Instacart Customer Segmentation & Sales Trends

To support a targeted marketing initiative, I analyzed Instacart transaction data to uncover customer behavior patterns and provide actionable insights for the marketing and sales teams.

Using Python in Jupyter Notebooks, I cleaned and merged large datasets, derived new variables, and grouped and aggregated the data to address key questions:
• What are the peak shopping days and hours?
• Which product categories are most popular?
• How can price ranges be simplified to support marketing messaging?
• What distinct customer types exist, and how do their behaviors differ?

I developed summary reports in Excel highlighting key trends and customer segments, and offered data-driven recommendations to inform campaign design, product positioning, and promotional timing.

Skills used: Python scripting, data wrangling, feature engineering, customer segmentation, Excel reporting
Tools: Jupyter Notebooks, Microsoft Excel