August 10, 2022

10 Great Portfolio Projects for Business Analysis (2022)

Business analysis project portfolio

You need a portfolio of relevant projects if you want to get a job as a business analyst. Why? There are at least two valid reasons:

  • Creating business analyst projects is an excellent way to practice your skills. Doing different exercises is good, but building an end-to-end project lets you apply various skills to solve real-world challenges.
  • Your portfolio of business analyst projects will be essential in your job hunt. To land an interview for a business analyst role, you need more than an eye-catching resume and list of all of your certificates and qualities. What you really need is to showcase your skills — the best way to do that is with a portfolio of projects.

In this article, we’ll share 10 great projects that you can add to your portfolio to help hone your skills and land your next interview. 

1. Sales Data Analysis

As a business analyst, you’ll likely work with sales data because it plays a crucial role in the commercial success of your company. Whether that means understanding current sales or forecasting future sales, this is a key skill that employers look for. 

Sales records usually contain information on a company’s customers, customers’ sales orders, payment history, product categories, etc. This data allows you to analyze your customers’ demographics, which products they buy, when they buy, how much revenue they generate, how well they respond to promotions, and more. 

How to Build Your Project

You can take an available dataset (like this one: Sales Product Data) and analyze sales data from various aspects. The main objective here is to extract key performance indicators (KPIs) that will enable you to make data-driven decisions and improve your company’s business.  

Below are some questions you can try to answer in a project on sales data analysis::

  • What is the total number of sales?
  • What is the average sales per month? 
  • What is the monthly revenue?
  • What are the key demographics of the customers?
  • Which market (country) generated the most sales on average?
  • What were the profits by segment?
  • When were the best- and worst-selling periods?
  • Which products sell best? 
  • Which products should the company order more or less of?
  • How should the company adjust its marketing strategies to VIP customers and less-engaged ones?
  • Should the company acquire new customers, and how much money should they spend on it?

2. Customer Churn Rate Prediction

Customer churn rate is also a key business indicator that can help you improve your business. It indicates the percentage of people who stopped using your company’s product or service during a defined period of time.

This metric is particularly relevant for subscription-based businesses where discontinuation of the product is easy to detect: the customer has stopped using your product or has canceled their subscription, so the company lost a client. 

A high customer churn rate can indicate serious issues with your business: low-quality product, negative customer experience, lack of customer support, etc. That’s why a key goal of any business is to minimize customer churn.

How to Build Your Project

You can build your own project on predicting customer churn rate. To do so, take an available dataset (like this one: Customer Churn Prediction 2020) and analyze a company’s data to identify customers who are likely to churn based on a variety of factors, such as the number of calls to customer service and the total charge for calls.

3. Customer Review Sentiment Analysis

Customer review sentiment analysis is a process of detecting customers’ feelings after they have purchased a company’s products. The company can gather this information from product reviews, feedback forms, tickets to their help center, online surveys, etc.

Every company is interested in conducting customer feedback sentiment analysis since it’s a secure way to determine possible reasons for customers’ complaints, and to strengthen the product features that make customers happy. As a result, the business can take measures to fix the issues in a timely manner, improve the customer experience, reduce the customer churn rate, adjust marketing campaigns, and maximize profits.

How to Build Your Project

To build a project on customer review sentiment analysis, you need to find an available dataset (e.g., Sentiment analysis with hotel reviews) with text data extracted from customer reviews of a certain company. Alternatively, consider parsing such data from the internet by yourself. Your task in this project is to preprocess the text data and explore it using specialized statistical and linguistic tools to identify positive, negative, and neutral experiences and, ideally, their strength and subjectivity. 

Be aware of some intrinsic weaknesses of text analysis techniques. For example, they aren’t always able to interpret slang words or rarely used abbreviations — or detect sarcasm.

4. Market Basket Analysis

Market basket analysis explores customer shopping patterns. In other words, we have to answer the question, Which products are commonly purchased together? As a simple everyday example, when someone buys shoes, they would probably be interested in buying shoe polish as well. In real-world market basket analysis, however, the examples can be much less obvious.

Detecting specific product associations helps retailers adjust their recommendation systems, improve marketing strategies, maintain balanced stock, and place the correlated goods close to each other in their stores. In the long run, this approach leads to increasing the company’s sales, improving customer satisfaction, and finding new business opportunities.

How to Build Your Project

As a business analyst project idea, you can take a large set of a retail company’s data (e.g., Groceries dataset for Market Basket Analysis (MBA)) and investigate customers’ historical transactions. You should focus on descriptive analytics of customers’ purchase behavior, revealing interesting combinations of products that are frequently bought together, and creating valuable suggestions for the company.

5. Price Optimization

Estimating the optimal prices for their products is one of the most important tasks for any modern company. This regards both new companies that just appeared on the market and already-existing ones that are trying to adapt to changing economic conditions — or are planning to grow their business geographically or by market segment.

To solve the price optimization problem successfully, a business analyst needs to investigate historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc.

How to Build Your Project

For this project, you can take a dataset of price data for a retail company (e.g., Retail Price Optimization) containing such information as product names, historical prices, product categories and characteristics, volume of sales, and time and geographic notations. The task here is to select and analyze relevant price-forming factors and the degree of their influence on the prices. Your main goal should be to calculate the optimal selling prices for the products to create efficient, data-driven recommendations for the company.

