Case Study: How We Increased a D2C Brand's Conversions by 47%

The Brand

A premium women's fashion brand based in Mumbai approached us in late 2024. They sell contemporary Indian fusion wear—kurtas, co-ord sets, and occasion wear—primarily to women aged 25-40 in metro cities.

Their numbers looked healthy on the surface:

• Monthly revenue: ₹85-95 lakh
• Monthly traffic: 180,000 visitors
• Average order value: ₹2,800
• Strong Instagram presence with 340K followers

But their conversion rate told a different story: 1.1%.

Industry benchmark for fashion D2C in India is 1.8-2.5%. They were leaving significant money on the table.

At their traffic levels, moving from 1.1% to even 1.8% conversion would mean an additional ₹1.2 crore in annual revenue. Without spending a single additional rupee on ads.

That gap is why they called us.

Note: We are not disclosing the brand name per our confidentiality agreement. All metrics and details are accurate.

The Diagnosis

Before proposing solutions, we spent two weeks understanding exactly why conversions were suffering. We do not guess. We measure.

Quantitative Analysis

We installed Microsoft Clarity for session recordings and heatmaps, analyzed Google Analytics 4 data, and audited their Shopify analytics.

The funnel breakdown revealed where customers were dropping:

Stage	Visitors	Drop-off
Homepage/Landing	180,000	—
Collection Page	142,000	21%
Product Page	89,000	37%
Add to Cart	31,000	65%
Initiate Checkout	12,400	60%
Purchase	1,980	84%

Two massive drop-offs stood out:

Product Page to Add to Cart: 65% of product page visitors left without adding anything. Something on product pages was failing.

Add to Cart to Purchase: 84% of people who added items to cart never completed purchase. The checkout funnel was hemorrhaging customers.

Session Recording Review

We watched over 200 session recordings, categorizing behavior patterns.

On Product Pages:

Customers scrolled past the add-to-cart button looking for more information. The button was above the fold on desktop but key details (size chart, fabric info, delivery time) were buried below.

Size chart interactions were frustrating. Customers clicked the size chart link, a popup appeared, they studied it, closed it, then had to scroll back up to select a size. Many abandoned during this friction.

Mobile users struggled with the image gallery. Swipe gestures were not intuitive. Pinch-to-zoom was slow. Customers wanted to examine fabric texture but the experience was clunky.

On Checkout:

Customers reached the shipping information page and paused at the phone number field. Many were entering numbers, seeing errors, correcting, and re-entering. The validation was too aggressive.

Delivery date estimates were missing. Customers reached payment selection without knowing when they would receive their order. Several recordings showed customers abandoning at payment after scrolling up and down looking for delivery information.

Payment failures were not handled well. UPI transactions that timed out showed generic error messages. Customers did not know if they should retry or if the order went through.

Speed Audit

PageSpeed Insights showed:

• Mobile score: 34
• Desktop score: 58
• Largest Contentful Paint (mobile): 5.2 seconds
• Time to Interactive (mobile): 7.8 seconds

For a fashion brand where 82% of traffic was mobile, a 5+ second LCP was devastating. Research shows each second of load time reduces conversions by 7%. They were losing customers before the page even fully rendered.

Technical Audit Findings

We audited the theme code and found:

23 Shopify apps installed, 9 of which were not actively used. These orphaned apps still loaded JavaScript on every page.

No lazy loading on product images. Collection pages loaded all 48 product images immediately, regardless of scroll position.

Render-blocking JavaScript from three apps: a reviews app, a currency converter, and an abandoned upsell tool.

Unoptimized product images. Many images were 3000x3000 pixels displaying at 400x400. Massive unnecessary file sizes.

No critical CSS inlining. The full 380KB stylesheet loaded before any content appeared.

The Strategy

Based on the diagnosis, we developed a prioritized optimization plan targeting three areas:

1. Product Page Experience (addressing the 65% drop-off)
2. Checkout Flow (addressing the 84% abandonment)
3. Site Performance (addressing the 5.2s LCP)

We presented a 6-week implementation plan. The brand approved, and we began work.

Phase 1: Product Page Transformation

Weeks 1-2 focused entirely on product pages, where we were losing the most potential customers.

