Pepe Jeans AdYogi BigAtom Case Study

Scale With Efficiency: How to Maintain Stable ROAS While Scaling Catalog Ads

For global apparel brands scaling catalog ad budgets, maintaining stable ROAS requires shifting from campaign-level management to SKU-level product intelligence. Automations and strategies leveraged by Pepe Jeans using AdYogi's BigAtom platform demonstrate how to scale catalog ads efficiently across Meta and Google without manual intervention.

Contents

• Brand Overview — 01
• Business Context — 02
• Key Results — 03
• Challenges in Scaling — 04
• Solutions & Strategies — 05
• Outcome — 06

Overview

Pepe Jeans partnered with AdYogi's BigAtom platform to improve performance marketing efficiency and unlock scalable growth from its catalog business across Meta and Google.

While the brand had strong demand and steady revenue growth, scaling further without compromising profitability had become increasingly challenging. Growth was constrained by discount dependency, limited control at the product level, and inefficiencies in catalog-led campaigns.

AdYogi's BigAtom platform enabled Pepe Jeans to transition from campaign-level optimisation to a product-led approach — where decisions on catalog ad spend, creatives, and inventory were driven by AdYogi's SKU-level intelligence. By connecting all ad platforms (Meta, Google, Snapchat), website backend, GA4 and inventory data into a unified AdYogi system, the brand was able to scale catalog ads contribution to revenue, improve ROAS stability, and create more structured, sustainable growth.

The Business Context

Pepe Jeans was at a stage where revenue growth was steady, but sustaining efficiency while scaling had become increasingly difficult. The brand needed to unlock new growth while improving profitability and control.

Key business challenges included:

Reducing discount-driven revenue

A large portion of sales was driven by discounts. While this supported volume, it led to inconsistent profitability. The brand needed to identify and scale products that could perform without heavy reliance on offers.

Increasing catalog ads contribution to revenue

Catalog ads, despite being a strong growth lever for fashion brands, contributed only ~20% of total revenue. Scaling this channel across Meta and Google was critical, but challenging due to product saturation, inefficient spend allocation, and lack of SKU-level controls.

Ensuring stability of catalog ad ROAS while scaling

As catalog budgets increased, maintaining consistent & sustainable ROAS became difficult:

• Products with broken inventory (e.g., missing core sizes) continued to receive spend
• Saturated or declining products were not deprioritised in time. This created volatility in returns and highlighted the need for stronger product-level controls such as AdYogi's inventory-aware exclusions and spend thresholds.

Driving diversification of category revenue

Revenue was concentrated in a few dominant categories. Emerging segments like footwear had not yet been systematically scaled due to the absence of a clear framework to identify and push high-potential products.

Solving creative fatigue for catalog ads

With large catalogs, creatives with plain background began to fatigue quickly, leading to declining engagement. In a post-Andromeda environment, maintaining freshness and variation at scale became essential.

The Approach

To address these challenges, Pepe Jeans adopted a product-led growth framework powered by AdYogi automation.

Instead of optimising campaigns in isolation, the focus shifted to:

• Understanding product-level performance across Meta and Google platforms
• Automating decisions around spend, creatives, and inventory
• And aligning marketing more closely with merchandising realities.

This shift allowed the brand to move from reactive optimisation to a more structured and scalable system for growth.

The Solution

Product-Level Performance Intelligence

The first step was establishing true visibility at the SKU level to make catalog ads more efficient.

AdYogi's BigAtom platform integrated data across Meta Ads, Google Ads, GA4, app analytics, and Shopify to create a unified product-performance layer. This was critical because product behaviour varies significantly across platforms — a product scaling on Meta may not perform the same way on Google. Using AdYogi's unified cross-network insights, Pepe Jeans could identify network-specific performance trends at the SKU level.

AI Product Level Analytics & Insights To Drive Efficiency Of Catalog Ads

To maintain stable ROAS while scaling catalog ad budgets, AdYogi collates data from all 4 sources:

• Business Metrics such as Revenue, Returns, COGS, Discounts etc from website
• Performance from Meta Catalogue Ads
• Funnel Metrics such as Conversion Rate, Clicks from Google Analytics, Adobe etc
• Performance from Google Shopping and PMax campaigns

AdYogi's BigAtom platform segments your catalog as per KPIs in 2x2 metrics (ROAS/SKU vs Ad Spends/SKU):

High Potential
- Product Count: 400
- Spends: 10% - 15%
- ROAS: 12x
- Revenue Contribution: 20%

Hero Products
- Product Count: 200
- Spends: 15% - 30%
- ROAS: 6x
- Revenue Contribution: 45%

Zombie Products
- Product Count: 2500
- Spends: 10% - 15%
- ROAS: 1x
- Revenue Contribution: 10%

Non-Performers
- Product Count: 150
- Spends: 35% - 40%
- ROAS: 1.3x
- Revenue Contribution: 25%

Why AdYogi PPM Is Critical
- Revenue concentrated in limited SKUs
- Hero-SKU dependence = volatility
- Zombie spend dilutes efficiency

Outcome
- Horizontal revenue scale
- Stable ROAS at higher spends
- Automated reallocation

Expected contribution
10–12% of incremental revenue

Stop-Loss Automation for Smarter Spend Control

Every product goes through a lifecycle, with different peak performance thresholds across products and categories.

