Workflow Overview:

1. Data Organization and Integrity Checks:

   • Initial datasets included orders, products, and customer information.

   • Integrity checks were conducted to address missing values, duplicates, and inconsistent data types​

2. Data Wrangling and Variable Derivation:

   • Key variables were derived, such as order frequency, product price range, and customer loyalty status.

   • Data was merged into a unified dataset, facilitating in-depth analysis​.

3. Exploratory Data Analysis and Visualization:

   • Data was grouped by day of the week, time of day, and product category to explore customer behavior and sales patterns.

   • Six key visuals were created to summarize the findings.

Key Visuals and Insights:

1. Busiest Days for Orders

• Insight: Saturday and Sunday emerged as the busiest days for orders, while Tuesday and Wednesday had the lowest order volumes.

• Recommendation: Launch targeted ad campaigns on Tuesdays and Wednesdays to increase sales on less busy days​.

2. Order Volume by Time of Day

• Insight: Most orders were placed between 9 AM and 6 PM, with a sharp drop-off after 6 PM.

• Recommendation: Run promotions between 5 PM and 12 AM to counteract the decline in orders, especially during weekdays​.

3. Average Order Value by Day

• Insight: Customers tend to spend more on orders placed on Saturdays and Fridays, with a slight dip during the weekdays.

• Recommendation: Emphasize promotions on high-priced items (e.g., alcohol, premium food) on weekends to capitalize on increased spending behavior​.

4. Price Range Distribution

• Insight: Mid-range products (priced between $5.01 and $15) accounted for a majority of purchases (67.5%), followed by low-range products.

• Recommendation: Focus marketing efforts on promoting mid-range products, while also highlighting high and low-range options for variety​.

5. Top Performing Departments

• Insight: The Produce department dominated sales, contributing to 41.8% of total orders, followed by dairy, snacks, and beverages.

• Recommendation: Prioritize marketing campaigns for produce and other top-performing departments, while considering discontinuation of underperforming products​.

6. Customer Loyalty Analysis

• Insight: Loyal customers with a median time between orders of 10 days or less made up a solid 33.2% of the customer base. Most customers placed 2-3 orders per month.

• Recommendation: Introduce a loyalty program offering incentives for customers who place 4+ orders per month. This will increase order frequency and customer retention​.

Conclusion & Strategic Recommendations:

This analysis of Instacart’s sales and marketing data reveals key opportunities to enhance customer engagement, optimize ad spend, and drive revenue growth. Key recommendations include:

1. Optimizing Ad Scheduling:
   Target slower periods (Tuesdays, Wednesdays, and after 6 PM) to boost sales with promotional offers.

2. Promoting High-Value Products:
   Focus on premium product categories and incentivize purchases on weekends when customers tend to spend more.

3. Loyalty Program Development:
   Build a loyalty program that rewards frequent orders, encouraging customers to shop more often and driving long-term customer retention.

4. Lack of Regional Differences in Ordering Habits:

   No significant differences were observed in the ordering habits across the four regions (South, Midwest, West, and Northeast). Customer behavior appears consistent, indicating that promotions and marketing strategies could be applied uniformly across regions.

5. Customer Demographics and Income:

   Higher incomes are generally concentrated in middle-aged (41-60 years) and senior (60+) age groups. Marketing efforts should be directed toward these groups to encourage higher-value purchases.

These insights enable data-driven decision-making for Instacart, providing a roadmap for future marketing and operational improvements.