R-Powered Strategic Retail Intelligence: From Performance Audit to Market R&D
Navigating complex data landscapes—across both the private sector and classified environments within the DoD—has reinforced the reality that analytical results are only as valuable as the transparency behind them. In high-stakes settings, the “black box” is a liability; every number must be defensible, and every methodology must be auditable.
This suite was born from my experience as a Product Manager identifying operational bottlenecks in the sales cycle. By applying a Master’s-level statistical foundation to the real-world friction I encountered in the field, I have engineered this project as a gold standard for Literate Programming. It transitions from a high-level operational audit to deep-dive market optimization, ensuring that strategic recommendations are always backed by a visible, reproducible technical trail.
I. Executive Sales Performance Dashboard
The Operational Source of Truth
This primary module serves as the central hub for the analytical framework, providing a real-time audit of revenue health across geographic markets and product categories.
Strategic Focus: Enables stakeholders to rapidly evaluate revenue concentration and monitor “health and wealth” metrics to optimize resource allocation.
Technical Highlight: Features a reactive backend that facilitates seamless drilling from quarterly trends down to weekly localized performance.
The Hub: Contains direct navigational links to the specialized R&D and Behavioral Intelligence reports below.
View Interactive Sales Dashboard
II. Strategic R&D: Algorithmic Gap Analysis
Portfolio Optimization & Market Benchmarking
Addressing the “Market Entry” problem, this module identifies “blind spots” in the current 97-model fleet to establish high-fidelity pricing benchmarks for new configurations.
Strategic Focus: Discovered two distinct “Blue Ocean” opportunities: an Aluminum-frame Over Mountain line and an Aluminum-frame Triathlon line.
Technical Highlight: Leverages an XGBoost pricing engine to establish market benchmarks, ensuring internal price parity before a prototype is built.
Validation: Compares XGBoost against Linear and Random Forest architectures to ensure the most robust predictive accuracy.
III. Behavioral Intelligence: High-Dimensional Market Mapping
Latent Market Discovery via Clustering
To drive targeted marketing and inventory strategies, this final module uncovers the latent behavioral patterns within a customer base of 30 retail bike shops.
Strategic Impact: Identified four distinct customer profiles, allowing marketing teams to transition from generic outreach to highly targeted, segment-specific campaigns.
Technical Methodology: Employs K-Means clustering and UMAP (Uniform Manifold Approximation and Projection) to project high-dimensional similarity into an intuitive 2D space.
The “Practitioner” Difference: Unlike traditional PCA, the use of UMAP preserves the local structure of the data, providing a more accurate visual representation of distinct customer “landscapes.”
View Customer Segmentation Audit
Methodological Note: Built for Auditability
Every report in this suite features interactive code-folding. By prioritizing transparency, these documents allow technical peers to inspect the data transformations and model parameters directly inline. This ensures that the methodology is not a “black box,” but a fully auditable asset—a requirement I maintain from my background in regulated and classified environments.
