Ryan Weber

Instacart Sales Analysis: Uncovering Customer Insights for Strategic Segmentation

About

Instacart, an app-based online grocery store, already has very good sales, but it wants to uncover more information about its sales patterns. Instacart would like to perform an initial data and exploratory analysis of some of its data to derive insights and suggest strategies for better segmentation based on the provided criteria.

DATA

  • Instacart data regarding the following
    • Customers
    • Products
    • Orders
    • Departments

Goals

  • Provide insight into customer base using data collected via the Instacart app.

Skills

  • Data Wrangling
  • Data Merging
  • Deriving Variables
  • Grouping Data
  • Aggregating Data
  • Code Samples on GitHub

Tools

  • Python
  • Excel

Data Wrangling and Cleaning

All the data sets needed for this analysis were cleaned and combined using Python.

Consistency Check – Mixed data types

Deriving Variables – Here, we took the many different prices of products and simplified them into 3 price groups.

Key Questions

  1. What are the busiest days? Busiest hour of day?
  2. What is the time of day where most money is spent?
  3. What are the product’s price groupings?
  4. What are the most popular products?
  5. What is the distribution of customers based on loyalty?
  6. What are the ordering habits based on loyalty & region
  7. What is the ordering behavior based on age?
  8. What is the ordering behavior based on family status & income

Results

Instacart’s busiest days are Friday through Sunday and the busiest hours are usually between 9 A.M. and 5 P.M.

Instacart’s largest customer group is its regular customers. Those are customers defined as having placed more than 40 orders. Regular customers are those having placed between 10 and 40 orders, and New customers are those who have placed fewer than 10 orders

Recommendations

  • The south has a higher purchase rate than the other regions.  Continue to provide popular products in that region while updating popular offerings in the poorer performing regions.
  • Regular customers purchase more than other loyalty groups.  Targeting new customers with special discounts could encourage them to become regular customers.
  • The busiest days of the week are Friday through Sunday, with peak hours being 9-5. Advertising during the off-peak times or offering off-peak deals could increase business during the slower times

Deliverable

GitHub Repository

Final Report