How Amazon Uses Discount Impact Analysis To Improve Sales And Profit
Overview
Amazon, headquartered in Seattle, Washington, and Arlington, Virginia, stands as one of the titans of modern commerce and technology. Founded by Jeff Bezos on July 5, 1994, initially as an online bookstore, Amazon swiftly diversified its offerings, earning the moniker "The Everything Store." Its revenue soared to a staggering US$574.8 billion in 2023, with operating income hitting US$36.85 billion and net income reaching US$30.43 billion. The company's total assets grew to US$527.9 billion, and its total equity to US$201.9 billion in the same year. With approximately 1,525,000 employees worldwide, Amazon operates across various sectors, including e-commerce, cloud computing, digital streaming, and artificial intelligence. Noteworthy subsidiaries such as Amazon Web Services, Ring, Twitch, and Whole Foods Market contribute to its multifaceted presence. As of October 2023, Amazon ranks as the 12th most visited website globally, with 82% of its traffic originating from the United States. It dominates sectors such as online retail, smart speakers, and cloud computing, challenging even the most established players. Amazon's disruptive influence, marked by technological innovation and aggressive reinvestment, has reshaped industries worldwide, though it has also faced criticism for issues such as data privacy, workplace culture, and anti-competitive practices. Nonetheless, its impact on global commerce and technology remains unparalleled. Now, let's dive into some insights (just for learning purposes!) on how Amazon might analyze their data to better understand their Discount Impact Analysis.
About Dataset
Let’s Inspect our Superstore dataset. This dataset got all the deeds on sales – order IDs, order and shipping dates, shipping mode, customer names and where they're at, plus product info like category and name. And of course, we've got the numbers – sales amount, quantity, discounts, and profit.
This data enables analysis of sales performance, profitability, and customer behavior to improve supply chain management decisions. Here are some sample Rows & Columns of the Superstore Dataset.
ORDER DETAILS
Transaction Information
Row ID
Order ID
Order Date
Ship Date
Ship Mode
Customer ID
Customer Name
Segment
Country/Region
City
State/Province
1
US-2019-103800
1/3/2019
1/7/2019
Standard Class
DP-13000
Darren Powers
Consumer
United States
Houston
Texas
2
US-2019-112326
1/4/2019
1/8/2019
Standard Class
PO-19195
Phillina Ober
Home Office
United States
Naperville
Illinois
3
US-2019-141817
1/5/2019
1/12/2019
Standard Class
MB-18085
Mick Brown
Consumer
United States
Philadelphia
Pennsylvania
PRODUCT DETAILS
Transaction Information
Postal Code
Region
Product ID
Category
Sub-Category
Product Name
77095
Central
OFF-PA-10000174
Office Supplies
Paper
Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
Each row in the dataset corresponds to an order made by a customer. We have the following features:
ORDER SHIPMENT
Transaction Information
Field
Type
Description
Order ID
String
Unique identifier for each order.
Order Date
Date
Date when the order was placed.
Ship Date
Date
Date when the order was shipped.
Ship Mode
String
Mode of shipment chosen for the order.
Segment
String
Market segment the customer belongs to.
Region
String
Region where the order was placed.
City
String
City where the order was placed.
State/Province
String
State or province where the order was placed.
Postal Code
Integer
Postal code of the location where the order was placed.
PRODUCT
Transaction Information
Field
Type
Description
Product ID
String
Unique identifier for each product.
Category
String
Category of the product.
Sub-Category
String
Subcategory of the product.
Product Name
String
Name of the product.
Sales
Integer
Total sales amount for the order.
Quantity
Integer
Quantity of the product ordered.
Discount
Integer
Discount applied to the order.
Profit
Integer
Profit generated from the order.
OTHER FEATURES
Transaction Information
Field
Type
Description
Row ID
String
Identifier for each row in the dataset.
Customer ID
String
Unique identifier for each customer.
Customer Name
String
Name of the customer placing the order.
The SuperStore Dataset contains over 10194 orders and 13 of our Columns have String Data type, 4 of our Columns have Integers Data Type and 2 of the columns have Date Data Type.
How Amazon uses discount impact analysis to improve sales & profit ?
To start with the Discount impact analysis ,it is necessary to follow the 4 factors of Data Analysis and that are :
1. Identify the users or stakeholders for the dashboard.
2. Design Empathy Map to define Users' Goals and Challenges or pain points.
3. Identify Metrics or KPIs Matter the Most.
4. Understand the Objectives and Goals.
5. Ask Business Questions
I know it looks a bit overwhelming, that's why in this article, we'll lay the foundation for a top-notch discount impact analysis.
Step 1: Identify the users or stakeholders for the analysis
Define the User or stakeholder who will use the discount impact analysis data story. In our case, We’ll use a Business Analyst. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story.
User Persona: Business Analyst
Responsibilities:
Analyzing sales data to discern trends, patterns, and opportunities for optimization within various product categories and market segments.
Utilizing statistical software and data visualization tools to conduct in-depth analysis of sales performance, pricing strategies, and customer behavior.
Evaluating the impact of discounting strategies on sales volume, revenue generation, and profitability across different product lines and regions.
Collaborating with marketing, sales, and finance teams to develop and implement effective discounting strategies that align with business goals and objectives.
