Sam’s Club: New Data Sets and AI-Powered Insights

This was a critical 0-to-1 project to build a brand new, high-impact feature within Walmart's Intelligent Business Growth (IBG) platform. IBG is the enterprise's next-generation platform designed for connected business planning, providing a unified view of customer demand and a model for operational impacts.

My specific goal was to design and launch a new core feature within IBG that would automatically calculate shipping expenses and generate accurate forecasts. This directly eliminated data fragmentation and empowered senior Sam's Club leadership with the timely, high-quality data needed for critical strategic planning, offering consistent exposure to senior leaders across Walmart and Sam’s Club finance and business units.

  • Key Contributions

    Role: Lead Product Designer

    ✓ I transformed a highly fragmented, manual process into a centralized, efficient forecasting platform.

    ✓ I established a single source of truth by integrating scattered shipping expense data from multiple legacy systems.

    ✓ I eliminated tedious data manipulation, allowing Merch and Finance teams to shift focus from data gathering to high-value strategic analysis.

  • Problem

    The Sam's Club Merchandising and Finance teams faced a critical business problem:

    They were forced to spend an excessive amount of time manually moving shipping expense data across multiple disparate legacy systems, including Excel and various BI tools. This extreme data fragmentation and inefficient, manual process directly resulted in inaccurate, untimely financial forecasts, significantly hindering senior leadership's ability to make agile, strategic business decisions.

The Solution

The core of the new expense forecasting tool is a Driver Tree Interface that I designed. The design revolves around a driver tree structure with nodes, parents, and children to visually represent the hierarchy and interdependencies of our expenses.

Custom Components for Complex Data

I created custom-built hierarchical input components for real-time financial scenario modeling. The new driver tree features collapsible drill-down structure with live calculation updates.

Impact

  • 75%

    Decrease: Online Completion Rate

    Over 2000 hours saved in manual data gathering time.

  • 15

    Core Variables

    Integrated 15+ core shipping variables into the forecasting model.

  • 100%

    Target Adoption

    100% target adoption achieved within the core Finance and Merchandising teams.