Background:

You’re an analyst for an existing company, Instacart, an online grocery store that operates through an app.

Goal:

Your task is to perform an exploratory data analysis of some of their data in order to derive insights and suggest marketing strategies.

Key business questions:

  • Are there certain types of products that are more popular than others?

  • What is the distribution among users in regards to their brand loyalty?

  • Are there differences in ordering habits based on a customer’s loyalty status? Region?

Data:

Public data “The Instacart Online Grocery Shopping Dataset 2017” and fictional customer data.

Tools/Skills:

  • Data wrangling

  • Data merging

  • Deriving variables

  • Grouping data

  • Aggregating data

  • Reporting in Excel

  • Population flows

  • Python

Findings:

Users will typically return the same day, between 5 and 10 days later, or 30+ days later. This could indicate a difference in shopping habits. The users returning right away may purchase items as needed, compared to the users who return in 5 to 10 days who are likely shopping for an entire week of groceries at once.

When referencing our data dictionary below, we can identify department 4, produce, as the most popular type of product by far. Followed by dairy and eggs, then snacks.

Regardless of what region the customer is located in, the most popular days of the week to shop remain the weekend.

Recommendations:

  • Keep produce and dairy & eggs departments stocked as they are the most popular among users.

  • Send out promotional material same day as an order is placed or between 5 and 10 days after an order is placed as these are the times users are most likely to re-order. After 15 days the likelihood goes down.

  • Expect most customers to use the app on weekends, across the country.