3 Sales Data Analytics Project Ideas for your Resume
This blog post explores 3 Sales Data Analytics Project Ideas you can use to build your resume and impress interviewers. Each project uses a real-world dataset to showcase your analytical skills and storytelling abilities.
But before we begin, if you're someone who wants to build projects for Data Science Portfolio then keep your tabs on what's happening in the world of Data Science & Businesses, and Hit EARLY ACCESS if you haven't already.
If you already have Early Access, you can just go ahead and read our Data Story! 😀
Have you ever wondered what data analysts actually do? This blog post will give you a sense by outlining 3 Sales Data Analytics projects you can do using a dataset about consumer goods. We'll walk you through each project step-by-step, including how to ask the right questions and turn data into insights that can boost sales!
About Dataset
This project utilizes a real-world dataset from the FMCG (Fast-Moving Consumer Goods) retail industry. Think of it as a detailed sales records of information on customer purchases, including what they bought (Order), where they bought it (Product details), and even some customer demographics. This rich data allows us to analyze sales trends, identify top-performing stores, and gain valuable insights into customer buying behavior.
So you want to be a data Analyst? Landing a data analyst job means showing your skills with real-world projects. Lucky for you, we've got 3 awesome sales analysis projects that will guide you through analyzing real-world sales data to answer critical business questions and uncover valuable insights. This project advance your data analytics career forward.
Project 1: Retail Sales Analysis
In this Sales Analysis Project, we're basically exploring sales data, checking out what customers are up to, and seeing how fast stuff flies off the shelves. With Super AI's cool data visuals, we're hoping to figure out things like when sales peak, which products rock and how our marketing tricks pay off.
1. Define User Persona for this Project
For this project, Let’s say you're having meeting with an HR Manager. They're curious about the sales data, but maybe not as familiar with it as other departments. So, we'll approach the data analysis from their perspective, focusing on questions they might have. This will help us uncover insights that are relevant and easy for them to understand.
2. Design an Empathy Map
Once, let's get into the mind of our HR Manager with an Empathy Map:
3. Define Objective
And now, let’s define the objective based on the HR Manager Empathy Map:
Objective : To enhance sales performance and employee satisfaction by developing a data-driven strategy that correlates workforce attributes with sales outcomes, identifies areas for targeted training and development, and integrates disparate data sources for a holistic understanding of the impact of workforce management on sales efficiency.
4. Identify your KPI's
On the basis of User's Pain & Gain and Objective, we'll target the KPI's. They are as follows:
KPI
Formula
Decision It Can Help Drive
Product Sales Growth Rate
((Current Period Sales - Previous Period Sales) / Previous Period Sales) x 100%
Evaluate the growth of individual products or categories, identifying trends and demand shifts.
Category Market Share
(Sales by Category / Total Sales) x 100%
Determine the dominance of product categories within the overall sales mix, guiding inventory and marketing strategies.
Profit Margin per Product
(Profit per Product / Sales per Product) x 100%
Assess the profitability of each product, informing pricing and cost management decisions.
Discount Effectiveness
((Sales with Discount - Sales without Discount) / Sales without Discount) x 100%
Measure the impact of discount strategies on sales volumes and profitability for different products.
Average Sales per Product
Total Sales / Number of Products Sold
Understand the average revenue contribution of each product, identifying high and low performers.
Customer Reach Index
(Number of Orders for a Product / Total Number of Orders)
Gauge the popularity and reach of each product among the customer base, indicating market penetration and customer preference.
Customer Acquisition Cost
Total Costs of Acquiring New Customers / Number of New Customers Acquired
By understanding the cost of acquiring new customers, Product Analysts can better evaluate the profitability and return on investment of marketing and promotional efforts for different products.
5. Ask Relevant Questions
Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):
Q1. What is the count of managers at the region, state and city level?
Q2. How are Regional sales managers performing in sales and profit?
Q3. Who are top performing state and city sales managers under Rohan Sharma?
Q4. What are the sales under Rohan Sharma by store type?
Q5. Who are the top performing sales reps in sales and profit?
Explore full Retail Sales Analysis Data Story Here👇
This project dives deep into the world of FMCG (Fast-Moving Consumer Goods) to identify top-performing products and areas for improvement.
By analyzing sales data, customer preferences, and market trends, we can:
Identify category leaders
Discover potential growth
Enhance the customer experience
1. Define User Persona for this Project
Let's say, for this use case our Business User will be - Product Analyst
Product analyst who uses sales info, customer insights, and market trends to:
Find FMCG bestsellers and category champions.
Crack customer purchase codes to understand what makes them buy.
Recommend winning product, pricing, and promotions.
By helping Product Analyst with data, we can optimize FMCG products and increase repeat rate by keeping customers happy!
2. Design an Empathy Map
Once, let's get into the mind of our Product Analyst with an Empathy Map:
3. Define Objective
And now, let’s define the objective based on the Product Analyst Empathy Map
Objective: To enhance sales performance and market competitiveness by analyzing product sales data, understanding customer preferences, and adapting product strategies accordingly to meet market demand and maximize sales revenue.
