Instacart Grocery Basket Analysis

Instacart is a popular online grocery delivery service in the U.S., operating through its app. The company's stakeholders are keen to better understand the diversity of their customer base and the purchasing behaviors within it. Realizing that a single marketing approach won’t effectively reach all customers, they are considering a more targeted strategy. Their aim is to tailor marketing campaigns to specific customer segments and evaluate how these campaigns impact sales.

Project background

The objective of this project was to perform an initial exploratory data analysis to derive insights and recommend strategies for segmenting customers based on various criteria. This analysis will help Instacart identify the right customer profiles and match them with relevant products. The key questions that guided this project include:

(Note: While Instacart is a real company with publicly available data, this project brief has been fabricated for the purpose of this case study.)

Techniques

Tools

Challenges

• Data wrangling & Subsetting

• Consistency checks

• Combining, Deriving

• Data Grouping and Aggregating

• Visualizations: created using Matplotlib and Seaborn

• Anaconda/Jupyter Notebook

• Pandas

• Excel

• GitHub

One of the biggest challenges was handling the dataset’s 30 million records, which required extensive cleaning, standardization, and formatting to prepare for analysis. Memory constraints were overcome through efficient coding practices to ensure the data could be meaningfully transformed and analyzed.

Dataset

Open-source data sets from Instacart

Dictionary

“The Instacart Online Grocery ShoppingDataset 2017”, Accessed from www.instacart.com/datasets/grocer y-shopping-2017 via Kaggle

Data Analysis

To conduct this customer segmentation analysis, the process involved the following key steps:

1. Data Cleaning and Merging: Multiple datasets were cleaned and merged using Pandas to ensure accuracy.

2. Trend Identification: Key trends were identified, such as peak shopping times and popular product categories. Outliers were removed to refine the data.

3. Actionable Insights: The analysis provided insights that can be leveraged to create targeted marketing campaigns.

Data Insights

1.Busiest Days and Hours:

• The busiest shopping days are Saturday and Sunday, with peak activity between 10 AM and 5 PM.

• The least busy days are Tuesday and Wednesday, with low activity during late-night hours (10 PM to 6 AM). This presents an opportunity to schedule promotions during quieter periods to stimulate activity.

The data analysis revealed several key insights that provide a deeper understanding of customer behaviors, shopping patterns, and product preferences. These insights will serve as the foundation for creating a more tailored and effective marketing strategy for Instacart. Below are the most significant findings:

2. High-Spending Times:

• Instacart customers are most active in the afternoon (12 PM - 6 PM) and morning (6 AM - 12 PM), with Saturday being the busiest shopping day.

• Friday ranks third in terms of activity, possibly indicating a restocking trend for the weekend. Produce, dairy, eggs, and snacks are the top items purchased during these busy periods, making them ideal for targeted marketing.

3. Price Segmentation:

• Products priced between $1 and $15 are the most frequently purchased, including produce, dairy, eggs, and snacks.

• Marketing efforts should focus on these lower-priced items since they drive the majority of customer attention. For higher-priced products (above $15), special offers could be introduced to boost interest.

4. Most Popular Products:

• Produce, dairy, eggs, snacks, beverages, and frozen foods are the top five most ordered categories.

• Less frequently ordered categories include pet items, international products, baby items, and personal care products. Instacart could consider incentivizing purchases of these underperforming items through discounts or promotions.

5. Customer Loyalty:

• 51.3% of customers are classified as "Regular Customers" (with 10-40 orders), while 33.2% are "Loyal Customers" (with over 40 orders), and 15.5% are "New Customers" (fewer than 10 orders).

• While most customers are regular shoppers, they tend to spend less than $10 per transaction. To increase brand loyalty, Instacart could implement rewards programs that encourage customers to place more frequent and higher-value orders.

6. Regional Ordering Habits:

• There are no significant differences in ordering habits across regions, but the Southern region has the largest customer base, making it a key target for growth.

• The West and Midwest regions should also be prioritized for expansion, as they have similar patterns of behavior.

7. Age and Family Status Impact:

• The majority of Instacart customers are middle-aged (30-60) or seniors (over 60), married, and have dependents. Despite this, family-related items are not top sellers.

• Tailored marketing campaigns could target families with dependents and promote bulk purchases, while other campaigns might focus on customers with specific dietary needs.

8. Differences in Customer Profiles:

• Customers across age groups tend to purchase similar items, with produce, dairy, eggs, and snacks being the most popular.

• High-priced items such as meat and seafood are predominantly purchased by mid- to high-income customers. Given that most customers are low spenders, targeted campaigns for high-priced items could help drive more diverse purchases.

Conclusions and Recommendations

1. Target Ads During Off-Peak Times: Schedule advertisements during less busy times, such as weekday evenings, to increase engagement when activity is lower.

2. Leverage Social Media: Use platforms like Instagram and Facebook to introduce new products and engage younger customers.

3. Personalize Promotions: Customize promotions based on customer demographics, such as family status and purchasing history, to increase relevance and engagement.

4. Implement Loyalty Programs: Encourage regular customers to become loyal customers by offering rewards for repeat purchases and higher-value transactions.

5. Promote Underperforming Products: Analyze and promote underperforming product categories, such as personal care and baby items, by offering special deals or surveying customers to better understand their preferences.

By leveraging these insights, Instacart can develop a more effective marketing strategy tailored to the unique behaviors and needs of its diverse customer base.

Based on the insights derived from the data analysis, several actionable conclusions and recommendations have been identified. These strategies aim to help Instacart optimize its marketing efforts, increase customer loyalty, and improve overall sales performance by targeting key customer segments more effectively. The following recommendations provide a clear path for driving growth and enhancing customer engagement.