survived post-2020 were not the ones with the best strategy decks — they were the ones that iterated fastest on real customer signals.
Sources: a16z Consumer Brand Playbook 2024, Simon-Kucher D2C Retention Study 2024, Inc42 D2C Brand Survival Report 2025, Klaviyo Email Benchmark India 2025, YC Startup School Product Analytics Guide 2024
🦝
RouCN — 0 to Launch.
Complete PM Path.
7 blocks. 30 sections. Every hypothesis data-backed. Every decision pre-defined. Every metric owned. This is not a strategy deck — it is an operating system for building India's raccoon sneaker brand from zero to a real, defensible, growing business. The raccoon doesn't ask permission. It just shows up.
Block 1 — Discovery ✓
Block 2 — Strategy ✓
Block 3 — Product ✓
Block 4 — Operations ✓
Block 5 — GTM ✓
Block 6 — Roadmap ✓
Block 7 — Measurement ✓
04
Operations & Compliance
07
Measurement & Iteration