Project case study
Reusable retail analytics product for cross-shopping, category adjacency, and store-flow decisions.
Helped retail teams move from isolated category views to placement and assortment decisions associated with up to 20% sales-growth impact.
Context
Retail stakeholders needed a repeatable way to understand how customers moved across departments and categories, then convert that signal into placement, adjacency, and assortment decisions.
The work covered customer priority assortment, cross-shopping behavior, category adjacency, and store-flow style outputs so planning teams could reason about placement, assortment, and category optimization across retail contexts.
System trace
A high-level operating path: where the request starts, how the system shapes it, and how other teams consume the result.
Spark and Python prepared large retail transaction datasets for repeatable cross-shopping analysis.
Reusable data preparation separated raw transaction work from analytical segmentation.
Aisle-flow and store-flow style outputs supported placement, adjacency, and category optimization conversations.
Analysis scope
Cross-category
Modeled relationships across departments, categories, and products rather than a single isolated product view.
Commercial signal
Up to 20% sales growth
Retail optimization work was associated with up to 20% sales growth through better placement and category decisions.
Reuse pattern
Segmented
Designed the analysis so multiple customer segmentation approaches could reuse the same prepared data foundations.
Architecture
Ownership
Lessons
Engineering decisions
The work was structured so segmentation methods could evolve without rebuilding the base transaction preparation each time.
The final shape prioritized comparison and exploration so retail users could reason about cross-shopping behavior directly.
What can be shown
The internal systems stay private. This section keeps the public parts: my role, system boundaries, technology context, scale, decisions, constraints, and what I learned.
Analysis scope
Cross-category
This case study shares the department, category, and product relationship pattern, not client data.
Business impact
Up to 20% sales growth
Retail optimization work was associated with up to 20% sales growth through placement and category decisions.
Supporting context
High-level architecture
Can be shown as transaction preparation, segmentation variants, relationship outputs, and BI consumption.
Scale signal
The case study can state cross-category and segmented reuse patterns without publishing client metrics.
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