D2C E-Commerce
Stotodo —
8× Revenue
in Under a Year
Scaled a D2C brand from ₹6L to ₹50L/month by building the entire growth engine from scratch — acquisition, creative, funnel, and retention — as the company's first growth hire.
Last updated: March 2026
8×
Revenue Growth
Shopify · Mar–Dec 2024
Numerator: ₹50L/month (peak, Dec 2024)
Denominator: ₹6L/month (baseline, Mar 2024)
Source: Shopify revenue dashboard
Note: Total revenue, all channels combined
30–40%
Landing Page CVR
GA4 · Jun–Dec 2024
Numerator: Sessions that completed purchase
Denominator: Total landing page sessions
Source: Google Analytics 4
Note: Post-rebuild average. Pre-rebuild: ~8%
2–3×
Remarketing Lift
Meta Ads · Apr–Dec 2024
Definition: CVR of remarketing vs. cold traffic
Source: Meta Ads Manager
Attribution: 7-day click / 1-day view
Note: Measured across retargeting ad sets
25–30%
Repeat Purchase Share
Shopify · Aug–Dec 2024
Definition: Revenue from returning customers ÷ total revenue
Source: Shopify customer analytics
Note: Baseline at launch was ~0% (no retention system)
Operating as
Solo IC Growth Lead
Company stage
Early-stage · 0→1
Business model
D2C · Direct-to-Consumer
Channels owned
Meta · Google · WhatsApp · Email · Influencer
Available from
Immediately · Open to remote
Context & Brief
The Situation I Walked Into
Understanding the business before touching a single campaign.
The Business
Stotodo was an early-stage D2C brand in the consumer products space, founded in early 2024 in Mumbai. The founders had a validated product with positive early signals but zero structured marketing — no funnels, no paid channels, no retention, no creative system. Revenue was generated almost entirely through word of mouth and organic reach.
I joined as the first-ever marketing hire — initially brought on as an intern for Shopify and IT operations. Within 6 weeks, the scope expanded to full growth ownership as the founders realized the scale of what needed to be built.
The Brief I Was Given
"We've got a product that works. We just need to find customers at scale — and make sure they come back."
— Founder, Stotodo · March 2024
In practice, this meant building the entire growth infrastructure from the ground up: identify which channels work, create a repeatable creative system, build a funnel that converts, and design retention flows that generate revenue without always spending on new acquisition. All of this with a constrained budget and a small founding team.
Diagnostic
⬛ Before State
What I Found on Day One
A full audit before any execution. Diagnose first, prescribe second.
Finding 01 — Acquisition
No structured paid channels. Zero Meta Ads or Google Ads presence. Revenue of ₹6L/month was driven entirely by organic social reach and personal referrals — not scalable, not predictable.
Baseline revenue: ₹6L/mo · Source: Shopify · Mar 2024
Finding 02 — Creative
Ad creatives were random, unstructured, and untested. No UGC pipeline. No problem-solution framework. No naming convention. No way to know what was working or why.
Avg creative lifespan: Unknown · No A/B testing in place
Finding 03 — Funnel
No dedicated landing pages. Traffic (such as it was) landed on the homepage with no conversion intent. Cart abandonment was untracked and unrecovered. Checkout completion rate was sub-8%.
LP conversion rate: ~8% · Source: Shopify analytics · Mar 2024
Finding 04 — Retention
Zero post-purchase communication. No email flows, no WhatsApp sequences, no win-back campaigns. Every customer was treated as a one-time buyer by default. Repeat purchase contribution: ~0%.
Repeat purchase rate: ~0% · No CRM or automation in place
Pre-Intervention Funnel — Diagnosed Leak Points
Awareness
Organic only
Site Visit
No paid traffic
Product Page
⚠ High drop-off
Add to Cart
⚠ No recovery
Purchase
⚠ ~8% CVR
Return
⚠ No retention
Growth Operating System
How I Think & Execute
The repeatable 5-step framework I applied at Stotodo — and apply across every engagement.
Baseline & Constraint Mapping
Before touching anything, I audited what existed: Shopify revenue data, traffic sources, existing creatives, team bandwidth, tech stack, and budget constraints. At Stotodo, the audit revealed we were starting from near-zero across all channels, with a modest monthly budget and a two-person founding team. I mapped the constraints first so execution decisions were grounded in reality, not aspiration.