Restructured Information Hierarchy

The original product page had:

• Product images (left)
• Title, price, variant selectors, add-to-cart (right)
• Description and details (below the fold)
• Reviews (far below)

Customers were scrolling past the purchase area looking for information they needed to make a decision.

We restructured to:

• Product images (left, improved gallery)
• Title and price (right, top)
• Key info tabs: Size Guide | Fabric & Care | Delivery (right, prominent)
• Variant selectors with visual swatches
• Add-to-cart button (sticky on mobile)
• Trust badges inline (returns, authenticity, support)
• Description and reviews below

The critical change: information customers needed was accessible without scrolling away from the purchase action.

Rebuilt Size Selection Experience

Size selection was a major friction point. We rebuilt it completely.

Before:

• Radio buttons for sizes (S, M, L, XL)
• "Size Chart" link opening a popup
• No fit guidance

After:

• Visual size buttons showing availability
• Out-of-stock sizes visible but greyed (with "Notify Me" option)
• Inline size recommendation: "Based on your measurements, we recommend M"
• Expandable size chart directly on page (no popup)
• Fit details: "Model is 5'6" wearing size S"
• Previous customer feedback: "82% say this fits true to size"

We integrated a simple size recommendation engine. Customers entered height and weight (optional), and the system suggested a size based on the brand's historical return/exchange data.

The size recommendation reduced size-related returns by 23% in the following months—an additional cost saving beyond conversion improvement.

Enhanced Mobile Image Gallery

The original mobile gallery used default Shopify swipe behavior. It worked but was not optimized for fashion browsing.

We implemented:

• Faster swipe transitions (reduced from 300ms to 150ms)
• Pinch-to-zoom with high-resolution image loading on zoom
• Thumbnail strip below main image for quick navigation
• Video integration for products with styling videos
• Double-tap to zoom shortcut

Small details, but fashion customers examine products closely. The gallery needed to feel premium, not default.

Added Urgency and Social Proof

We added contextual, honest urgency signals:

• Real-time stock levels for low-inventory items: "Only 3 left in M"
• Recent purchase notifications (real data, not fake): "12 people bought this today"
• Review summary prominently displayed: "4.6 ★ from 234 reviews"

We specifically avoided fake urgency. No "10 people viewing this right now" nonsense. Indian customers are increasingly skeptical of manufactured urgency. Real data builds trust.

Results After Phase 1

After two weeks, we measured product page performance:

Metric	Before	After	Change
Add-to-Cart Rate	17.2%	24.8%	+44%
Size Chart Engagement	31%	52%	+68%
Avg. Time on Product Page	48 sec	71 sec	+48%
Product Page Bounce Rate	62%	51%	-18%

Customers were engaging more deeply and adding to cart at significantly higher rates. But we still had the checkout funnel to fix.

Phase 2: Checkout Optimization

Weeks 3-4 focused on the checkout flow—the 84% drop-off from cart to purchase.

Pre-Checkout: Cart Page Improvements

The cart page is the last chance to reassure customers before they commit to checkout.

Changes implemented:

Delivery Estimate in Cart: Before entering checkout, customers now saw "Estimated delivery: [date] to [city]" based on their IP-detected location. This answered the #1 question we observed in session recordings.

Trust Reinforcement: Added a summary bar: "Free shipping ✓ | Easy 7-day returns ✓ | Secure payment ✓"

Order Summary Clarity: Itemized subtotal, shipping, taxes, and total. No surprises at checkout. If shipping was free (orders over ₹999), we highlighted the savings: "You're saving ₹99 on shipping!"

Express Checkout Options: Added prominent GPay and PhonePe buttons above the standard checkout button. Many customers prefer express checkout that skips address entry if they have payment app profiles.

Checkout Flow Fixes

Shopify's native checkout is good, but we customized several elements (the brand is on Shopify Plus):

Relaxed Phone Validation: The aggressive validation was rejecting valid numbers during entry. We adjusted to validate on field exit rather than on every keystroke.

