INSTACART DATA ANALYSIS
OVERVIEW
Instacart is an online grocery store that provides delivery and pick-up service within the United States and Canada.
PURPOSE
Analysis of Instacart data was done for CareerFoundry Data Analytics Course to practice Python and Jupyter Notebook.
OBJECTIVE
Derive meaningful insights and suggest strategies to improve Instacart sales.
Analysis Summary
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Project took few hours of work per week for 1 month.
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Customer Data Set
Products Data Set
Department Data Set
Orders Data Set
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Excel, Python, Jupyter Notebook
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Data cleaning, data wrangling, subsetting, data consistency check, combining data, deriving variables, grouping and aggregating data, creating visualization
Analytical Approach
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Cleaned original data using Python to remove any duplicates or missing values.
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Merged datasets using excel to create one single dataset for more detailed analysis.
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Created flags for customers to identify them based on their characteristics using Python.
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Find basic statistics of the dataset to understand the data in detail.
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Created charts with Google Looker to visualize the data.
The graph illustrates that most orders were placed on Saturdays and Sundays. Wednesdays have the least number of orders.
RESULTS
Customers are categorized based on their order history: those with over 40 orders are loyal, less than 10 are new, and between these ranges are regular. The graph shows a higher number of regular customers than loyal or new ones.
Customers were grouped according to their geographical regions. The chart demonstrates that the Southern region has the highest number of customers, while the Northeastern region has the lowest.
Conclusion & Recommendations
Orders are placed mostly on Weekends and least on Wednesdays. Therefore, sales are recommended on weekends to increase the number of purchases and promotions should be made on Wednesdays, so more customers engage.
There is a significantly higher number of regular customers compared to new customers.
South regions have the highest number of customers and Northeast the least. Increasing advertisements in the northeast region is recommended.
Departments with the least amount of sales need to be looked into to see if the net profit is worth keeping the products in sales.