There are many potential insights one can draw from this dataset. The ecommerce data provided contains information about sales orders, including the order ID, order date, shipping date, customer names, location (country, city, state), product categories, product names, sales amount, and profit amount. The dataset covers a range of orders over several years, with information on different product categories and their associated sales. It also provides insights into the distribution of orders across cities and states. After importing the data into Tableau, one can sort to see which states have the most total sales (CA, WA, AZ), which product categories have the highest profit (chairs, phones, machines), and various other intersections of data. The analysis from this data can be used to make decisions about what products to increase or reduce stock of, which states to focus on to push sales, and how to maximize profits by looking at which product categories have the highest profit margins.

If you’re interested, please take a look!

Dataset originally from https://www.kaggle.com/datasets/imgowthamg/walmart-sales-dataset

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

An interactive chart illustrating E-commerce customer complaints

Dataset

This dataset was created by Bhavika Puri 2811

Contents

CONTEXT

E-commerce (electronic commerce) is the buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, primarily the internet. These business transactions occur either as business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer or consumer-to-business

CONTENT

This is simple data set of US online_store from 2020.

INSPIRATION

So, the data cames with some questions !!

What was the highest Sale in 2020? What is average discount rate of charis? What are the highest selling months in 2020? What is the Profit Margin for each sales record? How much profit is gained for each product? What is the total Profit & Sales by Sub-Category? People from city/state shop the most? Develop a function, to return a dataframe which is grouped by a particular column (as an input)

If you have wonderful idea about this dataset, welcome to contribute !!! Happy Kaggling, please up-vote if you find this dataset helpful!🖤!

The visual analytics market is experiencing robust growth, fueled by the increasing reliance on internet-driven operations and the explosive expansion of e-commerce. The market's Compound Annual Growth Rate (CAGR) of 11.32% from 2019 to 2024 indicates a significant upward trajectory. This expansion is primarily driven by the need for e-commerce vendors to effectively track customer behavior, analyze market trends, and optimize decision-making processes. The surge in digital advertising within the e-commerce sector further accelerates the demand for sophisticated visual analytics tools that provide actionable insights from vast datasets. This market is segmented by type (e.g., descriptive, predictive, prescriptive) and application (e.g., marketing, finance, supply chain), offering diverse opportunities for vendors. Key players like Altair Engineering Inc., Alteryx Inc., and Tableau Software LLC are strategically positioned to capitalize on this growth, employing competitive strategies focused on innovation, customer engagement, and expanding market reach. The geographical distribution of the market reveals significant opportunities across North America, Europe, and the Asia-Pacific region, reflecting the global adoption of digital technologies and the increasing volume of data requiring analysis. While precise market sizing for 2025 and beyond requires further specification of the "XX" value, the current growth trajectory suggests a substantial increase in market value over the forecast period (2025-2033). The continued integration of visual analytics into various business functions across different industries will ensure sustained growth in the coming years. The competitive landscape is characterized by both established players and emerging startups. Established players leverage their extensive customer base and brand recognition, while smaller companies often focus on niche applications or innovative technologies. Future market growth will depend on factors such as technological advancements, the development of user-friendly interfaces, increasing data accessibility, and the ability of vendors to effectively address the unique analytical needs of different industry sectors. The ongoing integration of artificial intelligence (AI) and machine learning (ML) capabilities within visual analytics platforms is expected to drive further innovation and market penetration, enhancing the accuracy and speed of data analysis. Companies that successfully adapt to these evolving trends and deliver valuable insights will be best positioned for success in this dynamic and rapidly expanding market.

The 3D visualization for eCommerce market is experiencing robust growth, driven by the increasing demand for immersive online shopping experiences and the need for businesses to stand out in a competitive landscape. The market, currently valued at approximately $2 billion in 2025 (an estimated figure based on typical market size for emerging technologies and provided context), is projected to experience a Compound Annual Growth Rate (CAGR) of 20% between 2025 and 2033. This expansion is fueled by several key factors, including the rising adoption of augmented reality (AR) and virtual reality (VR) technologies, improving internet infrastructure enabling faster loading times for 3D models, and the growing preference among consumers for interactive and engaging online shopping experiences. Key applications driving market growth include fashion, jewelry, furniture, and automotive sectors, where 3D visualization offers significant benefits in showcasing product details, reducing return rates, and enhancing customer satisfaction. The cloud-based segment holds a significant market share due to its scalability and cost-effectiveness, although on-premises solutions remain relevant for businesses with stringent data security requirements. Competition in the market is characterized by a mix of established players and emerging startups. Major companies like Threekit, VNTANA, and Zakeke are leading the way in innovation, offering comprehensive solutions that cater to various e-commerce needs. However, the market landscape is dynamic, with continuous innovation in 3D modeling software and rendering techniques further driving market expansion. Geographic growth is expected to be widespread, with North America and Europe representing major markets initially, followed by rapid expansion in Asia-Pacific regions due to increasing internet penetration and e-commerce adoption. Restraints to growth include the high initial investment costs associated with implementing 3D visualization technology and the need for specialized skills in 3D modeling and animation. Despite these challenges, the overall market outlook remains highly positive, with significant growth opportunities expected over the forecast period.

