The Scale up Journey of a 300 Crore Women Apparel Brand

Case Study

Content

01 Brand Background

02 Challenges

03 What We Built: The Growth Strategy

• AOP: The Annual Growth Blueprint
• Purple Day Sale: The Bi-annual Scale Play
• Strategic Full-Funnel Marketing
• Automations For Bottom-Funnel Efficiency
• Repeats & Retention: Building LTV
• Offline Stores: Scaling beyond online

Brand Background

To scale a large women's ethnic-wear brand from ₹60 Cr to ₹300 Cr in revenue profitably, fashion D2C brands must move away from manual "hero-SKU" management and implement a structured, data-driven growth engine. Libas, one of India's leading women's ethnic fashion brands, had already established strong brand recall and consistent demand. However, as the brand increased marketing investments to drive further growth, performance began to plateau due to challenges around efficient scaling, ROAS volatility, wasted spend on out-of-stock core sizes, and D2C vs marketplace price-parity issues.

Libas partnered with AdYogi to build this automated growth system. By shifting the focus from short-term campaign optimisation to AdYogi's category-based revenue planning and BigAtom automation platform, the brand sustained scale across a 5,000+ SKU catalog.

The Challenge: The "Scaling Paradox"

Libas had strong brand recall and steady demand, but like many D2C brands, growth started to plateau once they tried to push budgets harder. The problem wasn't demand — it was efficient scaling. The primary roadblocks were:

1) The ROAS Ceiling

Whenever budgets were increased, performance didn't scale linearly. ROAS became volatile because the system started pushing into higher-cost audiences and less efficient inventory. In short: incremental spend was buying incrementally worse traffic, making scaling feel risky and unpredictable.

2) Catalog Blind Spots

When scaling a 5,000+ SKU fashion catalog, manual campaign management naturally forces the team to focus on a small set of proven "hero" SKUs. Over time, those heroes start to fatigue (higher frequency, lower CTR/CVR), while thousands of other products — including high-intent, high-margin, or trend-driven styles — never get a chance to be discovered through ads. AdYogi prevents this visibility imbalance by deploying automated product performance tracking, ensuring budgets are dynamically reallocated across the full catalog rather than over-investing in a few items.

3) The Discount Trap

Growth leaned heavily on price-offs to create short-term spikes in conversion. But this came with two costs:

• Margin erosion, especially when discounts stacked with ad costs.
• Low-LTV acquisition, where customers primarily purchased only during heavy sale periods and didn't return at full price.

The outcome: sales improved, but the quality of growth (profitability + repeat rate) weakened.

4) The Marketplace Conflict

Pricing and promotions weren't always consistent between Libas' D2C website and marketplaces. That inconsistency trained customers to price-compare and shift purchases to wherever the deal looked better, leading to D2C cannibalization. This also reduced the effectiveness of D2C retargeting — people discovered on D2C, but converted elsewhere.

5) Lack of Full Funnel Marketing

Budgets were skewed toward bottom-funnel retargeting because it showed the best immediate ROAS. But over time, this created a pipeline problem:

• Retargeting pools didn't grow fast enough
• New-user discovery weakened
• Consideration audiences weren't nurtured

So the account became dependent on a small pool of warm users, making scaling difficult and performance fragile during non-sale periods.

6) Creative Constraints & Fatigue

Libas relied heavily on static creatives, which immediately limited how much the campaigns could scale and stay fresh. Static-only output restricted the brand from fully leveraging high-performing dynamic formats like videos, carousels, collection-style experiences, and automated catalog creatives—formats that typically drive stronger engagement and better efficiency as budgets grow.

01 No structured creative measurement loop

Creatives weren't consistently tracked and reviewed using clear performance signals (CTR, CVR, thumb-stop rate, add-to-cart rate, frequency vs. drop in performance). As a result, the same styles continued running longer than they should have, even after performance started decaying.

