How House of Indya Drove a
200% Sales Surge in 3 Months
Scaling fusion wear performance with automated Stop-Loss, Dynamic Product Sets, and Quadrant Analysis — from manual updates to a fully product-led system.
Managing 90K+ SKUs with no smart product targeting
House of Indya needed to reduce wasted media spend and improve product targeting while scaling its large fusion wear catalog efficiently.
House of Indya is a contemporary Indian fusion wear brand with 90,000+ products spanning ethnic and modern styles.
With a large and growing catalog, the brand needed to move beyond manual product updates and random promotion toward an automated, product-led performance system to scale efficiently on Meta and Google.
Inefficient Spend on Low-Converting Products
High-budget SKUs were consuming a large share of ad spend without generating proportional revenue.
Lack of Smart Product Targeting
Campaigns were promoting random products instead of focusing on proven bestsellers or high-potential items.
Missed Opportunities with New Arrivals
New collections with stronger ROAS and better margins were not being dynamically prioritised in campaigns.
Learning Phase Disruptions
Frequent manual SKU updates pushed Meta and Google campaigns back into the learning phase, slowing optimisation and reducing performance stability.
Product Performance Management
BigAtom’s Quadrant Analysis segmented the entire 90K+ catalog into four performance-based groups — each with a clear automated strategy for scaling, pausing, or discovering.
High Potential Products
New arrivals and hidden gems with strong conversion rates but insufficient budget — the next wave of top performers waiting to be unlocked.
Hero Products
Proven top-performing SKUs delivering strong returns. These were prioritised with dedicated campaigns and protected from budget dilution.
Low Discoverability
Products with limited exposure needing visibility testing before a confident scaling or pausing decision could be made.
Non-Performers
SKUs consuming budget without profitable returns. Targeted for automated Stop-Loss pausing to recover and redeploy wasted spend.
Three targeted strategies
Stop-Loss Automation, Dynamic Product Sets, and Quadrant Analysis combined to build a stable, self-optimising catalog performance engine.
Reduced Wasted Spend with Stop-Loss Automation
BigAtom implemented automated ROAS and spend-based rules to pause underperforming SKUs daily — ensuring budget was not repeatedly spent on products failing to convert, and redirecting spend toward Hero Products.
Improved Targeting with Dynamic Product Sets
BigAtom used New Arrivals Segmentation to dynamically identify and prioritise fresh SKUs based on real-time product age — ensuring the most relevant, high-converting products were consistently featured in campaigns.
Scaled Efficiently with Quadrant Analysis
Auto-refreshing product sets continuously showcased top performers without repeatedly resetting the ad platform’s learning phase — maintaining campaign stability while keeping promoted products fresh and performance-led.
Product-led performance metrics
Shifting to automated SKU-level decisions delivered compounding gains across sales, efficiency, and return on ad spend.
