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.
How it's measured
- Numerator
- ₹50L/month (peak, Dec 2024)
- Denominator
- ₹6L/month (baseline, Mar 2024)
- Source
- Shopify revenue dashboard
- Note
- Total revenue, all channels combined
How it's measured
- Numerator
- Sessions that completed purchase
- Denominator
- Total landing page sessions
- Source
- Google Analytics 4
- Note
- Post-rebuild average. Pre-rebuild: ~8%
How it's measured
- Definition
- CVR of remarketing vs. cold traffic
- Source
- Meta Ads Manager
- Attribution
- 7-day click / 1-day view
- Note
- Measured across retargeting ad sets
How it's measured
- Definition
- Revenue from returning customers ÷ total revenue
- Source
- Shopify customer analytics
- Note
- Baseline at launch was ~0% (no retention system)
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
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.
“We've got a product that works. We just need to find customers at scale — and make sure they come back.”
What I Found on Day One
A full audit before any execution. Diagnose first, prescribe second.
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.
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.
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%.
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%.
Pre-Intervention Funnel — Diagnosed Leak Points
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.
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.
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.
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.
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.
How the Growth Happened
Month by month — not just that it happened, but how and in what sequence.
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/moMeta 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%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%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×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-sustainingThe Numbers, With Full Context
Hover each metric to see the full methodology and data source.
How it's measured
- Source
- Shopify dashboard · December 2024
- Baseline
- ₹6L/month (March 2024)
- Note
- Total revenue across all channels
How it's measured
- Source
- GA4 · Jun–Dec 2024
- Baseline
- ~8% (March 2024)
- Attribution
- Session-based, same-session purchases only
How it's measured
- Source
- Shopify + Meta + Google Ads Manager
- Model
- Blended — total revenue ÷ total ad spend
- Period
- Stable from Aug 2024 onwards
How it's measured
- Source
- Shopify customer analytics
- Definition
- Revenue from 2nd+ purchase customers
- Baseline
- ~0% (no retention system in March 2024)
Before → after
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.
The receipts
Every number on this page traces back to a system of record. Screens are available on request — some figures are commercially sensitive, so they aren't published.
Broad Audience Meta Ads — Scaled Before Proving the Creative
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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
Liked What You Saw?
I'm available for immediate hire — full-time, fractional, or contract. If you're building a D2C, SaaS, or high-growth brand and need someone who takes end-to-end ownership of growth outcomes, let's talk.
Replies within 24 hours.