Added Delivery Date to Shipping Options: Previously, shipping options showed "Standard Shipping - ₹99" with no timeline. Now: "Standard Shipping - ₹99 (Delivery by March 18-20)"

COD Optimization: Cash on Delivery was available but treated as a second-class option, listed last. For this brand's audience, COD was critical—34% of orders used it. We moved COD to a more prominent position and added a small COD fee (₹49) with clear messaging to offset RTO risk.

Order Confirmation Page: Added clear next-step information: "You'll receive a confirmation email shortly. Track your order anytime at [link]."

Payment Failure Recovery

This was a critical fix that most brands overlook.

When a UPI payment timed out or failed, the original experience showed a generic error. The customer had to start checkout again from scratch. Many simply left.

We implemented:

Specific Error Messages: "Your UPI payment timed out. This sometimes happens with bank delays. Click below to try again—your cart is saved."

One-Click Retry: Payment retry button that preserved all checkout information.

Alternative Payment Suggestion: "Having trouble with UPI? Try paying with card or COD instead." with one-click payment method switch.

Abandoned Payment Recovery: If customer left after payment failure, automated WhatsApp message within 30 minutes: "Hi [Name], we noticed your payment didn't go through. Your cart is saved—click here to complete your order."

We tracked payment failure recovery specifically. Of customers who experienced payment failures, 31% completed purchase after our recovery flow was implemented. Previously, that number was under 10%.

Results After Phase 2

Checkout metrics after implementing all changes:

Metric	Before	After	Change
Cart-to-Checkout Rate	40%	58%	+45%
Checkout Completion Rate	16%	29%	+81%
Payment Failure Recovery	9%	31%	+244%
COD Order Rate	34%	36%	+6%

The checkout was no longer a leaky bucket.

Phase 3: Performance Optimization

Weeks 5-6 focused on speed. We had fixed the experience, but customers still had to wait 5+ seconds for pages to load.

App Cleanup

We audited all 23 installed apps:

• 9 apps removed entirely (unused or redundant functionality)
• 4 apps replaced with lighter alternatives
• 3 apps configured to load only on relevant pages
• 7 apps kept as-is (necessary and well-optimized)

Total JavaScript reduction from app cleanup: 340KB removed.

Image Optimization

We implemented comprehensive image optimization:

Responsive Images: All product images now serve device-appropriate sizes. Mobile devices receive 600px images, not 3000px images scaled down by the browser.

Lazy Loading: Images below the fold load only as customers scroll near them. Initial page load now includes only visible images.

WebP Format: All images now serve as WebP to supporting browsers (95%+ of traffic). Average 30% file size reduction versus JPEG.

Image Compression: All images run through optimization, targeting 85% quality (visually indistinguishable from 100%, significantly smaller files).

Result: Average collection page image payload dropped from 4.2MB to 890KB.

Critical CSS and JavaScript Deferral

We restructured how CSS and JavaScript loaded:

Critical CSS Inlined: The CSS needed for above-the-fold content (header, hero, first product row) was inlined in the HTML head. No render-blocking stylesheet request.

Full CSS Loaded Async: The complete stylesheet loaded asynchronously after initial render.

JavaScript Deferred: All non-critical JavaScript used defer attribute. Third-party scripts (chat widget, analytics) loaded after user interaction or 5-second delay.

Results After Phase 3

Metric	Before	After	Change
Mobile PageSpeed Score	34	78	+129%
Largest Contentful Paint	5.2s	1.8s	-65%
Time to Interactive	7.8s	2.9s	-63%
Total Page Weight (Collection)	5.8MB	1.4MB	-76%

The site now loaded in under 2 seconds on mobile. Customers could browse and purchase without waiting.

The Combined Results

Six weeks after starting the project, we measured overall performance against the original baseline.

Conversion Rate

Period	Conversion Rate	Orders (Monthly)
Before (Baseline)	1.1%	1,980
After (Month 1)	1.47%	2,646
After (Month 2)	1.58%	2,844
After (Month 3)	1.62%	2,916

The conversion rate increased 47% from baseline (1.1% → 1.62%) within three months.

Revenue Impact

With stable traffic of ~180,000 monthly visitors and AOV of ₹2,800:

Metric	Before	After	Monthly Impact
Monthly Orders	1,980	2,916	+936 orders
Monthly Revenue	₹55.4L	₹81.6L	+₹26.2L
Annual Revenue	₹6.65Cr	₹9.79Cr	+₹3.14Cr

The brand gained ₹3.14 crore in annual revenue from the same traffic they were already receiving.