Needs:
Access to comprehensive sales data covering various products, regions, and time periods to ensure accuracy in analysis and decision-making.
Proficiency with statistical analysis software such as R or Python to conduct advanced analytics, including regression analysis, forecasting, and segmentation.
Integration capabilities with other systems within the company's sales and marketing infrastructure to maintain a unified view of customer data and sales performance.
Challenges:
Ensuring the accuracy and consistency of sales data to provide reliable insights into the effectiveness of discounting strategies.
Addressing the complexities of analyzing sales performance across diverse product categories, distribution channels, and customer segments.
Balancing the need for discounting to drive sales with the potential impact on brand perception, customer loyalty, and long-term profitability.
Since, we have defined the User Persona & have mapped the needs, challenges and responsibilities. Our Next step will be to design an Empathy Map which will map the pain points of the user.
Step 2: Design Empathy Map
To truly connect with the experiences and expectations of a Business analyst, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users.
By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.
Step 3: Identify the Key Performance Indicators (KPI’s)
The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into their discount impacts.
Transaction Information
KPI Name
Formula
Description
Total Sales
Sum of all Sales
Measures the overall revenue generated from all sales. This KPI is essential for evaluating the financial health and growth trajectory of the business across all regions and categories.
Sales by Region
Sum of Sales per Region
Analyzes sales distribution across different geographical regions, helping identify which regions contribute most to revenue and where to focus growth strategies.
Top Performing States and Cities
Max(Sales) grouped by State and City
Identifies states and cities with the highest sales, indicating areas of strong market presence and potential regions for targeted marketing or expansion.
Top Performing Categories by Region
Max(Sales) grouped by Category and Region
Reveals the product categories with the highest sales in each region, offering insights into regional consumer preferences and market demand.
Top Product by Sales and Profit
Max(Sales) and Max(Profit) per Product
Determines the products with the highest sales and profit, highlighting successful items that significantly contribute to the company's financial performance.
Step 4: Understand the Goals & Objectives of User
Now, on the basis of Empathy Map and KPI’s, we need to define our goals and objectives of the Users. So, that it will align with the data story functionalities to ensure decision making. Here are the Key Objectives & Goals :
Objective The objective of the business analyst within a superstore context is to leverage data analysis techniques and tools to optimize operational efficiency, strategic decision-making, and customer satisfaction. By harnessing sales data and market insights, the analyst aims to enhance the store's performance across various aspects of its operations, from inventory management to marketing strategies.
Goals:
Operational Efficiency: The primary goal is to improve operational efficiency by analyzing sales data and inventory levels to optimize stocking levels and minimize stockouts. Additionally, the analyst aims to identify bottlenecks in the supply chain and streamline processes to reduce costs and improve overall efficiency.
Strategic Decision-Making: Another goal is to provide insights to support strategic decision-making, such as pricing strategies, product assortment planning, and expansion opportunities. By analyzing market trends and customer preferences, the analyst can help identify growth opportunities and mitigate risks.
Customer Satisfaction: The analyst also aims to enhance customer satisfaction by understanding customer preferences and behavior. By analyzing customer feedback and purchase history, the analyst can identify areas for improvement and develop strategies to enhance the shopping experience.
Step 5 : Ask Business Questions
Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives.
What is the average discount rate?
Metric: discount rate
Question: some text
Show me the average discount rate?
Observation: The average discount rate is around 16%.
How are total sales and average discount rate across regions ?
Metric: total sales, average discount rate
Question: some text
Total sales by region?
Add average discount rate
Observation: It shows the Central region has a high discount rate of 24% while its sales and profit lags really behind. This high discount rate needs to be investigated.
How are total sales and discount patterns distributed across categories in different regions?
Metric: sales, discount rate
Question: some text
Sales by category by region?
[Add More] Average discount rate by category by region?
Observation: In the central region, Furniture and Office supplies have a high discount rate despite generating very less sales and profit. We may need to optimize the pricing here.
What is the average discount rate by sub-categories across regions? How do it relate with total sales and profit? Are there any regions and sub-categories we need to look at?
Metric: discount rate, sales, profit
Question: some text
Average discount rate by sub-category by region?
Add total sales and total profit
Add category
Where region is Central
Observation: We can clearly observe that some of the sub-categories in the Central region have high discount rate like Binder (51%), Appliances (45%), Furnishings(40%), Machines(33%) etc, which needs to be optimized as they are generating massive losses.
Outcomes
The comprehensive analysis of discount rates and their effects on total sales and profit across different regions and product categories has provided valuable insights. The average discount rate across all regions is approximately 16%. However, the Central region exhibits an unusually high average discount rate of 24%, while its sales and profits remain significantly lower than those of other regions. This discrepancy suggests that the elevated discount rates in the Central region are not effectively boosting sales or profitability.
Conclusion
The findings underscore the necessity for a targeted review and optimization of discount strategies, particularly in the Central region. The high discount rates currently applied are not yielding the desired increase in sales or profitability and, in some cases, are resulting in considerable financial losses. To address this, a strategic reevaluation of discount policies is essential. Implementing more effective pricing strategies, refining marketing efforts, and ensuring discounts are used judiciously can enhance overall financial performance. These data-driven insights will enable better-informed decisions, leading to optimized pricing, improved sales, higher profitability, and increased customer satisfaction across all regions.
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