02 Catalog ad creative fatigue

Catalog ads repeatedly showed the same product images (often plain product shots on similar backgrounds). As frequency increased, audiences got desensitized, leading to lower CTR, weaker conversion rates, and rising CPMs—especially when the brand attempted to scale spend.

03 Limited personalization by audience cohort

With static creatives, the messaging and visual context stayed the same for everyone. That meant Libas couldn't tailor creatives for different buyer mindsets (value-seekers vs. new-season shoppers vs. occasion buyers), which reduced relevance and muted performance at scale.

04 Catalog ad creative fatigue

Without automation or modular creative systems, new creative variations took time—so campaigns couldn't react quickly to fatigue, seasonality, or product momentum.

Net impact: Even when product-market demand was strong, the account hit a creative ceiling—scaling budgets increased frequency and fatigue faster than it increased incremental conversions, putting pressure on ROAS and limiting sustainable growth.

Strategies

01 Annual Operating Plan (AOP) — Turning Growth Into a System

Instead of running marketing as a monthly "performance sprint," AdYogi aligned with Libas on a structured Annual Operating Plan (AOP) — a single source of truth for growth targets, seasonality, category focus, and execution priorities designed to scale ethnic wear profitably.

What we aligned on

1 Annual growth goals + monthly runway:
We broke the annual revenue goal into monthly targets that accounted for seasonality (sale spikes vs. non-sale months), expected inventory depth, and new collection launches.

2 Merchandise-led marketing planning:
Marketing inputs were built around what the brand was prioritizing — core categories, hero collections, ASP goals, and inventory availability — so spends were tied to business readiness, not just platform performance.

3 Creative requirements mapped in advance:
We planned creative volume and formats ahead of time (catalog variations, new drops, sale creatives, occasion-based themes), ensuring campaigns didn't hit a creative bottleneck when scaling.

How we kept the plan "alive"

1 Deviation tracking:
We monitored weekly performance vs. plan (revenue, ROAS, CAC, contribution by category). Any variance was flagged early — not at month-end.

2 Root-cause + Cover strategies:
When performance deviated, we diagnosed the reason (creative fatigue, inventory gaps, pricing conflicts, audience saturation, category softness) and deployed counter-measures quickly:

• Shift budgets to stronger categories/SKUs
• Introduce new creative variations,
• Push a different offer construct,
• Activate mid-funnel demand capture,
• Rebalance prospecting vs. retargeting.

3 Why it mattered
This reduced "reaction marketing" and created predictable scaling. Libas moved from chasing ROAS daily to running a controllable growth engine aligned with business priorities.

02 Category-Wise Revenue Planning + Price Parity — Fixing the Foundation

AdYogi replaced the "spend-and-see" approach with a category-wise revenue and efficiency plan, ensuring every key category had a defined role in growth (scale, margin protection, new-user acquisition, or volume) and achieved a positive Contribution Margin 2 (CM2).

What changed

1 Category-level targets:
To prevent ROAS volatility during scaling, AdYogi sets revenue and ROAS guardrails by category (e.g., kurtas vs. co-ords vs. ethnic sets), so budgets follow a structured plan instead of being pulled only by short-term, last-click ROAS.

2 SKU distribution by category:
We ensured each category had a healthier mix beyond a few hero SKUs — expanding discovery into high-potential products that were previously invisible.

The Key Unlock: Price Parity Strategy to Stop Cannibalization

One of the biggest growth blockers was inconsistent pricing between D2C and marketplaces. When customers found better deals on marketplaces, D2C campaigns ended up doing "discovery work" while conversion happened elsewhere — hurting D2C ROAS and limiting website scaling.

What we implemented

1 Uniform pricing across channels (strict cross-channel price parity):
AdYogi enforced strict price parity so marketplaces could not undercut the D2C store's effective checkout price.