Additional Metrics

Metric	Before	After	Change
Average Session Duration	2:12	3:41	+68%
Pages per Session	3.2	4.8	+50%
Bounce Rate	54%	41%	-24%
Cart Abandonment Rate	78%	61%	-22%
Return Rate (Size Issues)	18%	14%	-22%

Customers engaged more deeply, abandoned less frequently, and returned fewer items due to sizing issues.

What Made This Work

Reflecting on the project, several factors drove success:

Data-Driven Diagnosis

We did not assume we knew the problems. We measured, watched session recordings, and let customer behavior guide our priorities. The solutions addressed actual friction points, not theoretical ones.

Prioritized High-Impact Changes

We focused on the biggest drop-off points first. Product pages and checkout represented 80% of the conversion gap. We fixed those before optimizing homepage or collection pages.

Incremental Validation

We measured after each phase, confirming improvements before moving to the next. If Phase 1 had not shown results, we would have investigated further before proceeding.

Holistic Approach

Experience improvements (Phase 1-2) and performance improvements (Phase 3) compound each other. A beautiful product page that takes 5 seconds to load still loses customers. A fast page with confusing UX still loses customers. Both matter.

Attention to Indian Market Specifics

COD handling, UPI payment recovery, WhatsApp as a recovery channel, phone number validation quirks—these India-specific details matter. A playbook from US ecommerce blogs would have missed them.

What We Would Do Differently

Transparency matters. Here is what we learned:

We should have started performance optimization earlier. Speed improvements take time to reflect in Google's CrUX data (real user metrics). Starting Phase 3 in week 1 (parallel to Phase 1) would have accelerated SEO benefits.

We underestimated the size recommendation impact. The size recommendation engine became one of the highest-engagement features. We could have invested more in making it sophisticated earlier.

WhatsApp recovery should have been day-one priority. The payment failure WhatsApp recovery delivered outsized results. We implemented it in week 4. It should have been week 1.

Conclusion

This brand had strong products, strong traffic, and a loyal audience. But their website was not converting that attention into revenue.

The problems were not exotic. Size selection was confusing. Checkout lacked delivery information. Payment failures were handled poorly. Pages loaded slowly. These are common issues—but their combined impact was ₹3+ crore in lost annual revenue.

The fixes were not exotic either. Better information hierarchy. Clearer size guidance. Smarter checkout flow. Payment recovery. Performance optimization. Nothing revolutionary. Just thorough execution of fundamentals.

That is the lesson: most conversion problems are solved by doing the basics extremely well, not by chasing novel tactics.

If your Shopify store has traffic but conversions are below industry benchmarks, you likely have fixable problems. The gap between where you are and where you could be might be worth crores in annual revenue.

We offer conversion audits that identify your specific friction points and prioritized recommendations to address them. Our audits pay for themselves many times over when implemented.

---

Frequently Asked Questions

What is a good conversion rate for Shopify stores in India?

For fashion D2C brands in India, 1.8-2.5% is typical. Premium brands with strong audiences can reach 2.5-3.5%. Conversion rates below 1.5% usually indicate fixable UX or performance issues.

How long does it take to see conversion rate improvements?

Initial improvements are visible within 2-4 weeks of implementing changes. Full impact typically stabilizes within 2-3 months as you gather sufficient data and make iterative refinements.

What is the most common reason for low Shopify conversions?

Based on our audits, checkout friction is the most common issue—unclear shipping costs, missing delivery estimates, poor payment failure handling, and required account creation. Product page issues (size selection, insufficient information) are the second most common.

How much does conversion rate optimization cost?

A comprehensive CRO project like this case study typically costs ₹3-6 lakh depending on scope and complexity. The ROI is usually 10-30x within the first year based on revenue improvement from the same traffic.

Can you guarantee conversion rate improvements?

We cannot guarantee specific percentage improvements, as results depend on your starting point, traffic quality, and product-market fit. However, every audit we have conducted has identified actionable improvements, and every implementation has produced measurable results.