Instacart, is an online grocery store that operates through an app. Instacart already has very good sales, but they want to uncover more information about their sales patterns.
The task is to perform an initial data and exploratory analysis of some of their data in order to derive insights and suggest strategies for better segmentation based on the provided criteria.
open source dataset provided by Instacart; The Instacart Online Grocery Shopping Dataset 2017
Customers Data Set provided by CareerFoundry for learning purposes.
● The sales team needs to know what the busiest days of the week and hours of the day are (i.e., the days and times with the most orders) in order to schedule ads at times when there are fewer orders.
● They also want to know whether there are particular times of the day when people spend the most money, as this might inform the type of products they advertise at these times.
● Instacart has a lot of products with different price tags. Marketing and sales want to use simpler price range groupings to help direct their efforts.
● Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
● 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 and 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? Consider the price of orders, the frequency of orders, the products customers are ordering, and anything else you can think of.