Background: Instacart, an online grocery company, is seeking to discover more about its sales patterns with the end goal of developing a targeted marketing strategy. To achieve these goals, the company requires a data analyst to get them started by deriving insights and suggesting marketing strategies based on initial data and exploratory analysis.
Context: This project was completed as part of the CareerFoundry's curriculum.
Topics covered were Python, data wrangling, data merging, deriving variables. grouping data, aggregating data, reporting in Excel, and population flows.
This knowledge was used to provide guidance on a marketing strategy for Instacart.
Note, while Instacart is a real company, the data set was fabricated for the purpose of this project.
Key questions include:
What are the busiest days of the week & hours of the day?
If there are particular times of the day do people spend the most money?
Creating simpler price range groupings for items; Are there certain types of products more popular than others?;
What’s the distribution among users in regards to their brand loyalty (i.e., how often do they return to Instacart)?
Are there differences in ordering habits based on a customer’s loyalty status?; Are there differences in ordering habits based on a customer’s region?
Is there a connection between age & family status in terms of ordering habits?; What different classifications does the demographic information suggest? Age? Income? Certain types of goods? Family status?
What differences can you find in ordering habits of different customer profiles?
Data sets: Note, due to file size restrictions, original datasets for this project could not be uploaded to GitHub.
The followind datasets should be added directly into the 'Original Data' folder.
customers dataset https://s3.amazonaws.com/coach-courses-us/public/courses/data-immersion/A4/A4_Data_Assets/customers.zip
departments dataset https://s3.amazonaws.com/coach-courses-us/public/courses/data-immersion/A4/A4_Data_Assets/4.4_departments.zipTools
orders_products_prior dataset https://s3.amazonaws.com/coach-courses-us/public/courses/data-immersion/A4/A4_Data_Assets/order_products__prior.zip
orders and products csv files https://s3.amazonaws.com/coach-courses-us/public/courses/data-immersion/A4/A4_Data_Assets/4.3_orders_products.zip
Tools used:
Python