At a Glance
- Client: BodyGlove – Leading Apparel Brand (Hellcat, Bullmer, Wexford)
- Scale: 2,00,000+ Orders Monthly
- Channels: Amazon, Flipkart, Shopify, Myntra, Ajio, JioMart, & Quick Commerce.
- Impact: ₹30 Lakhs saved in 3 months | 1.5% Net Margin Improvement.
The Customer: Scaling Complexity
BodyGlove is a powerhouse in the Indian apparel sector, managing a massive catalog across 8+ sales channels. For a brand doing 2 Lakh orders a month, complexity is the enemy.
In the apparel business, a single product (e.g., "Men’s Running Tee") isn't just one SKU—it’s a cluster of 20+ variants (Sizes S-XXL, multiple colors).
The Challenge: The "Black Box" of Ad Spend
Despite strong top-line growth, BodyGlove's finance and marketing teams were struggling with a critical blind spot: Ad Spend Attribution.
They were spending significantly on Meta, Amazon Ads, and Flipkart Ads. While their team reported a healthy "Campaign Level ROAS," the unit economics told a different story.
- The “Hero vs. Zero” Trap: A campaign for a specific t-shirt line looked profitable, but in reality, only the “Black – Size M” was selling. The ad spend was being wasted on “Red – Size XXL” which had zero conversion, but the campaign-level data hid this inefficiency.
- Channel Margin Blindness: They were applying the same ad budget strategy to Shopify (low fees) as they were to Myntra and Amazon (high commission/logistics fees), eroding profitability on marketplaces.
They needed to move from "Campaign Guesswork" to "SKU-Level Precision."
The Solution: ForceSight’s Technical Attribution
BodyGlove deployed ForceSight to act as their central financial nervous system.
1. The Allocation Engine (Solving the Variant Issue) ForceSight’s proprietary Allocation Model allowed BodyGlove to map ad spend down to the Parent SKU level. Instead of dumping costs arbitrarily, the system intelligently distributed "Brand Awareness" and "Category" spend based on Net Revenue and Gross Quantities.
- Result: The team finally saw the true Cost Per Acquisition (CPA) for every specific style, not just the campaign average.
2. Profit-Contextualized Guardrails BodyGlove utilized ForceSight’s Rule-Based Engine to set channel-specific profitability targets.
- Rule A (Shopify): Max Ad Spend cap set at 25% of Sales (aggressive growth).
- Rule B (Amazon/Myntra): Max Ad Spend cap set at 10% of Sales (margin protection due to high commissions).
Daily Impact: How BodyGlove Uses ForceSight
The ForceSight dashboard has replaced the complex Excel trackers previously used by BodyGlove's marketing and finance teams.
- 9:30 AM – The Breach Check: The team starts the day by reviewing ForceSight’s “Breach Notifications.” The system automatically flags any SKU or Category where ad spend has exceeded the pre-set margin rules (e.g., if Amazon Ad Spend hits 12% on a low-margin category).
- 11:00 AM – Surgical Optimization: Instead of pausing an entire campaign, the marketing team identifies the specific “Zero” SKUs dragging down profitability. They cut spend on the bleeding variants while doubling down on the “Heroes.”
- Weekly Review – The Single Source of Truth: Finance and Marketing meet using one dashboard that shows Ad Spend alongside COGS, Returns, and Commissions. No more arguments about which data is correct.
The Results
In just 90 days of using ForceSight, BodyGlove transformed their efficiency:
- ₹30 Lakhs Saved: By identifying and cutting ad spend on non-performing SKUs and variants that were “hiding” inside successful campaigns.
- 1.5% Margin Expansion: By reallocating that saved budget to high-margin channels and “Hero” products, they didn’t just save money—they made their existing revenue more profitable.
