Instacart is an online grocery platform operating in the US and Canada. While the marketing department aims to enhance ad targeting, the sales department seeks to increase category sales.

To support these goals, data analysis using Python was conducted to develop a targeted marketing strategy. The focus was on exploratory analysis, customer profiling, and segmentation to ensure Instacart reaches the right customer profiles with suitable products.

Industry: E-Commerce, Food & Non-Food

Technologies used:

• Programming Language: Python
• Libraries:
  • Pandas, NumPy, and SciPy for data analysis
  • Seaborn and Matplotlib for data visualization

The dataset is a relational set of files describing customers' orders from 2017. The dataset is anonymized and contains a sample of over 3 million grocery orders from more than 200,000 Instacart users.

1. 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.
2. 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.
3. 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.
4. 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.
5. How does order behaviour differ for various customer segments based on customer's loyalty status, region, and demographics?

• Descriptive exploratory analysis, analysis of variable frequencies, use of data dictionary.
• Data wrangling & subsetting (change data types, rename columns, create new columns using conditional logic in form of if-stateemnts, user-defined function, loc(), and for-loops).
• Data consistency checks (deal with missing values, remove uplicates).
• Merging customer data with other datasets.
• Usage of a data dictionary.
• Grouping, aggregating data: create flags for customer profiling, summary columns of descriptive statistics using groupby().
• Analysis of order behaviour of different customer groups.
• Data visualisation in Python (histograms, bar charts, line charts, scatterplots).

The project files are structured as follows:

01 Project Management - includes a project brief.
02 Scripts - includes Jupyter notebooks.
03 Analysis - includes visualisations generated for the exercises.
04 Sent to Client - includes final documentation (Excel) targeted to stakeholders.

Note that I have I have refrained from uploading the dataset due to file size, kindly notify me if you wish to see the prepared dataset.

“The Instacart Online Grocery Shopping Dataset 2017”, Accessed fromhttps://www.kaggle.com/competitions/instacart-market-basket-analysis/overview (accessed 03-02-2025).

Disclaimer: Instacart is a real company that’s made their data available online. However, the contents of the attached project brief have been fabricated by CareerFoundry (Data Analytics Bootcamp provider) for the purpose of this analysis.