Pakistan’s largest ecommerce data – Power BI Report

Dataset Link: pakistan’s_largest_ecommerce_dataset Cleaned Data: Cleaned_Pakistan’s_largest_ecommerce_dataset

Raw Data:

Rows: 584525 **Columns: **21

Process:

All the raw data transformed and saved in new Excel file Working – Pakistan Largest Ecommerce Dataset

Processed Data:

Rows: 582250 Columns: 22 Visualization: Here is the link of Visualization report link: Pakistan-s-largest-ecommerce-data-Power-BI-Data-Visualization-Report

Conclusion:

In categories Mobiles & Tables make more money by selling highest no of products and also providing highest amount of discount on products. On the other side Men’s Fashion Category has sell second highest no of products but it can’t generate money with that ratio, may be the prices of individual products is a good reason behind that. And in orders details we experience Mobiles & Tablets have highest no of canceled orders but completed orders are almost same as Men’s Fashion. We have mostly completed orders but have huge no of canceled orders. In payment methods cod has most no of completed order and mostly canceled orders have payment method Easyaxis.

Well, this dataset contains raw and cleaned data where we have added state code and used some lookup functions to clean this dataset. It includes 4 files where 2 are CSV and 1 is png and dashboard

Dataset link - https://www.kaggle.com/datasets/benroshan/ecommerce-data

The global data visualization market, currently valued at $9.84 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various sectors necessitate efficient tools for analysis and interpretation. Businesses are increasingly recognizing the importance of data-driven decision-making, leading to significant investments in data visualization solutions. Furthermore, the rising adoption of cloud-based platforms and the growing demand for advanced analytical capabilities, such as predictive analytics and machine learning integration within visualization tools, are significantly contributing to market growth. The market is segmented by organizational department (Executive Management, Marketing, Operations, Finance, Sales, Other), deployment mode (On-premise, Cloud/On-demand), and end-user industry (BFSI, IT & Telecommunication, Retail/E-commerce, Education, Manufacturing, Government, Other). The competitive landscape is characterized by a mix of established players like Salesforce (Tableau), SAP, Microsoft, and Oracle, and smaller, specialized vendors. The competitive intensity is likely to remain high, with vendors focusing on innovation, strategic partnerships, and expanding their product portfolios to cater to specific industry needs. The North American market currently holds a significant share, driven by early adoption of advanced technologies and a robust IT infrastructure. However, the Asia-Pacific region is anticipated to witness the fastest growth due to increasing digitalization across various sectors and rising demand for data-driven insights in rapidly developing economies. While the on-premise deployment model still holds a considerable market share, the cloud/on-demand model is gaining traction owing to its scalability, cost-effectiveness, and accessibility. Factors such as data security concerns, integration complexities, and the need for specialized skills could act as potential restraints on market growth. However, ongoing technological advancements, coupled with increasing awareness of data visualization benefits, are expected to mitigate these challenges and drive market expansion in the coming years. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Notable trends are: Retail Segment to Witness Significant Growth.

Dataset

This dataset was created by Nikhil Dafale

Contents

Dataset Description

This dataset contains historical sales data for an e-commerce platform, including customer behavior, product preferences, and transaction details. The data is structured to support analyses aimed at understanding customer behavior, predicting product preferences, and improving overall revenue through strategic marketing and sales efforts. Full data: Brazilian E-commerce Public Dataset

Key Components:

• Customer Data: Includes demographic information, purchase history, and browsing behavior.
• Product Data: Details about the products, such as category, price, and sales volume.
• Transaction Data: Records of individual transactions, including timestamps, purchased items, and total amounts.
• Reviews Data: Customer reviews and ratings for products, useful for sentiment analysis and product feedback.

Usage:

The dataset is intended for: - Analyzing customer behavior to improve marketing strategies. - Predicting product preferences to enhance cross-selling and up-selling. - Automating reporting and creating real-time dashboards. - Implementing and testing machine learning models for sales prediction and customer retention strategies.

Format:

The dataset is provided in CSV format, with each file corresponding to a different aspect of the e-commerce data (e.g., customers, products, transactions, reviews). Each file includes relevant columns for the type of data it contains.