2 Shifted value perception to D2C:
Once parity was fixed, AdYogi positioned the Libas D2C website as the superior channel to prevent marketplace cannibalization, offering:

• Loyalty perks and exclusive customer retention hooks.
• Early access to new drops.
• Exclusive bundles to drive up Average Order Value (AOV) and protect CM2.
• Better customer experience + retention hooks

Why it mattered

Price parity protected D2C conversion, reduced cannibalization, and strengthened the economics of running acquisition campaigns because the brand could now retain users and collect first-party data without leakage.

03 Purple Day Sale (February & August) — Converting a Sale Into a Growth Lever

Purple Day Sale was happening twice a year — but we stopped treating it like a clearance-only event and redesigned it as a full-funnel growth moment: acquisition + conversion + AOV lift + repeat capture

Pre-buzz Framework (10 Days Before)

Instead of waiting for the sale to start, we built intent early.

• Teaser + wishlist momentum: Creatives and messaging were designed to drive "Add-to-Cart / Wishlist" behavior even before discounts went live.
• Audience building: Pre-buzz traffic created high-intent pools for retargeting during the sale window — making sale-day ROAS more stable at higher spends.

Offer Engineering for AOV Lift

Instead of only percentage discounts, we used structured offers that increased basket size.

• Bundles like "Buy 3 at ₹X" were introduced to push multi-item carts.
• This drove higher AOV (your claim: ~25% uplift), making paid acquisition more viable even during promo periods.

High-ASP Priority to Protect Margins

Sales usually spike volume but hurt profitability. We ensured scaling didn't come at the cost of contribution margin.

• We focused budgets on high-ticket bundles and ensembles where even discounted ASP remained healthy.
• This helped sustain ROAS and margins while still driving scale.

Post-sale Retention Capture

The sale was also treated as an acquisition wave.

• We built retention pathways post-sale (new-user segmentation, repeat nudges, loyalty hooks) so Purple Day didn't just deliver revenue — it improved LTV.

Why it mattered

Purple Day became a predictable, repeatable "scale event" that improved not only revenue but also AOV quality and customer pipeline.

04 Strategic Full-Funnel Marketing (TOF → MOF → BOF)

Top Funnel: Branding & Awareness

Top funnel was built to make scaling sustainable—not overly dependent on retargeting or discount-led spikes. When budgets lean only on bottom-funnel audiences, performance eventually stalls as pools saturate, frequency rises, and costs inflate. We used TOF to continuously bring new users into the funnel and strengthen preference for buying on Libas' own site (reducing marketplace leakage).

How we executed

1 Kiara Advani campaign at scale:
Used as the primary reach + recall engine to build aspiration and modern ethnic positioning.

2 Always-on micro-influencer seeding:
A steady layer of creator-led discovery (try-ons, styling, occasion edits) to improve engagement and keep a continuous discovery loop among Gen Z and millennials.

Middle Funnel: Consideration & Intent

MOF was designed to move "interested but undecided" users into high-intent prospects—especially important in ethnic wear where buyers often browse, compare, and purchase closer to an occasion or a sale window. Instead of relying only on offers, we focused on brand-led demand generation that increased confidence and intent signals.

How we executed

• Intent-building content: Occasion-based edits (workwear, festive, wedding guest), collection highlights, best-seller validation, fabric/fit cues, and category guidance (kurtas vs sets vs co-ords).
• Nurturing window shoppers: Messaging that helped users progress from "looks good" to "ready to buy," increasing depth of engagement (product views, wishlists, add-to-carts).
• Feeding BOF with stronger pools: Built larger, warmer audiences so conversion didn't depend on repeatedly

Bottom Funnel: Conversion

BOF was structured as a lean conversion engine focused on capturing demand at the SKU level—not generic retargeting. Once TOF and MOF created demand and intent, BOF's job was to convert efficiently while protecting ROAS during scale.

