This project provides a comprehensive analysis of Instacart's sales data, uncovering critical customer purchasing patterns to guide targeted marketing strategies. By addressing key questions posed by the sales and marketing teams, this analysis aims to deliver actionable insights that enhance engagement, drive sales, and improve the relevance of promotions.
- Identify the busiest days and hours for customer orders.
- Understand customer spending patterns and peak spending times.
- Simplify product price ranges for more effective marketing strategies.
- Analyze product popularity across departments.
- Examine customer segmentation by loyalty, region, age, income, and family status.
- Develop data-driven recommendations for tailored marketing campaigns.
01 Project Management
Contains the project brief and supporting documentation.
02 Data
Original Data: Raw datasets for the analysis.Prepared Data: Cleaned and processed datasets ready for exploratory and statistical analysis.
03 Scripts
Python scripts executed in Jupyter notebooks to perform data manipulation, analysis, and visualization.
04 Analysis
Includes a Visualizations subfolder featuring graphical insights and visual representations of key findings.
05 Sent to Client
Final Excel presentation summarizing the results and recommendations.
- What are the busiest days and hours for customer orders?
- At what times do customers spend the most money?
- How can Instacart's pricing be grouped to aid in marketing?
- Which product categories are most popular?
- How is brand loyalty distributed among users?
- Do ordering habits differ by loyalty status, region, or demographics (age, income, family status)?
- What classifications can be derived from demographic data?
- How do customer profiles influence ordering habits?
- Busiest Days & Hours: Analysis reveals peak order days and times to optimize staffing and marketing campaigns.
- Peak Spending Times: Insights into high-spending hours to target promotional efforts.
- Popular Categories: Certain departments outperform others, highlighting trends in grocery shopping behavior.
- Age and Family Status: Key connections inform tailored product recommendations.
- Regional Differences: Variations in order volume and preferences guide localized campaigns.
- Loyalty distribution enables differentiated strategies for retaining high-loyalty customers and converting new ones.
- Python Libraries:
- Pandas: Data manipulation and cleaning.
- NumPy: Numerical computations.
- Matplotlib & Seaborn: Visualization.
- Scipy: Advanced statistical computations.
- Charts and graphs displaying customer trends and segmentations.
- Heatmaps of busiest order periods.
- Demographic-based customer classification for targeted marketing.