AdYogi's BigAtom stop-loss automation captured the optimal spend range for each product at a given time range and automatically removed such products from promotion across Google and Meta once performance declined. Beyond simple pausing, AdYogi enabled smart inclusion and exclusion logic at the product-set level — removing underperforming products while dynamically reintroducing them if performance recovered.

Filter Summary

Exclusion Rule
- Total spends per product greater than 5000 in last 7 days
- And
- Website blended ROAS is less than 2.2

• Category 1 is Men
• And
• Category 2 is T-Shirt
• And
• Exclude products with "Best Seller"
• Meta: Where campaign is in Pepe New products
• Where campaign is not "PMAX - retargetting"

Inclusion Rule
- Total spends per product less than last 7 days
- And
- Organic revenue per product is greater than 1000 in last 7 days
- And
- Conversion rate is greater than 1.5%

• Category 1 is Men
• And
• Category 2 is T-Shirt
• And
• Exclude products with "Best Seller"

Optimal Spending Per Product:
- Growth Intent Captured — INCLUDE IN PROMOTION USING INCLUSION AUTOMATION RULE
- Sustained Growth in ROAS — CONTINUE TO PROMOTE
- Decline in ROAS — STOP PROMOTION USING EXCLUSION AUTOMATION RULE

This ensured that:

• Inefficient products were automatically identified and automatically removed from promotion
• Spends saved was automatically redirected toward Best Sellers & High Potential SKUs
• Scaling did not lead to uncontrolled wastage

Importantly, the AdYogi system also quantified ad spend saved through automation, giving visibility into efficiency gains.

AdYogi's BigAtom also had signals in place to reintroduce the removed products back into promotion which helped in avoiding dead inventory. This became a key lever in maintaining stability of catalog ad ROAS while scaling.

Inventory-Based Automation

A major inefficiency came from promoting products that were technically in stock but commercially weak due to missing key sizes. Because AdYogi's BigAtom platform is directly connected to the website backend and inventory systems, it enabled size-level inventory intelligence — including understanding revenue contribution by size and color across categories.

• Configure Rule
• Real-Time Monitoring
• Automation
• Core Size Out of Stock — Excluding Promotion From Meta / Google
• Core Size Back-In-Stock — Including Back in Promotion Meta / Google

GRAPHIC BACK PRINT OVERSIZED T-SHIRT RIPLEY - BLACK $ 70 Please select a size. SIZE CHART

Disapointed Customers ➡ No Conversions ➡ Waste Of Spends

Using this SKU-level intelligence, Pepe Jeans configured category-specific rules in AdYogi to:

• Auto-identified revenue contribution of core sizes & colors at category and subcategory level
• Set up customised logic across categories (e.g., different core sizes for footwear vs apparel),
• Auto exclude products when core sizes went out of stock,
• and dynamically reintroduce (removed) products as inventory comes back in stock.

Additionally, AdYogi's inventory run-rate intelligence helped the team make better merchandising decisions, creating a tighter feedback loop between demand generation and supply planning.

• Higher conversion efficiency,
• Reduced wasted spend,
• Stronger ROAS stability during scaling.

SHREYAS IYER in Pepe Jeans LONDON

Creative Automation to Solve Catalog Fatigue

As catalog scale increased, creative fatigue became a major performance bottleneck — especially in a post-Andromeda environment where creative freshness plays a larger role.

AdYogi's BigAtom enabled large-scale creative automation by:

• Generating background variations across 5,000+ SKUs,
• A/B testing different visual styles to identify what resonated best,
• Scaling winning variations automatically.

Dynamic overlays were also used to:

• Display pricing, offers, and sale messaging,
• Sync directly with website data, ensuring real-time accuracy.

This ensured that catalog creatives stayed fresh and relevant without manual effort.

The result:

• 7–10% increase in CTR
• ~19% improvement in catalog ROAS
• 16% reduction in cost per purchase

Offer Price ₹ 2,499 ₹ 4,999 — 5,041 Ratings

Product Segmentation for Category Diversification

To address category concentration, Pepe Jeans used AdYogi product segmentation to bring structure to how the catalog was promoted.

Instead of running all products uniformly, the catalog was segmented into product groups & product sets such as:

• High-performing SKUs ready for scale
• Emerging-category products (e.g., footwear)
• Sale-driven products for short-term demand spikes
• Low-performing or deprioritised SKUs
• Promotion of high margin products

This enabled:

• Focused scaling of emerging categories, increasing footwear contribution to 20% of total revenue, which became a key lever for growth
• Promotion of high margin products leading to reduced dependency on discount led sales
• Better control over new launches and merchandising priorities
• Structured campaign strategies across both BAU and sale periods

Segmentation was also used to plan offer-led campaigns, such as recurring "Buy One Get One" events — driving predictable spikes in demand while maintaining efficiency.

This approach led to stronger diversification of category revenue and more controlled, repeatable growth.

The Outcome

By combining product intelligence with AdYogi automation, Pepe Jeans transformed its performance marketing approach.

The brand was able to:

• Significantly increase catalog ads contribution to revenue
• Maintain stable and improved ROAS while scaling
• Reduce dependency on discount-driven sales
• Solve creative fatigue at scale
• Build a more diversified category revenue mix

More importantly, the shift to an AdYogi product-led system enabled continuous optimisation — where decisions were driven by real-time product performance rather than broad campaign signals.

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