How we executed

• SKU-level conversion: Served the most relevant products based on observed behavior and product momentum.
• Automation-led efficiency: Reduced wasted spend (e.g., broken inventory), controlled ROAS volatility (stop-loss guardrails), and minimized creative fatigue through variations—so scaling budgets didn't spike inefficiency
• Predictable scaling: Incremental spend was supported by fresh demand + qualified intent pools, not forced frequency on the same warm audiences.

05 Driving High Repeat Rate (Retention-Led Scale)

Sustainable scale doesn't come from endlessly acquiring new customers—it comes from increasing repeat purchases and LTV so acquisition can stay profitable even as budgets grow. For Libas, retention wasn't treated as "post-purchase email work"; it was built into paid media using BigAtom + AdYogi automation, so existing customers consistently saw the right newness, at the right time, based on what they historically buy.

What we implemented

Existing-Customer Newness Engine (using BigAtom automations)

We created a system to push new collections specifically to past buyers—so repeat purchases were driven by fresh drops, not just discounts. This helped Libas convert an already-warm audience faster, while protecting ROAS and contribution margins.

Retention Loops (Triggers + Exclusive Access)

We built repeat loops that gave past buyers a reason to come back:

• Early access to Purple Day Sale for existing customers (making loyalty feel real, not generic).
• Automated triggers that showcased fresh arrivals based on historical category preferences—so customers weren't seeing random inventory, but "newness in their taste."

LTV-Based Targeting (High-Value Cohorts)

Instead of treating all past customers the same, we identified "high-value" cohorts based on what they prefer and how they typically repurchase.

• Example: customers who buy premium ethnic sets / lehengas behave differently from customers who buy casual kurtas.
• We served cohort-specific "New In" ads and category edits so the messaging and product selection matched their willingness to spend and likelihood to repeat.

Category Progression (Share of Wallet Expansion)

To grow LTV, we didn't only aim for "buy the same thing again." We intentionally moved customers into adjacent categories:

• Apparel buyers were progressively introduced to accessories and perfumes (Libas' newer categories).
• This increased share of wallet and unlocked incremental revenue without increasing acquisition pressure.

The Data Deep-Dive That Powered This

We went beyond surface-level repeat rate and built a cohort-based LTV understanding:

Cohort Bucketing (First Purchase - Future Value)

We analyzed:

• What customers bought as their first purchase,
• Bucketed them by category, sub-category, price band, and sometimes merchandise type,
• And mapped the LTV and repeat likelihood of each cohort.

Next-Best Category Mapping

For each cohort, we identified:

• The most likely second purchase category, and
• Which "next-best" products should be promoted to maximize repeat probability (not just revenue today).

This turned retention into a predictable playbook: "If a customer starts with X, the best next push is Y."

Closing the Feedback Loop (Preventing Low-LTV Acquisition)

Retention insights didn't stay in reporting—they fed back into acquisition and catalog promotion.

• If certain categories/sub-categories/merchandise types repeatedly led to low repeats (either as a first purchase or even after the second purchase), we treated them as low-LTV feeders.
• Those were then excluded or deprioritized from promotion using AdYogi's Smart Product Segments, ensuring we scaled customer cohorts that actually sustain long-term growth.

Automation-Led Bottom Funnel Optimisation: The BigAtom Advantage

To profitably scale a 5,000+ SKU catalog without ROAS becoming volatile, fashion brands must move away from manual, hero-SKU dependent optimization. AdYogi deployed BigAtom (our product performance and automation platform) to run the bottom funnel like a system. The focus was simple: promote the right SKUs, protect ROAS with guardrails, eliminate waste, and keep creatives fresh—at scale.

01 Product Analytics:

Large catalogs often have products with strong organic demand but zero paid visibility because media teams naturally default to known winners. BigAtom solved this by identifying high-intent products—items with strong organic engagement (views, clicks, adds-to-cart) but low or no ad spend—and automatically moving them into promotion.

Outcome: Reduced dependence on a few hero SKUs and expanded the "sellable" catalog without inflating acquisition costs.

