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Competitor Data Analysis Tooling

Role

Senior Software Engineer | Aspiring Technical Program Manager

Company

Walmart Global Tech

Impact

Improved decision-making for marketplace vendors through real-time competitor data insights.

As a Senior Software Engineer, I managed the end-to-end execution of a competitor data analysis tooling initiative, aimed at equipping Walmart’s marketplace vendors with real-time insights to make data-driven pricing and inventory decisions. This project streamlined vendor decision-making and enhanced marketplace competitiveness.

Challenges & Problem Statement

Walmart’s pricing strategy faced challenges in dynamically adjusting to competitor pricing, demand fluctuations, and seasonal trends. The existing pricing mechanisms relied on static rules, limiting the ability to respond to:

  • Rapid market changes leading to missed revenue opportunities.

  • Inconsistent pricing strategies, causing merchant dissatisfaction.

  • Inaccurate demand forecasting, leading to overstocking or stockouts.

To address these inefficiencies and improve pricing agility, I initiated a predictive pricing research initiative, exploring AI-driven models to dynamically optimize pricing decisions.

Key Responsibilities & Execution

  1. Led Cross-Functional Research & Collaboration

    • Coordinated across Engineering, Data Science, Product Management, and Pricing Strategy teams to align research objectives with business goals.

    • Conducted comprehensive market benchmarking and literature reviews to gather insights on predictive pricing models used by leading e-commerce platforms.

    • Facilitated stakeholder workshops to understand current pricing challenges and gather qualitative feedback on potential predictive approaches.

  2. Designed & Developed Predictive Pricing Framework

    • Compiled a detailed research report outlining the potential benefits and risks of adopting predictive pricing models.

    • Delivered a series of presentations to key stakeholders, including the Pricing Strategy Team and senior management, to communicate research findings in clear, business-focused terms.

    • Recommended a phased approach to exploring predictive pricing, emphasizing the importance of further pilot studies and risk mitigation strategies before any development efforts are considered.

  3. Synthesized Findings & Strategic Recommendations

    • Presented research findings and actionable insights to Walmart’s Pricing Strategy Team, demonstrating how AI-driven pricing models could increase revenue predictability.

    • Developed a scalable framework for integrating predictive pricing insights into Walmart’s existing merchant pricing tools.

    • Proposed A/B testing and phased rollout strategies, ensuring minimal risk and maximum efficiency during implementation.

  4. Risk Management & Implementation Roadmap

    • Identified potential risks in dynamic pricing models, including data inconsistencies, over-discounting, and pricing volatility.

    • Recommended fail-safe mechanisms, such as price caps, demand fluctuation buffers, and automated anomaly detection systems.

    • Provided a strategic analysis on how predictive pricing could impact revenue, vendor satisfaction, and market competitiveness, offering both quantitative forecasts and qualitative insights.

    • Advised on the creation of a feedback loop to continuously refine the research model based on evolving market conditions and stakeholder input.

Key Achievements & Business Impact

  1. Developed an in-depth methodology to evaluate AI-driven pricing models through qualitative and quantitative analysis.

  2. Paves the way for pricing adjustments that could boost revenue by improving price accuracy and responsiveness.

  3. Positions Walmart to adapt more quickly to market changes, strengthening its competitive edge.

  4. Empowers strategic planning by shifting from static to data-driven pricing strategies, ultimately improving vendor satisfaction and market performance.

Lessons Learned & Takeaways

  1. In-depth market and data research can reveal hidden trends that inform strategic pricing decisions.

  2. Clear, concise presentations of complex research findings are essential to gain executive buy-in and set the stage for future initiatives.

  3. Identifying and understanding potential risks helps in crafting balanced, realistic recommendations.

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