Case Studies
Transactional
B2C
Fashion
High Ticket
Amazon

Finding Efficiency to Expand Digital Investment for Omni-Channel Retailer: Increase Variable Contribution Margin by $14 Million in first 9 Months

David Manela
March 12, 2025

Problem Overview

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

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)

Business Problem

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.

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.

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:

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.

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.

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.

Key Results & Outcomes

Significant YoY Growth

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