02 Smart Product Segments: Catalog Control by Business Objectives

Instead of managing thousands of products manually, we created AdYogi Smart Segments to align daily promotion with monthly business goals. The catalog was dynamically bucketed based on performance and business intent, such as:

1 ROAS tiers
(Winners, stable, exploratory, underperformers)

2 AOV bands
(High ASP margin-protect SKUs vs volume drivers)

3 Product Segmentation
(New launches, merchandise, category, dead inventory)

This ensured budgets were always allocated to the right mix—scale + margin + newness—instead of simply chasing last-click ROAS.

03 ROAS Guardrails: Stop-Loss + Budget Reallocation

Scaling breaks when budgets keep flowing into SKUs that have already peaked. We implemented ROAS stop-loss automation that:

1 Determines optimal spend per SKU within a segment (based on historical efficiency and conversion signals)

2 Automatically stops promotion when a product crosses that threshold or shows performance decay

3 Reallocates budget to the next-best potential products inside the segment.

Crucially, this wasn't just auto-pausing—we also tracked and quantified the savings generated by stop-loss to show how much spend was prevented from going into low-efficiency clicks.

• GROWTH INTENT CAPTURED — INCLUDE IN PROMOTION USING INCLUSION AUTOMATION RULE
• HIGH Growth in ROAS — CONTINUE TO PROMOTE
• Decline in ROAS — STOP PROMOTION USING EXCLUSION AUTOMATION RULE

OPTIMAL PRODUCT SPENDING

04 Broken Inventory Automation: Eliminating Waste from Core Size Stockouts

In fashion, conversions are heavily driven by core sizes (e.g., M / L depending on category). Because major advertising platforms don't understand this nuance—often keeping spending active as long as the parent SKU is "in stock," even if core sizes are missing—AdYogi's BigAtom platform automates size-level inventory control.

The AdYogi system automates this by:
1 Identifying the core size logic by category.
2 Automatically pausing ads for specific SKUs across Meta and Google when core sizes go out of stock, then instantly resuming campaigns when replenished.

Configure Rule → Real-Time Monitoring → Automation

Core Size Out of Stock Excluding Promotion From Meta, Google, Snapchat

Core Size Back-In-Stock Including Back in Promotion Meta, Google, Snapchat

Women Ethnic Motifs Embroidered Fusion Silk Straight Kurta — ₹1799 — Please select a size.

CORE SIZES OUT OF STOCK → Disappointed Customers → No Conversion → Waste of Spends

Impact: AdYogi prevented high-bounce traffic (users clicking but not buying due to missing sizes) and protected a meaningful portion of the budget that would otherwise be wasted during scale periods.

05 Creative Automation: Freshness + Relevance Without Manual Editing

Static catalog images fatigue quickly at scale. We implemented creative automation that dynamically generated variations using:

1 USP/Offer overlays (e.g., "Kiara's Favorites", "70% Off", "Best Seller", "New Drop")

2 Background variations to reduce creative fatigue and keep the feed visually fresh across cohorts and sale windows

This improved engagement without needing design teams to manually edit thousands of images—and ensured catalog ads stayed "new" even when the same products ran longer.

06 Google Title Optimisation: Capturing Higher-Intent Search Demand at Scale

Google Shopping and PMax rely heavily on feed relevance, and titles are a major driver of whether your products show up for high-intent queries. Manual title optimisation is impossible at a 5K–10K+ SKU scale, so we automated it using AI resulting in higher relevance, stronger impression share for valuable queries, improved click quality, and more efficient CPCs driven by better matching.

