This repository contains my analysis and strategic recommendations for a retail chip category review. The project involved processing large datasets to identify high-value customer segments and evaluating the success of a store layout trial.
Project Structure /data: Includes QVI_Data.csv.zip (compressed transaction and customer dataset) and supporting data files.
Task_1.ipynb: Data cleaning, validation, and customer segmentation analysis.
Task_2.ipynb: Experimentation, control-store matching, and uplift testing.
report.pdf: Final presentation and executive summary.
Key Insights and Results Customer Segmentation
Analysis of transaction data identified "Mainstream" shoppers—specifically Young Singles/Couples, Retirees, and Older Singles—as the primary drivers of category revenue. These segments showed a clear preference for premium brands and 175g "Party Size" packs.
Trial Performance
I evaluated a three-month shelf layout trial in Stores 77, 86, and 88 by comparing them against matched control stores. The results validated a 20.2% increase in unique customer traffic during the trial period. Statistical testing confirmed that this uplift was significant and not due to random variance.
Strategic Recommendation
Based on the validated sales and traffic growth, the final recommendation is a national rollout of the new shelf layout. This strategy prioritizes high-margin premium brands (Kettle and Smiths) at eye-level to align with the shopping habits of the most profitable customer segments.
Technical Stack: language: Python libraries: Pandas and Numpy(for data manipulation), Matplotlib and Seabor(for data visualization), Scipy(for statistical testing) reporting: PowerPoint