BIGATOM by AdYogi: E-Commerce Analytics and SKU-Level ROAS Optimization for VERO MODA

Case Study: Strategies used by Vero Moda with AdYogi

Scaling D2C Catalogue Spends Profitably on Meta & Google While Reducing Discount Dependency and Operational Complexity

Overview

As a leading international women's fashion brand operating a massive inventory across 15+ product categories, Vero Moda recognized the critical need to aggressively scale its Direct-to-Consumer (D2C) channels. However, managing catalogue marketing across hundreds of active product styles manually presented major roadblocks. Spends often saturated specific items while ignoring new collection launches, severely limiting margin optimization and scaling speed.

To overcome these scale constraints, Vero Moda partnered with AdYogi to integrate the BigAtom platform—an AI-powered catalogue intelligence and automation layer built specifically to drive high-performance bidding, custom dynamic merchandising overlays, and automated stop-loss guardrails across Meta & Google.

The Core Challenge

How do you profitably scale catalogue ads on Meta & Google without increasing discount dependency or operational complexity?

- Vero Moda Scaling Dilemma
Referenced from Case study (VEROMODA).pdf

Detailed Scaling Challenges

01 Heavy Reliance on Discount-Led Sales

A significant portion of D2C ecommerce revenue was driven through aggressive promotional discounts. While this cleared excess volume, it limited overall margin expansion, built a high shopper dependency on seasonal offers, and caused unstable performance whenever discounts were reduced.

02 Inefficient Catalogue Scaling

Meta and Google's native algorithms lack deep product-level merchandising intelligence. Consequently, ad budgets were disproportionately allocated to a small percentage of existing bestsellers, leaving high-potential new arrivals entirely unscaled and starving for exposure.

03 Inventory Inefficiencies at Variant Level

In fashion commerce, products are often marked "In Stock" on standard feeds despite core size-variants (the most common converted sizes) being entirely sold out. Native ad networks kept driving paid traffic to these broken product listings, resulting in high drop-off rates and wasted ad spends.

04 Creative Fatigue Across Large Catalogues

Static catalogue creatives quickly lose engagement in competitive news feeds. Furthermore, native overlay implementations presented major operational issues: updating overlays triggered campaign learning resets, failed to adapt dynamically to product frames, and disrupted premium brand aesthetics.

The BigAtom Solution

1. Unified Product Intelligence

BigAtom bridged native reporting silos by combining Shopify, Meta, Google, and GA4 data streams. This enabled Vero Moda to move budget allocations away from broad campaign averages and prioritize high-margin, high-converting SKUs that held strong organic traction.

2. Automated Spend Control with Stop-Loss Rules

To prevent saturated products from consuming excess budgets, automated stop-loss logic was configured. The platform continuously monitored SKU-level ROAS trends and automated execution rules:

• Exclusion rule: Automatically exclude products where weekly spends exceed ₹5,000 and ROAS falls below 3.5X.
• Inclusion rule: Automatically promote items when organic revenue exceeds ₹1,000 and conversion rate rises above 2%.

3. Inventory-Aware Catalogue Automation

The platform analyzed size-level inventory depth and variant-to-revenue contribution patterns. Items missing core sizes or major color ranges were automatically deprioritized on ad delivery feeds until warehouse replenishment occurred, preserving precious budget.

4. Creative Automation & Smart Overlays

Rule-based overlays and background refreshes were automatically applied at scale across active catalog listings. These custom overlays adjusted dynamically to individual product frames without ever resetting Meta's active campaign learning phases.

Vero Moda Performance Optimization

Conclusion

Vero Moda’s operational scale gains demonstrate that high inventory volume and robust digital scaling can coexist profitably when backed by variant-level merchandising intelligence.

By integrating AdYogi’s BigAtom framework, the brand successfully solved the fashion commerce paradox—maintaining healthy bottom-line ROAS and accelerating inventory rotation while reducing discount dependency.

Grow Your Brand With AdYogi Across Channels

D2C: Meta Business Partner, Google, Snapchat
Marketplace: Amazon, Myntra, Flipkart
Quick Commerce: Swiggy Instamart, Zepto, Blinkit

Partner Brands: Raymond, Underneat, Milton, Lifelong, 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, Gully Labs, Twamev, FILA, Pepe Jeans London, Jaypore, Wrogn, Tramontina, Veirdo, Bewakoof, Jaypore