Current unoptimized title → Our AI engine captured the missing attributes & added high volume relevant trending keywords → AI optimized product title

Libas Blue Kurta Printed — $82.46 $110 — 4.4 stars (2.3K) — 30-day returns

Missing Attributes:

• Material
• Bottom Details
• Dupatta

Potential Opportunity Gained: 87,600 Monthly searches

AI optimized product title: Libas | 3 Piece Cotton Kurta | Straight Suit Kurta With Dupatta | V Neck, Floral Printed, Blue | Palazzos Bottom — 30-day returns — 4.4 stars (2.3K)

Title Optimized as per Google Taxonomy with relevant trending keywords

"AdYogi has worked extensively with Google to build in-depth capabilities on feed optimization to improve Pmax scale and ROAS" — MANU BHAGAT - STRATEGIC AGENCY MANAGER, Google

The Omni-Channel Game Changer: Turning Meta Ads into Measurable Store Revenue

Libas isn't just an online brand—offline stores are a major growth lever. The challenge with most omnichannel setups is that store impact from digital ads stays invisible, which forces teams to optimize only for online conversions. We fixed this by building a closed-loop omnichannel system using Meta's OCAPI + store-level execution + incrementality measurement, aligned to 4-step framework: Get the signals right- Launch offline sale campaigns - Build local content - Measure incrementality.

01 OCAPI: Closing the Online-to-Offline Measurement Loop

What is OCAPI?

OCAPI is Meta's solution that makes offline sales measurable. AdYogi integrated OCAPI to send in-store purchase signals from POS or CRM systems to Meta, allowing brands to attribute, optimise, and scale offline revenue-just like a pixel does for websites.

How OCAPI Works

• Sends offline purchase signals from your CRM/POS to Meta.
• Meta matches transactions with users who interacted with your ads.
• Enables optimization for both website and in-store conversions simultaneously.

02 Store Cohorting: Radius-Based Targeting Around Each Outlet

Store campaigns were structured with store-level targeting, building user cohorts within a 5–10 km radius of key outlets. These cohorts were prioritised based on store potential, local demand, and city performance, with campaigns focused on driving store visits, direction requests, and offline purchases.

Bucket stores into Cohorts for maximum impact

NEW STORES

• These are newly launched stores - opened within the last 12 months.
• The strategy is focused on increasing awareness & footfall to the new store.

PRIORITY STORES

• These are the stores which have high potential and couple possibly contribute more towards revenue.
• Special focus on driving additional revenue for these stores.

PROBLEM STORES

• These are stores that are currently struggling in terms of footfall
• Focus on improving footfall and revenue contribution.

03 Hyper-Local Creatives: Making Ads Feel "From Your Neighbourhood"

Omnichannel works best when ads feel locally relevant. We scaled regional + hyper-local creative so each store cohort saw ads that matched their context:

Festival-led regional pushes: Creative and messaging adapted to regional moments to trigger store visits during peak local demand.

Store-led urgency + availability: Ensured creatives aligned with offline inventory reality so stores could convert the demand we were driving (fast-moving offline stock needed faster creative refresh).

Local language/culture cues: Improved relevance and response rate, especially when targeting nearby audiences.

04 Measuring Incrementality: Proving What Ads Truly Added

Attribution alone can over-credit ads. So AdYogi added incrementality as the final layer:

• Measured the true lift driven by Meta ads on offline sales by comparing exposed vs control audiences (incrementality/Conversion Lift approach).
• Used results to validate what was working and reallocate budgets toward tactics that drove incremental store revenue, not just reported conversions.

AdYogi

Build Your 100 crore brand today

D2C — Meta Business Partner, Google, Snapchat

Marketplace — Amazon, Myntra, Flipkart

Quick Commerce — Instamart, Zepto, Blinkit

Brands: Raymond, Underneat, Milton, Lifelong, मलमल (Malmal), S, Aza, Just Herbs, Vero Moda, Superdry, Westside, Libas, Arth By Emcure, Neeman's, Borosil, Mufti, Kaya, Rare Rabbit, Reliance Retail, Indya, Reebok, Nobero, गली LABS (Gully Labs), Twamev, FILA, Pepe Jeans London, Jaypore, Wrogn, Tramontina, Veirdo, Bewakoof, Jaypore