top of page

Instacart

Instacart, a leading online grocery store, enables customers to order groceries through an app.

Objective

Conduct a detailed analysis of Instacart’s sales data to enhance their marketing strategy, focusing on more precise customer targeting and effective segmentation to drive sales growth.

Data
Skills

  • Data cleaning, wrangling, subsetting, and merging

  • Data consistency checks

  • Deriving new variables

  • Grouping and aggregating data with Python

  • Visualization with Python

  • Creating population flow and reporting in Excel

Tools

  • Microsoft Excel

  • Anaconda

  • Jupyter Notebook

  • Python

  • Python libraries Pandas, NumPy, Seaborn, Matplotlib, and SciPy

Customer Ordering Pattern

The graph suggests that the prices of orders may fluctuate significantly throughout the day. There's a noticeable dip in prices around the 6-hour mark and a peak around the 14-hour mark

instacart 1.png

Price Distribution

instacart 2.png

The high frequency of orders in the 5-10 dollar range suggests that a significant portion of the customer base is price-sensitive and gravitates towards products within this price range.

Top Sales

instacart 3.png

The chart provides insights into customer preferences and shopping habits on the Instacart platform. Customers seem to frequently purchase fresh produce, dairy products, and snacks.

Sales Distribution by Department Category

instacart 4.png

Fresh foods account for the largest share of sales (55.2%), indicating a strong preference for fresh produce among Instacart customers, followed by packaged foods(22.8%) of sales.

Customer Profile

The pie chart shows that 51.3% of customers are 'Regular', indicating repeat purchases. While 'Loyal' customers comprise 33.2%, 'New Customers' account for 15.5%, highlighting the importance of ongoing acquisition efforts.

instacart 5_edited.png

Customer Distribution by Loyalty status

instacart 6.png

There is a general trend of increasing income with age, the relationship is not perfectly linear. There's a significant spread of income values within each age group.

Conclusions & Recommendations

Targeted Marketing

Leverage the observed income distribution to target specific income segments with tailored marketing campaigns.

Product & Service Offerings

Offer a variety of products and services to cater to the diverse needs and preferences of customers across different income levels.

Customer Segmentation

Analyze the data to identify distinct customer segments based on age, income, and other relevant factors. This will enable more personalized marketing and customer service.

Ordering Times

The busiest times are weekends from 10AM-4PM. To increase traffic during slow times, ads should be run on the weekdays before 10AM and after 4PM..

bottom of page