"Mehul rebuilt our entire Google Ads account by intent tier — ROAS went from 3× to 8.5× in one quarter. He also built our analytics infrastructure from scratch. We actually know what's working now."
"Doubled our ROAS in under 2 months. No fluff, no promises — just restructured the campaigns, fixed the tracking, and showed us the numbers."
"Consistent 500+ qualified leads per month at under ₹295 CPL. Mehul built the entire measurement framework from scratch — we could see where every lead came from."
"Mehul rebuilt our entire Google Ads account by intent tier — ROAS went from 3× to 8.5× in one quarter. He also built our analytics infrastructure from scratch. We actually know what's working now."
"Doubled our ROAS in under 2 months. No fluff, no promises — just restructured the campaigns, fixed the tracking, and showed us the numbers."
"Consistent 500+ qualified leads per month at under ₹295 CPL. Mehul built the entire measurement framework from scratch — we could see where every lead came from."
A growth-focused Manager blending data, strategy, and execution.
Mehul Bhaliya. Category Manager and Growth Lead at Sierra Living Concepts, a US D2C furniture brand.
In 14 months, Kitchen & Bar went from ₹22L to ₹1.4Cr/month — a 6× revenue jump at 40%+ gross margin throughout. That happened because I owned every decision in the chain: assortment, pricing, paid acquisition, CRO, and the GA4+GTM analytics infrastructure I built from scratch.
I now lead 5 Junior Category Managers across 300+ SKUs and own full P&L accountability for the portfolio.
Before Sierra, I ran freelance mandates for Aarav Hotel (ROAS 1.5× → 3× in 2 months) and Zetrance (478–523 leads/month at ₹277–293 CPL across Rajasthan). I don't run campaigns in isolation — I build the system the campaigns run on.
MBA, IIT Jodhpur.
A history of owning e-commerce operations, scaling categories, and optimizing funnels.
Full lifecycle ownership from assortment curation and vendor negotiation to pricing strategy and monthly P&L reporting. I've taken a category from ₹22L to ₹1.4Cr/month — the result of owning every decision in the chain, not just the strategy doc.
Full-funnel campaign management across Google Ads (Search, Shopping, PMax) and Meta — with measurement infrastructure built in from day one. Most campaigns fail because tracking is broken before the ads even go live. That's the first thing I fix.
Server-side GA4+GTM infrastructure, 20+ custom events, 14 Looker Studio dashboards, and UTM standards your whole team actually follows. Built for a $150K+/month D2C operation — so your team stops guessing and starts deciding.
Structured A/B testing, Zoho CRM automation with lead scoring and lifecycle workflows, and product configuration tools that lifted add-to-cart from 2.82% to 3.63% — without changing a single product. If you have traffic, there's revenue hiding in your funnel.
Owned end-to-end P&L for 300+ SKUs. Scaled revenue 6× using pricing strategy, 8.5× ROAS campaigns, SEO, and CRO. 40%+ gross margin throughout.
Zero to ₹17L+/month in 6 months — 15-SKU assortment, Good-Better-Best pricing architecture, Google Shopping, Meta, and GA4 wired in from day one.
Rebuilt full account by intent tier. Target ROAS bidding per margin segment. A/B tested ad copy, landing pages, bid strategies.
Server-side tagging, 20+ custom events, 14 Looker Studio dashboards, UTM standards, GA4 audience exports for RLSA.
Automated WhatsApp and email sequences, enforced a 2-hour follow-up SLA, and ran 20+ PDP A/B tests — adding over ₹1.3Cr in quarterly revenue without increasing ad spend.
Configuration across 38 SKUs, 5 dynamic pricing calculators, 20+ A/B tests. Cart abandonment: 84% → 63%.
Google Search & Call ads, audience refinement, LTV/CAC dashboards in Looker Studio. Doubled ROAS in 2 months on ₹45K/month spend.
Google Search, PMax, Meta lead campaigns across Rajasthan. Media plan, messaging matrix, measurement framework from scratch.
Open to full-time roles and consulting mandates in D2C e-commerce, category management, and performance marketing.
Category Management · Performance Marketing · CRO · Analytics
Kitchen Island & Bar was fragmented. No hero SKUs. No paid structure. No PDP standard. Revenue was ₹22L/month and attribution was broken — spend was going out without knowing what was working. The category had 150+ SKUs but no prioritisation logic.
Full P&L — assortment decisions, pricing, Google Ads, Meta, SEO, CRO, and the analytics infrastructure that tied it all together.
