Instacart

Jan 22, 2024
Analysis of Customer Preference

Overview

This project involved conducting an initial data and exploratory analysis of sales patterns and customer segmentation at Instacart, an online grocery store operating through a mobile app.

Context

The rationale behind the project stemmed from Instacart's desire to better understand their customer base and their purchasing behaviors. The stakeholders aimed to develop targeted marketing strategies to optimize advertising effectiveness and boost sales. As an analyst, I was tasked with uncovering insights from the available data to inform these strategies.

Project Object



Project Object

Project Object

  • Identify the busiest days and hours for orders to optimize ad scheduling

  • Determine peak spending times to tailor product advertisements

  • Simplify price range groupings to direct marketing efforts effectively

  • Identify popular product categories and departments with high order frequencies

  • Analyze customer segmentation based on factors to understand variations in ordering behaviors


Data

Data Set

  • The Instacart Online Grocery Shopping Dataset 2017, Accessed from www.instacart.com/datasets/grocery-shopping-2017  via Kaggle on 2024. 1.6

  • Data Dictionary

  • Customers Data Set (Instacart is a real company that’s made their data available online. However, the customer data have been fabricated for this project by CareerFounday)

Process

  • This Python project analyzes and addresses business queries for an online grocery store that functions through a mobile app. The initial phase involves data wrangling, encompassing data cleaning, consistency validation, and merging tasks.

  • The consolidated dataset is then utilized to respond to the predefined business questions, accompanied by visualizations created using Python, Tableau, and Excel.

Key Analysis

  1. Early morning is the best time for Ads, because the orders are less than at other times, but the expenditure is higher.

  1. The young high-income group consumed more than the middle-aged class in departments such as alcohol, household, personal care, bakery, and breakfast.

distribution of customer profile

Recommendations

Recommendations

Recommendations

  1. Focus on Early Morning and Middle age& Mid-income group

  2. Develop new customers: The "Young & High income" group

  3. Corporation Social Responsibility: The "Elderly low-income" is socially vulnerable