WMS Data Visualization Tools Market Outlook

According to our latest research, the WMS Data Visualization Tools market size on a global scale was valued at USD 1.46 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.1% projected from 2025 to 2033. This dynamic growth trajectory is driven by increasing digital transformation initiatives across supply chains and the rising necessity for real-time data-driven decision-making in warehouse management. By 2033, the market is forecasted to reach USD 4.44 billion, reflecting the growing adoption of advanced analytics and visualization solutions within warehouse management systems (WMS) worldwide. As per our latest research, the surge in e-commerce, the need for operational efficiency, and the integration of artificial intelligence and IoT technologies are major factors propelling this market’s expansion.

The primary growth driver for the WMS Data Visualization Tools market is the intensifying demand for real-time insights and actionable intelligence in warehouse operations. As businesses strive for higher efficiency and accuracy, WMS data visualization tools enable organizations to transform complex data sets into intuitive visual formats, facilitating quicker and more informed decision-making. The proliferation of omni-channel retailing and the surge in global e-commerce transactions have significantly increased the volume and complexity of warehouse data, necessitating advanced visualization tools to manage inventory, optimize picking routes, and enhance labor productivity. Moreover, the integration of these visualization tools with AI and machine learning algorithms is further enhancing predictive analytics capabilities, allowing organizations to anticipate demand fluctuations, minimize stockouts, and reduce operational costs.

Another significant growth factor is the increasing adoption of cloud-based WMS solutions, which has democratized access to advanced data visualization tools for organizations of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling even small and medium enterprises (SMEs) to leverage sophisticated analytics without the need for substantial upfront investments in IT infrastructure. The shift towards cloud-based solutions also facilitates seamless integration with other enterprise systems such as ERP and TMS, providing a holistic view of supply chain operations. Furthermore, the ongoing advancements in data security and compliance standards are alleviating concerns related to cloud adoption, thereby accelerating the market’s growth trajectory.

Continuous technological innovation is also playing a crucial role in shaping the future of the WMS Data Visualization Tools market. The rapid evolution of IoT devices, the deployment of RFID and barcode systems, and the proliferation of mobile applications are generating vast amounts of data in warehouses. Data visualization tools are essential for harnessing this data, enabling warehouse managers to monitor key performance indicators (KPIs), track asset movements, and identify process bottlenecks in real time. Additionally, the growing emphasis on sustainability and green logistics is prompting organizations to utilize visualization tools for monitoring energy consumption, waste generation, and resource utilization, further expanding the application scope and driving market growth.

Regionally, North America dominates the WMS Data Visualization Tools market, accounting for the largest market share in 2024. This leadership is attributed to the high penetration of advanced warehouse automation technologies, strong presence of leading market players, and early adoption of digital solutions across retail, manufacturing, and logistics sectors. Europe follows closely, driven by stringent regulatory requirements, increasing focus on supply chain transparency, and the rapid expansion of e-commerce. The Asia Pacific region is emerging as the fastest-growing market, fueled by rapid industrialization, expanding retail landscapes, and significant investments in logistics infrastructure, particularly in China, India, and Southeast Asia.

Component Analysis

The WMS Data Visualization Tools market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment, encompassing dashboard solutions, analytics platforms, and visualization engines, holds the lion’s share of the market. Organizations are increasingly investing in sophisticated software tools that can seamlessly integrate wit

The 3D visualization market for online stores is experiencing robust growth, driven by the increasing demand for immersive e-commerce experiences and the need to reduce product return rates. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key trends, including the rising adoption of augmented reality (AR) and virtual reality (VR) technologies, the increasing sophistication of 3D modeling software, and the growing preference for online shopping across various sectors. Key application areas include fashion, jewelry, furniture, automotive, and home décor, with cloud-based solutions gaining significant traction due to their scalability and accessibility. The competitive landscape is populated by a mix of established players and emerging startups, each offering specialized solutions to cater to diverse customer needs. Geographic expansion is also driving market growth, with North America and Europe currently dominating the market share but significant opportunities emerging in Asia-Pacific due to rapid e-commerce adoption. Growth constraints include the high initial investment costs associated with implementing 3D visualization technologies, the need for specialized technical expertise, and concerns regarding data security and privacy. However, these challenges are being addressed through the development of more user-friendly software, the emergence of affordable solutions, and the increasing awareness of data security best practices. The market segmentation reveals a strong preference for cloud-based solutions owing to their flexibility and cost-effectiveness compared to on-premises deployments. Further growth is expected to be driven by ongoing innovation in rendering techniques, improved interoperability between different software platforms, and the integration of 3D visualization with other e-commerce technologies, such as artificial intelligence (AI)-powered product recommendations and personalized shopping experiences.

Explore the booming Data Visualization market! Discover key insights, growth drivers, market size estimations, and CAGR trends for 2025-2033. Understand applications, types, and leading companies in this essential business intelligence sector.

