Sales Data Analysis: Store Performance and Customer Insights

This analysis focuses on understanding sales performance across various customer segments and stores. We aim to uncover key insights into brand preferences among specific customer groups and compare sales performance between trial and control stores to assess the impact of store interventions.

Data Collection

Three datasets are used in this analysis:

• Transaction Data: Details on individual transactions, such as product name, quantity, total sales, and transaction date.
• Customer Data: Contains customer demographic details like Lifestage and whether they are considered premium customers.
• Sales Data: Aggregated sales and customer metrics at the store level.

These datasets were loaded using the fread() function and combined for further analysis.

Data Preprocessing

Several steps were performed to clean and process the data:

• Date Formatting: Transaction and sales data included date columns that were converted to proper date formats.
• Removing Non-Chip Products: Transactions involving non-chip products like Salsa were filtered out.
• Feature Extraction: Pack size and brand were extracted from product names, and brand names were standardized.

Exploratory Data Analysis (EDA)

Sales by Lifestage: We grouped the data by Lifestage and summed total sales for each group. The "Young Singles/Couples" group contributed significantly to total sales.

Brand Preferences: The top brands for the "Mainstream Young Singles/Couples" group were identified, revealing key market preferences.

Trial Store vs Control Store Comparison

Pre-Trial Data Analysis: We calculated key metrics like total sales and the number of customers for each store on a monthly basis. Only stores with complete observations were included in the analysis.

Scaling and Sales Comparison: A scaling factor was calculated to align the control store's pre-trial sales with those of the trial store, and a comparison was made during the trial period.

Key Findings

• Sales Distribution by Lifestage: "Young Singles/Couples" contributed significantly to total sales.
• Brand Preferences: The top brands for this segment were identified, providing actionable insights for marketing strategies.
• Trial Store Performance: Sales performance improved in the trial store relative to the control store during the trial period.

Conclusion

This analysis highlights the importance of customer segmentation to understand sales trends. Comparing trial and control stores offers insights into the effectiveness of store interventions. Further analyses could explore customer retention strategies and brand loyalty trends.