Company Profile

  • Industry & Footprint: Leading fashion brand operating across multiple locations in the U.S.
  • Business Type: B2C (Business-to-Consumer)
  • Engagement Channels: Retail locations, Email, SMS, Phone, and Online
  • Transaction Size: $50 to $500 per transaction
  • Geographical Scope: Specific locations in the U.S. and key international cities

Business Problem: 

  1. Need for Real-Time Performance Visibility
    • Struggled to track performance across all platforms, channels, and campaigns in real-time (LC + MTA).
    • Required optimization down to the fully loaded margin (FLM) at the campaign level.
  2. Segmentation of New vs. Existing Buyers
    • Needed separate planning and reporting to accurately map activity drivers for each group.
    • Saw an opportunity to increase activity among existing buyers and better target new customers.
  3. Inefficient Budget Allocation
    • Prior-year investment mix was unclear; team needed to determine optimal daily spend per channel.
    • Sought to balance spending efficiency with key brand objectives (e.g., Women’s Lifestyle).
    • How to decide investment mix between D2C channels and other channels (like Amazon)
  4. Complexity of Measurement & Reporting
    • Required Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM) to accurately tie outcomes to investments.
    • Operational issues (e.g., QA concerns, brand mishaps) lowered confidence in data and performance.

Deployed Solutions

  1. Comprehensive Data Platform
    • Real-Time Tracking: Implemented real-time monitoring for all platforms, channels, and campaigns (LC + MTA).
    • FLM Optimization: Enabled day-to-day adjustments in spending based on fully loaded margin data.
  2. Differentiated Strategy for New vs. Existing Buyers
    • Activity Mapping: Identified unique conversion and engagement drivers for new vs. existing customers.
    • Improved Engagement: Focused on boosting activity among existing customers while scaling new customer acquisition.
  3. Marketing Mix Modeling (MMM) & Multi-Touch Attribution (MTA)
    • First-Ever MMM: Conducted 3,000 model iterations with 94% accuracy, incorporating category-level data.
    • Live MTA Model: Provided more precise budget allocation insights, especially for top-of-funnel investments.

Key Results & Outcomes

Significant YoY Growth

$13.9M YoY increase in Fully Loaded Variable Contribution Margin over 9 months.