6. Stock Market Data Analysis

Stock Market Data Analysis involves exploration of the stock market in general, a particular investment sector, or a specific trading instrument. Traders and investors need this analysis to understand past and current trends in the market — and, hence, make better buying and selling decisions.

The stock market generates a huge amount of data every day on the price values and trading volumes of a company. 

Consider answering the following questions:

  • How often did the company increase (or decrease) in price on a given day?
  • What is the general trend of average monthly closing prices over the year?
  • Are there any seasonal patterns in trading volumes?
  • Is there a relationship between the daily maximum and minimum prices for the company?
  • Do large differences in the daily maximum and minimum prices coincide with higher or lower trading volumes?
  • Do the patterns in the most recent year match previous years?

How to Build Your Project

In order to build your project, you can select a specific dataset (such as Microsoft Stock Data, Amazon Stock Data, or INTEL Stock Data, explore the company’s historical stock performance, and find insights about the future. 

7. Customer Segmentation

Customer segmentation involves segregating a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. This enables the company to direct its marketing campaigns and offers to the correct target audience. Such a strategy helps the business save time, optimize effort, maximize profits from each client, and improve customer experience.

How to Build Your Project

For your project on customer segmentation, you can find an available dataset (like this one: Customer Segmentation Classification) that contains customer data of a certain organization. Then, analyze the data from the standpoint of paying capacity and purchasing pattern similarities among the company’s clients. Most likely, you’ll discover that such patterns depend on a wide range of demographic and geographic factors. On the other hand, there are other criteria you can take into account, such as retail vs. wholesale customers. At the end of the project, try to determine some suggestions for which kinds of existing or products or products-in-development the company should advertise to each segment.

Keep in mind that for your customer segmentation model to be effective for the company’s needs, it should provide a reasonable number of classes.

8. Fraud Detection

Fraud is a widespread problem in many industries, like banking, sales, and insurance. The most common form of fraudulent activity is credit card fraud, but there are others, such as identity theft or a cyber attack. This problem is especially challenging because fraudsters’ strategies are constantly adapting and becoming more sophisticated. This means that there is no one-size-fits-all solution for detecting fraud.

How to Build Your Project

A project on fraud detection would be an asset for your business analyst portfolio. What you need to do is to take a dataset with the data on online transactions (e.g., this one: Credit Card Fraud Detection) and analyze it for suspicious operations using statistical methods. Are there any features that the fraud transactions have in common? Knowing such features (or combinations of features) in advance would help the company identify fraudulent actions timely and take preventive measures.

9. Life Expectancy Analysis

Life expectancy is a critical indicator of health in a certain country (or region). This metric depends not only on the level of medicine in that country but also on its environmental conditions, economic and political context, and social tendencies.

Analyzing the correlation between gross domestic product (GDP) per capita and life expectancy is a good idea for your next business analyst project. 

How to Build Your Project

Find a suitable dataset (e.g., Life Expectancy (WHO)) that provides information on both life expectancy and GDP per capita by year for different countries and regions, explore and visualize the data using appropriate plots, and develop meaningful insights. You may notice some trends for each country or region, as well as an overall tendency. Think about the following questions:

  • For each geographic unit, is there a clear correlation between GDP per capita and life expectancy?
  • What are the geographic units with the highest and lowest life expectancy? What about their GDP?
  • What other potential issues could take place in the geographic units with a lower life expectancy?
  • In general, is life expectancy in the modern world growing? And GDP? 

10. Building a BI App

In your everyday work as a business analyst, you’ll need to use business intelligence (BI) applications like Microsoft Power BI. Therefore, it’s important to become familiar with business intelligence before applying for business analyst jobs, and to showcase your BI skills in your project portfolio. Building a BI app by yourself is a great way to do so.

How to Build Your Project

For this project, consider taking the available data for a certain company, building a data model for it, and creating a series of analysis and visualizations on various metrics related to the products of that company. Such metrics might be product popularity, which shows the level of customers’ engagement with different products, and product ratings, which indicate customer satisfaction. Try to answer the following questions:

  • Which products have improved over time?
  • Which products have deteriorated over time?
  • Are there some distinct patterns for both categories of products (improved vs. deteriorated) in terms of their popularity and customer satisfaction? 

Based on the outcomes of your exploration, you can make recommendations to the company on which products need improvement. You can find a project like this as part of the Business Analyst path that we developed at Dataquest to take you from beginner to job-ready in less than a year.


Takeaway

In this project round-up, we considered 10 cool ideas for business analyst projects to add to your portfolio. Building business analyst projects for your portfolio is a perfect way to practice your skills, and demonstrate your proficiency in business analytics.


By learning with Dataquest, you’ll create high-quality business analyst projects. Half of the project ideas we discussed in this article come from the Business Analyst Career Path. For each of those projects, you’ll receive the data to analyze and guidance to follow. It goes without saying that you’re very welcome to build upon the provided instructions, dig deeper into the data, and extract your own insights.

Good luck!

Elena Kosourova

About the author

Elena Kosourova

Elena is a petroleum geologist and community manager at Dataquest. You can find her chatting online with data enthusiasts and writing tutorials on data science topics. Find her on LinkedIn.

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