📄 Output: Funnel audit doc + Baseline KPI snapshot (Mar 2024)
Funnel Diagnosis & Leak Prioritization
With the baseline mapped, I identified where the biggest revenue leaks were. At Stotodo, the largest leverage points were: (1) zero paid acquisition — any structured spend would compound, (2) ~8% landing page CVR — improving this would multiply the value of every rupee spent on acquisition, and (3) zero retention — every customer was being lost after one purchase. I ranked these by potential revenue impact and sequenced work accordingly.
📄 Output: Annotated funnel map with revenue impact estimates per stage
Experiment Backlog & ICE Prioritization
I built an experiment backlog using ICE scoring (Impact × Confidence × Ease) to prioritize what to test first. At Stotodo, this meant: launch Google Search first (high intent, high confidence, fast feedback), then Meta with UGC creatives (high impact potential, medium ease), then build landing pages (high confidence, high impact on CVR), then activate retention flows last (after acquisition proved out). This sequencing prevented premature optimization.
📄 Output: Experiment tracker (ICE-scored backlog, 20+ tests logged)
Weekly Execution Cadence
I ran a consistent weekly sprint rhythm: Monday — review last week's numbers and pull key metrics. Tuesday/Wednesday — execution (new creatives live, ad adjustments, automation updates). Thursday — mid-week performance check, kill underperformers, scale winners. Friday — growth update to founders (one-page: wins, learnings, next week's focus). This cadence meant decisions were made on data, not instinct, and founders had visibility without needing to ask.
📄 Output: Weekly growth update template (shared every Friday)
Attribution & Decision Rules
I used a blended attribution model: last-click for paid performance decisions (campaign budget allocation), assisted attribution for understanding the full customer journey, and Shopify's first-party data as the revenue source of truth. Decision rules were explicit: any ad set below target ROAS for 7 consecutive days gets paused; any creative with CTR below 1.5% gets killed after 500 impressions; any WhatsApp flow with open rate below 40% gets rewritten. Removing subjectivity from kill decisions was critical at this scale.
📄 Output: KPI dashboard (Looker Studio) + Attribution model documentation
Execution Timeline
How the Growth Happened
Month by month — not just that it happened, but how and in what sequence.
Month 1 — March 2024
Audit, Baseline & First Channel Live
Full Shopify and analytics audit. Identified four primary leak points (see Before section). Launched Google Search Ads with a tight keyword set targeting high-intent product searches. Built the first structured landing page aligned to ad messaging. First paid leads came in within week 2.
Revenue at entry: ₹6L/mo
Month 2 — April 2024
Meta Ads Live + UGC Creative System Built
Launched Meta Ads with problem-solution UGC creatives sourced from early customers and micro-influencers. Introduced a creative naming convention and testing framework: 3 concepts × 3 hooks × 2 formats = 18 variants per batch. Identified first winning creative within 10 days. Landing page CVR climbed from ~8% to ~18% through copy and layout adjustments.
Combined revenue: ~₹12L/mo · LP CVR: 18%
Month 3–4 — May–June 2024
Influencer Pipeline + Remarketing Architecture
Built a micro-influencer outreach and content pipeline — 8–12 creators per month, standardized brief, UGC rights secured. Remarketing campaigns live across Meta and Google, targeting site visitors and cart abandoners. WhatsApp cart recovery sequence launched (3-message flow, 24h / 48h / 72h). Repeat purchase rate climbed from 0% to ~8%.
Revenue: ~₹22L/mo · Repeat purchase: 8%
Month 5–6 — July–August 2024
Retention Engine + Email Lifecycle Launched
Full WhatsApp + email lifecycle system deployed: welcome flow, post-purchase upsell sequence, win-back campaign at 30 days, VIP segment for repeat buyers (3+ orders). Behavior-based segmentation live in Klaviyo. Landing page CVR peaked at 38%. ROAS across paid channels stabilized at 4–5×.
Revenue: ~₹32L/mo · Repeat purchase: 18% · ROAS: 4–5×
Month 7–10 — September–December 2024
Scale Phase + System Runs Independently
Scaled winning campaigns, expanded creative output, and introduced a second product line into the funnel. The growth system ran on the weekly cadence without constant intervention. Repeat purchase contribution reached 25–30% of total revenue. Revenue peaked at ₹50L/month by December 2024 — 8× the March baseline.
Peak revenue: ₹50L/mo · Repeat purchase: 25–30% · System self-sustaining
Results
✅ After State
The Numbers, With Full Context
Hover each metric to see the full methodology and data source.
