8 Business Analyst Project Ideas for your Resume

Business analysts are crucial in organizations because they bridge the gap between business demands and technical solutions. Their analytical abilities and strategic insights propel corporate growth and innovation. Here are eight interesting ideas to drive innovation and contribute to organizational success if you're a business analyst looking for inspiration for your next project.

Beginner Level 

1. Sales Performance Analysis

Project description :

The primary goal of this project is to gain valuable insights into the sales performance of the company over the specified time frame. Through comprehensive analysis and visualization, the project aims to identify patterns, trends, and key factors influencing sales. This includes understanding the impact of discounts, examining the performance of different product categories and sub-categories, and exploring customer behavior.

What you’ll learn:

• SQL fundamentals such as join operations and aggregation functions.
• Database Creation and Import
• Data Cleaning and Transformation:
• Data Analysis and Data Visualization through SQL
• Advanced SQL Concepts such as Subqueries and window functions‍

‍Tools used: 

• SQL Database such as MySQL or PostgreSQL

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2. Customer Segmentation

Project description :

This project aims to understand the customers of an automobile bike company better by dividing them into groups based on their behavior. This can be done using an RFM Model (Recency, Frequency, Monetary) .  This model helps to  look at when, how often, and how much customers buy.Then divide customers into  groups based on this analysis. The goal is to figure out which groups of customers to focus on to increase the company's sales. 

What you’ll learn :

• RFM (Recency, Frequency, Monetary) analysis , a behavior-based approach grouping customers into segments. 
• Data analysis and segmentation using pandas library.
• Customer data visualization using Matplotlib library. 
• Building sales dashboard for customer segmentation. 

Tools used : 

• Python: To check the data's quality and cleaning operation. Python's pandas, matplotlib, and seaborn libraries, to analyze the datasets and get helpful insights from the data.
• Tableau: Tableau, a tool for Business Intelligence, to look into the data and make charts, graphs, and visualizations. This will help to create a Sales Dashboard for Customer Segmentation for the automobile bike company.

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3. Expense Tracking and Budget Analysis

Project description: 

The aim of the Expense Tracking and Budget Analysis project using Excel is to develop a user-friendly system for monitoring and analyzing personal or business expenses. It enables effective budgeting, expense categorization, and provides insights for informed financial decision-making.

What you’ll learn:

• Writing formulae and functions in excel for automated calculations, budget comparisons, and trend analysis.
• Conditional formatting techniques for visual cues on budget adherence.
• Creating charts and graphs to visually represent spending patterns.

Tools used: 

Excel or Google spreadsheet

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Intermediate Level 

4. Market Basket Analysis

Project description :

This project focuses on implementing Market Basket Analysis to uncover valuable insights into customer purchasing behavior within the retail sector. Market Basket Analysis is a data mining technique that explores associations and patterns among products in customer transactions. The goal is to leverage these insights for optimizing product placement, marketing strategies, and overall retail operations.

What you’ll learn:

•  Market Basket Analysis Using the Apriori Method
• Data Mining of  customer data
• Finding patterns and  meaningful associations among products that indicate customer preferences and buying habits.

Tools used: 

• Python

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5. Retail Price Optimization

Project description: 

This project focuses on implementing price optimization strategies using historical data to determine the most profitable pricing for products or services. Efficient pricing is crucial for maximizing company profitability, taking into account factors such as demography, operating costs, survey data, and the nature of the business and product. Regression techniques can be applied to find an optimal price. 

What you’ll learn:

• Exploratory data analysis
• Data visualization
• Demand forecasting
• Price optimization
• Regression Model for predicting Optimal Prices
• Model Explainability

Tools used: 

• Python libraries such as Pandas, Matplotlib and Scikit-Learn

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6. Employee Performance Analytics

Project description :

Attrition, or employees leaving a company, is a common issue affecting businesses, causing disruptions and extra costs for hiring and training. Using a model to predict possible departures helps HR intervene in time. Reducing attrition improves overall business efficiency. In this project discover the factors contributing to employee turnover and investigate key queries, such as analyzing distance from home based on job roles and attrition, or comparing average monthly income by education and attrition.

What you’ll learn:

• Extensive Exploratory Data Analysis to get data insights and  for finding reasons for attrition
• Correlation and Bi-variate analysis for finding similar variables.
• Decision trees for predictive modeling.
• Using Automated Machine Learning with H2o

Tools used: 

• R programming language 
• H20 library for ML

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Advanced Level

7. Predictive Analytics for Sales Forecasting

Project description :

The Walmart dataset, a prominent retail corporation, has shared data from 45 hypermarkets, including store details and monthly sales, tracked on a weekly basis. The focus is on assessing the impact of various factors, especially holidays (Christmas, Thanksgiving, Super Bowl, Labor Day), on store sales. The primary objective is to predict weekly sales, considering the dataset's size, time-related features, and spatial elements. The analysis aims to uncover how time and location influence sales and, crucially, how holidays contribute to increased store sales.

What you’ll learn:

• Visualize time series patterns, autocorrelation, and decomposition.
• Time series analysis using ARIMA model.
• Conduct statistical tests like ADF to check stationarity.
• Automating ARIMA model selection. 

Tools used: 

• Python programming language 
• NumPy and pandas for data manipulation
• Stats models Statistical library 
• Scikit-Learn for machine learning 
• Arima model for forecasting

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8. Customer Lifetime Value (CLV) Modeling

Project description: 

Customer Lifetime Value (CLV) prediction is crucial for businesses, representing the net profit a company expects from its entire future relationship with a customer. This project focuses on estimating CLV using probabilistic models, specifically the BG/NBD and Gamma-Gamma models, to understand and predict customer purchasing behavior. The goal is to segment customers based on their CLV and formulate tailored marketing strategies.

What you’ll learn:

• Multivariate, Sequential, Time-Series and Text Analysis 
• Data Handling using NumPy and pandas 
• Customer Lifetime Value Modeling using Lifetimes Library.
• Estimating customer future transaction probability using the Beta-Geometric/NBD model.
• Estimating conditional expected average transaction value using the Gamma-Gamma model.
• Data preprocessing and visualization. 

Tools used: 

• Python programming 
• Pandas and NumPy library 
• Customer Lifetime Value Modeling library such as Lifetimes Library and Beta Geo Fitter
• Visualization libraries

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