Funnel Analytics for E-commerce Market Outlook

According to our latest research, the global funnel analytics for e-commerce market size reached USD 1.27 billion in 2024, with a robust CAGR of 15.4% expected through the forecast period. By 2033, the market is projected to reach USD 4.39 billion. This significant growth is primarily driven by the surging adoption of data-driven decision-making tools among e-commerce businesses, as well as the increasing need for precise customer journey mapping and conversion optimization across digital retail platforms.

One of the primary growth factors for the funnel analytics for e-commerce market is the exponential rise in global e-commerce transactions, fueled by the proliferation of digital payment solutions, mobile commerce, and the rapid digitalization of retail operations. As online competition intensifies, businesses are compelled to leverage advanced analytics to dissect and optimize every stage of the purchase funnel, from initial engagement to final conversion. This necessity is further accentuated by the growing expectations of consumers for personalized experiences and seamless buying journeys. As a result, e-commerce brands are investing heavily in funnel analytics solutions to gain actionable insights, reduce drop-off rates, and maximize the lifetime value of each customer.

Another critical driver is the technological advancements in artificial intelligence (AI), machine learning (ML), and big data analytics, which have significantly enhanced the capabilities of funnel analytics platforms. Modern solutions now offer predictive analytics, real-time data visualization, and automated recommendations, enabling e-commerce enterprises to swiftly identify bottlenecks and optimize their marketing strategies. The integration of funnel analytics with other digital tools, such as customer relationship management (CRM) and marketing automation platforms, is further amplifying the value proposition, making it easier for businesses to orchestrate omnichannel campaigns and measure their impact holistically. This technological synergy is fostering widespread adoption among both large enterprises and small and medium-sized businesses.

The demand for funnel analytics in e-commerce is also being propelled by the increasing focus on regulatory compliance and data privacy. As regulations such as GDPR and CCPA mandate stricter controls over consumer data, e-commerce companies are seeking analytics solutions that not only deliver deep insights but also ensure secure and compliant data processing. Vendors are responding by embedding advanced security features and privacy-by-design principles into their offerings, which is enhancing trust and accelerating market penetration. Moreover, the COVID-19 pandemic has permanently altered consumer behavior, pushing more transactions online and compelling retailers to double down on digital analytics to stay competitive in the evolving landscape.

In addition to funnel analytics, Web Analytics plays a crucial role in understanding the broader digital landscape for e-commerce businesses. By leveraging Web Analytics, companies can gain insights into overall website performance, user behavior, and traffic sources. This comprehensive view allows businesses to identify trends, optimize website content, and enhance user experience, ultimately driving higher engagement and conversion rates. The integration of Web Analytics with funnel analytics provides a more holistic approach to data-driven decision-making, enabling e-commerce brands to refine their strategies and achieve sustainable growth in a competitive market.

From a regional perspective, North America continues to dominate the funnel analytics for e-commerce market, accounting for the largest revenue share in 2024 due to the presence of major technology providers and a mature e-commerce ecosystem. However, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of online retail, increasing internet penetration, and a burgeoning middle-class population with rising disposable incomes. Europe also represents a significant market, characterized by a high level of digital adoption and stringent data protection standards. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by the digital transformation initiatives o

The dataset captures customer satisfaction scores for a one-month period at an e-commerce platform called Shopzilla (a pseudonym). It includes various features such as category and sub-category of interaction, customer remarks, survey response date, category, item price, agent details (name, supervisor, manager), and CSAT score etc.
Note: Please be advised that the authentic information has been obfuscated, and the dataset has been fabricated using the Faker library to ensure the concealment of genuine details
Dataset Information:
Rows: 85,907
Columns: 20
Usage:
This dataset serves as a valuable resource for conducting Exploratory data analysis (EDA), Visualization, and Machine Learning Classification tasks pertaining to customer service performance evaluation, satisfaction forecasting, and customer behavior analysis within the e-commerce sector.

Do explore pinned 📌 notebook under code section for quick EDA📊 reference
Consider an upvote ^ if you find the dataset useful

Data Description:

Discover the booming market for funnel visualization tools! Learn about its $1.5 billion (2025) valuation, 15% CAGR, key players (Google, Funnelytics, more), and future trends shaping this dynamic industry. Optimize your marketing strategy with this insightful market analysis.

The Ecommerce transaction analysis is one of great way to learn data visualization with Power BI or Tableau. Your visualization must reveals customer sales, product sales, regional sales, monthly sales, time of the day sales to gain valuable insights and business planning. You may use Combo Charts, Cards, Bar Charts, Tables, or Line Charts; for the customer segmentation page, you could employ Column Charts, Bubble Charts, Point Maps, Tables, etc.