₹50L
Peak Monthly Revenue
Source: Shopify dashboard · December 2024
Baseline: ₹6L/month (March 2024)
Note: Total revenue across all channels
30–40%
LP Conversion Rate
Source: GA4 · Jun–Dec 2024
Baseline: ~8% (March 2024)
Attribution: Session-based, same-session purchases only
4–5×
Blended ROAS
Source: Shopify + Meta + Google Ads Manager
Model: Blended — total revenue ÷ total ad spend
Period: Stable from Aug 2024 onwards
25–30%
Repeat Revenue Share
Source: Shopify customer analytics
Definition: Revenue from 2nd+ purchase customers
Baseline: ~0% (no retention system in March 2024)
Revenue Growth — ₹L/month
Shopify data
Key Metrics — Before vs After
GA4 + Shopify
LP CVR (Before)
8%
LP CVR (After)
38%
Repeat Rev (Before)
~0%
Repeat Rev (After)
28%
Revenue (Before)
₹6L
Revenue (After)
₹50L
📐 Measurement Notes
All revenue figures sourced from Shopify dashboard. "Landing page CVR" defined as completed purchases ÷ total landing page sessions, measured via GA4 with Shopify purchase event as conversion goal. ROAS calculated as Shopify attributed revenue ÷ total ad spend across Meta + Google, using platform-reported last-click attribution with 7-day click / 1-day view window. Repeat purchase share defined as revenue from customers with 2+ orders ÷ total monthly revenue, sourced from Shopify customer analytics. Figures represent monthly averages across the Aug–Dec 2024 period, not peak single-month values. Seasonality note: November–December 2024 included a festive sale period which contributed to the peak revenue figure.
Proof & Evidence
The Receipts
Anonymized screenshots and artifacts from the actual engagement. Add your real screenshots here — each claim should be one click from proof.
Meta Ads Manager
Campaign Performance
Apr–Dec 2024
Campaign Performance
Apr–Dec 2024
Google Ads Manager
Search Campaign Results
Mar–Dec 2024
Search Campaign Results
Mar–Dec 2024
Creative Testing Log
Winning Variants
Apr–Oct 2024
Winning Variants
Apr–Oct 2024
Remarketing Campaign
ROAS vs Cold Traffic
Jun–Dec 2024
ROAS vs Cold Traffic
Jun–Dec 2024
📎 Replace placeholder cards above with actual screenshots from Meta Ads Manager and Google Ads. Export as PNG or PDF, anonymize spend values if needed, host on Google Drive or Cloudinary, and link each card to the artifact. Tip: even showing trend lines with values redacted builds significant credibility.
Shopify Revenue Dashboard
Mar–Dec 2024
Monthly trend
Mar–Dec 2024
Monthly trend
GA4 Funnel Report
LP Conversion Rate
Mar vs Dec 2024
LP Conversion Rate
Mar vs Dec 2024
Shopify Customer Analytics
Repeat Purchase Rate
Monthly trend
Repeat Purchase Rate
Monthly trend
Looker Studio Dashboard
Weekly KPI report
Sep–Dec 2024
Weekly KPI report
Sep–Dec 2024
WhatsApp Flow Builder
Cart Recovery Sequence
3-message automation
Cart Recovery Sequence
3-message automation
Klaviyo Email Flows
Post-purchase + Winback
Sequence architecture
Post-purchase + Winback
Sequence architecture
Experiment Tracker
ICE-scored backlog
20+ tests logged
ICE-scored backlog
20+ tests logged
Weekly Growth Update
Founder report template
Friday cadence
Founder report template
Friday cadence
UGC Ad Creative
Top performing hook
Problem-solution format
Top performing hook
Problem-solution format
Landing Page — Before
Homepage as landing
~8% CVR
Homepage as landing
~8% CVR
Landing Page — After
Intent-aligned LP
38% CVR
Intent-aligned LP
38% CVR
Influencer Content
Micro-creator output
UGC rights secured
Micro-creator output
UGC rights secured
Intellectual Honesty
The One That Didn't Work
Every growth practitioner has failures. Here's one of mine — and what I changed because of it.
⚠ Failed Experiment
Broad Audience Meta Ads — Scaled Before Proving the Creative
What I tried
In Month 2, I scaled Meta ad spend to a broad audience (no interest targeting, lookalike-only) with a generic lifestyle creative — reasoning that broad + good creative would outperform narrow interest targeting based on industry advice at the time.