4. Identify your KPI's
Next, let's measure success with some KPIs:
KPI
Formula
Decision It Can Help Drive
Product Sales Growth Rate
((Current Period Sales - Previous Period Sales) / Previous Period Sales) x 100%
Evaluate the growth of individual products or categories, identifying trends and demand shifts.
Category Market Share
(Sales by Category / Total Sales) x 100%
Determine the dominance of product categories within the overall sales mix, guiding inventory and marketing strategies.
Profit Margin per Product
(Profit per Product / Sales per Product) x 100%
Assess the profitability of each product, informing pricing and cost management decisions.
Discount Effectiveness
((Sales with Discount - Sales without Discount) / Sales without Discount) x 100%
Measure the impact of discount strategies on sales volumes and profitability for different products.
Average Sales per Product
Total Sales / Number of Products Sold
Understand the average revenue contribution of each product, identifying high and low performers.
Customer Reach Index
(Number of Orders for a Product / Total Number of Orders)
Gauge the popularity and reach of each product among the customer base, indicating market penetration and customer preference.
Customer Acquisition Cost
Total Costs of Acquiring New Customers / Number of New Customers Acquired
By understanding the cost of acquiring new customers, Product Analysts can better evaluate the profitability and return on investment of marketing and promotional efforts for different products.
5. Ask Relevant Questions
Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):
Q1. What is our overall sales and monthly sales trend in 2023?
Q2. How are our categories performing by states in sales?
Q3. What is our overall profit? What is our profit by product and category?
Q4. How are our sales by population and categories?
This project is all about using geographic and demographic data to find the best locations and target markets for our FMCG retail products. By analyzing where people live, their lifestyles, and preferences, we can pinpoint high-potential areas and develop deep-focused sales and marketing strategies shaped to specific regions and customer groups. It's about mapping out success by understanding our customers on a deeper level and connecting with them in a way that resonates. Get ready to explore new uncover untapped market opportunities!
1. Define User Persona for this Project
Let's say, for this use case our Business User will be - Market Analyst
Market analyst who uses location data and demographics to:
Find prime locations for FMCG products.
Crack customer lifestyle codes to see what they like.
Craft targeted marketing campaigns that resonate.
Reaches to untapped market
2. Design an Empathy Map
Once, let's get into the mind of our Market Analyst with an Empathy Map:
3. Define Objective
And now, let’s define the objective based on the Product Analyst Empathy Map
Objective: Focus high-potential geographic locations and customer segments to concentrate our efforts on. This will allow us to optimize our market presence and boost sales performance by creating localized marketing and sales strategies that shape specifically to the preferences and conditions of each region. The goal is to effectively reach and resonate with target audiences in different areas through specific approaches.
4. Identify your KPI's
Next, let's measure success with some KPIs:
KPI
Formula
Decision It Can Help Drive
Sales Performance by State
Total Sales by State
Identify high and low-performing states to tailor regional sales strategies.
City Sales Contribution
Sales by City / Total Sales
Evaluate the contribution of each city to overall sales, highlighting areas for potential market expansion or increased focus.
Population Group Sales Performance
Sales by Population Group / Total Sales
Assess sales performance across different population groups to refine marketing and product placement strategies.
Discount Impact on Sales by Region
((Sales with Discount in Region - Sales without Discount in Region) / Sales without Discount in Region) x 100%
Analyze the effectiveness of discounts on boosting sales in specific regions.
Average Sales per Transaction by Region
Total Sales in Region / Number of Transactions in Region
Understand the average transaction value in each region, informing pricing and promotion strategies.
Product Category Popularity by Region
(Number of Units Sold of Category in Region / Total Units Sold in Region)
Determine the most popular product categories in each region, aiding in inventory management and regional marketing efforts.
Profit Margin by Region
(Profit in Region / Sales in Region) x 100%
Evaluate the profitability of sales activities in different regions, guiding resource allocation and pricing strategies.
Sales Growth Rate by Population Group
((Current Period Sales - Previous Period Sales by Population Group) / Previous Period Sales by Population Group) x 100%
Identify sales growth trends within specific population groups, adjusting strategies to capture emerging opportunities.
Customer Acquisition Cost (CAC) by Region
(Marketing Costs + Sales Costs)
This KPI measures the cost of acquiring new customers in different regions, helping to evaluate the efficiency of marketing and sales efforts.
5. Ask Relevant Questions
Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):
Q1. What are our overall and yearly sales and profit?
Q2. How are sales and profit in different regions and what is our sales and profit by country?
Q3. What is our sales and profit by state in the south region?
Q4. Which are our top 10 cities by sales in Karnataka?
Q5. Who are our top 5 categories and subcategories in the south region?
Q6. what are our sales and profit by product in south region?
Q7. What is our sales and Average Discount by subcategory in the south?
Explore full Geographical Analysis Data Story Here👇
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