Built GA4 server-side tracking + GTM data layer before touching any campaigns. 14 Looker Studio dashboards. Defined 5 standard KPIs as the weekly decision framework. Reporting went from a 3-day manual Excel process to real-time.
Top 20% of SKUs were driving 80%+ of revenue. Reallocated budget away from the long-tail. Hero SKUs got dedicated campaigns with SKU-level ROAS targets. Tail SKUs were consolidated or cut.
Full account restructure: Hero / Mid / Tail segments with separate Target ROAS bidding per margin tier. Rebuilt ad copy, landing pages, and bid strategy simultaneously. R-Factor: 38% → 99.18%.
Found a 56% cart-to-payment drop-off through GA4 funnel analysis. Ran 20+ A/B experiments on PDPs, configuration tools, and checkout. Add-to-cart: 2.82% → 3.63%. Cart abandonment: 84% → 63%.
50+ SEO-optimised pages. 10+ DA 45+ backlinks. 80+ keywords in Top-5 SERPs. 270% organic traffic growth — reduced paid CAC dependency.
| Metric | Before | After |
|---|---|---|
| Monthly Revenue | ₹22L | ₹1.4Cr |
| Revenue Multiple | — | 6× in 14 months |
| Google Ads ROAS | ~3× | 8.5× on ₹30L/month |
| CTR | 0.6% | 1.28% |
| Cart Abandonment | 84% | 63% |
| Add-to-Cart Rate | 2.82% | 3.63% |
| Organic Traffic | Baseline | +270% |
| SERP Top-5 Keywords | ~10 | 80+ |
| Annual GMV | — | ₹40Cr |
Start the SEO build on Month 1, not Month 4. Organic traffic takes 6+ months to compound — I started paid first because of revenue pressure. If I had the timeline again, I'd run both tracks from day one.
Analytics Infrastructure · Data Engineering · Looker Studio
No structured analytics. Decisions based on gut feel and a 3-day manual Excel reporting cycle. Leadership couldn't see the business in real time. Budget was being spent without knowing what was working.
Server-side GA4 + GTM implementation from scratch. 20+ custom events. 14 Looker Studio dashboards used across the company. UTM standards. GA4 audience exports wired into RLSA.
Defined every event the business needed: product views, config interactions, add-to-cart, checkout steps, order confirmation. Built the GTM data layer to capture each one server-side — more reliable, bypasses ad blockers.
14 Looker Studio dashboards: category-level, campaign-level, SKU-level, CRM pipeline, weekly review. Each built for a specific decision — not a vanity report. Leadership could see yesterday's numbers by 9am.
Defined 5 KPIs as the weekly decision framework. Wrote UTM naming conventions adopted across paid + email + affiliate. Team cut underperforming SKUs within the first month of having the data.
| Metric | Before | After |
|---|---|---|
| Reporting Cycle | 3 days, manual | Real-time |
| Custom Events | 0 | 20+ |
| Dashboards | 0 | 14 (Looker Studio) |
| Team Using Data | 1 (manual) | Full team |
| Organic Traffic (downstream) | Baseline | +270% |
Every other result — the 6× revenue, 8.5× ROAS, CRM improvement — runs on this infrastructure. You can't optimise what you can't measure. This was built first.
CRM Automation · Revenue Operations · Lead Scoring
Sales team had no structured lead management. Leads were dropping silently — no visibility into where or why. Close rate was 58.5%. Follow-up was inconsistent; some leads waited 2–3 days. No pipeline view for leadership.
Lead scoring model. Automated WhatsApp + email sequences. 2-hour follow-up SLA — enforced via automation, not manual discipline. Looker Studio pipeline dashboard.
Scored leads by source, product interest, price band, and engagement depth. High-score leads triggered immediate escalation. Low-score leads entered nurture automation. Stopped treating all leads as equal priority.
Built WhatsApp + email drip triggered by lead stage. First touch within 2 hours — automated. Follow-up sequence over 7 days for non-responders. Sales team stopped managing follow-up manually.
Real-time Looker Studio view: leads by stage, conversion by source, average time-to-close by rep. Leadership could see pipeline health without asking the sales team.
| Metric | Before | After |
|---|---|---|
| Lead-to-Close Rate | 58.5% | 71.6% |
| Follow-up SLA | 2–3 days | 2 hours (automated) |
| Incremental Revenue | — | +₹1.3Cr quarterly |
| Pipeline Visibility | None | Real-time dashboard |
The close rate didn't improve because the sales team got better. It improved because leads stopped falling through the cracks. Automation replaced a discipline problem with a system.