What happened
CPL spiked 4× within 5 days. ROAS dropped below break-even. The creative wasn't proven at a small scale first — I scaled spend before the creative earned the right to scale. Burned approximately ₹40,000 in 8 days with no meaningful revenue return.
What I changed
Introduced a mandatory "prove before you scale" rule: no creative scales past ₹500/day budget until it achieves target ROAS at ₹200/day for minimum 5 days. This single rule saved significant budget waste for the rest of the engagement and became a standing principle in my experiment framework.
Transferable principle: The biggest mistake in paid media isn't running bad ads — it's scaling bad ads. Creative validation at a small budget before scaling is a non-negotiable step, regardless of how promising the hypothesis looks.
Strategic Learnings
What I Now Know to Be True
Three transferable principles from this engagement — not just what happened here, but what I believe about growth because of it.
Conversion beats acquisition — always
The single highest-leverage move at Stotodo wasn't increasing ad spend — it was improving landing page CVR from 8% to 38%. This effectively quadrupled the number of customers we got from the same traffic volume. Before scaling any paid channel, the funnel it feeds has to earn the right to receive more traffic. A great acquisition engine feeding a broken funnel is just an expensive way to lose money faster.
Transferable principle: Fix the funnel before scaling the budget. I now audit CVR at every funnel stage before recommending any spend increase at any company I work with.
Retention is the real growth multiplier in D2C
Growing repeat purchase contribution from 0% to 25–30% of revenue was the single most capital-efficient outcome of this engagement. Acquiring a new customer in D2C costs 5–7× more than selling to an existing one. Every rupee I put into WhatsApp flows and email sequences generated ₹8–12 in return because there was no media cost. The retention engine didn't just improve profitability — it made the acquisition economics of the whole business better by reducing the revenue pressure on paid channels.
Transferable principle: In D2C, building a retention system in Month 1 is as important as building acquisition. I now treat them as parallel workstreams, not sequential ones.
Creative is the real targeting in Meta — structure it like a science
At Stotodo, the creative testing flywheel was what separated the good months from the great ones. The insight was that on Meta's broad/advantage+ setup, the creative IS the targeting — it self-selects the audience. A problem-solution UGC hook outperformed every interest-targeted lifestyle creative by 3–4× because it pre-qualified the viewer in the first 3 seconds. Structuring creative as a system (concept × hook × format matrix, naming conventions, ICE scoring for scaling) turned a random process into a repeatable one.
Transferable principle: Creative strategy deserves the same rigour as media strategy. I now build a creative testing framework as a deliverable at every D2C engagement.
Forward-Looking
What I'd Do in Your First 90 Days
If you hired me into a similar D2C or growth role today, here's exactly how I'd operate from day one.
Day 1–30
Diagnose & Baseline
Full audit of existing channels, funnels, and creative assets
Pull baseline KPIs from GA4, Shopify/CRM, ad platforms — establish the numbers that matter
Map the funnel with leak estimates at each stage
Identify the single highest-leverage intervention (usually CVR or retention, rarely more spend)
Stakeholder alignment: agree on what "winning" looks like and how we'll measure it
First weekly growth update delivered by end of Week 1
Day 31–60
Execute & Validate
First experiments live — landing page tests, creative variants, or retention flows depending on the priority
ICE-scored experiment backlog in place and running
Quick wins identified and reported to build internal trust
Attribution model documented and agreed upon
Creative testing system producing new variants weekly
Weekly cadence fully established — decisions on data, not gut
Day 61–90
Scale & Systematise
Winning channels and creatives being scaled systematically
Retention system live — at least one automated flow generating repeat revenue
KPI dashboard shared with the team — growth is visible to everyone
GTM playbook documented for the channels and tactics that work
Month 3 review: clear picture of CAC, LTV, and payback period
Growth roadmap for the next quarter drafted and presented
What the Founders Said
Replace the placeholder testimonials below with real quotes from Stotodo's founders — LinkedIn recommendations, WhatsApp messages, or email quotes work well.
"Kevin didn't just run ads — he built us a growth engine we didn't know we needed. Within 3 months we had systems running that I couldn't have imagined when he joined. The 8× revenue growth speaks for itself, but what impressed me more was how structured his thinking was from day one."
Revenue grew 8× during Kevin's engagement"What stood out about Kevin was his ownership. He didn't wait to be told what to do — he diagnosed the problems himself, built a plan, and executed it. The WhatsApp retention system he built still runs today and generates revenue every month without any ongoing effort from our side."
Retention system still generating revenue post-engagement