This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Advertisement Sales Dataset

The Advertisement Sales dataset is a collection of data points used to analyze the impact of advertising on sales. This dataset consists of 200 entries, each representing a unique observation with data on various types of media advertising and corresponding sales figures.

Key Features: ID: A unique identifier for each observation. TV: The amount of money spent on TV advertising (in thousands of dollars). Radio: The amount of money spent on Radio advertising (in thousands of dollars). Newspaper: The amount of money spent on Newspaper advertising (in thousands of dollars). Sales: The sales figures for the product (in thousands of units).

Summary Statistics: TV advertising: Ranges from $0.7k to $296.4k, with an average spend of $147.03k. Radio advertising: Ranges from $0k to $49.6k, with an average spend of $23.29k. Newspaper advertising: Ranges from $0.3k to $114k, with an average spend of $30.55k. Sales: Ranges from 1.6k to 27k units, with an average of 14.04k units.

Use Cases: Advertising Strategy: Businesses can use this dataset to understand the effectiveness of different advertising channels (TV, Radio, Newspaper) on sales performance. Predictive Modeling: Analysts can build predictive models to forecast sales based on advertising spend across different media.

ROI Analysis: Marketers can calculate the return on investment (ROI) for each advertising channel to optimize their budgets. Correlation Studies: Researchers can study the correlation between advertising spend and sales to derive insights on consumer behavior.

Potential Analyses: Regression Analysis: Determine how changes in advertising budgets influence sales. Comparative Analysis: Compare the effectiveness of different advertising mediums. Trend Analysis: Identify trends in advertising spending and sales performance over time.

This dataset provides a robust foundation for exploring the relationships between advertising expenditures and sales outcomes, enabling data-driven decision-making for marketing strategies. ​

This dataset contains comprehensive sales data that can be used for analysis, visualization, and modeling. It includes key attributes such as:

order_id: Unique identifier for each order. product: Name of the product sold. quantity_ordered: Quantity of the product purchased in each transaction. price_each: Price of a single unit of the product. order_date: Date and time when the order was placed. purchase_address: Full address of the purchase, including street, city, and state.

Potential Use Cases Sales Analysis: Identify trends in product performance and seasonal demand. Revenue Insights: Analyze total and per-unit revenue across products or cities. Geographical Analysis: Discover top-performing cities and regions. Time-Based Trends: Analyze monthly sales trends and patterns. Machine Learning Applications: Build predictive models for sales forecasting or customer segmentation.

This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

The Online Sales Dataset provides a detailed overview of global online sales transactions across various product categories. It includes transaction details such as order ID, date, product category, product name, quantity, unit price, total price, region, and payment method.

About Datasets:

Domain : Sales Project: Coca Cola Sales Analysis Datasets: Power BI Dataset vF Dataset Type: Excel Data Dataset Size: 52k+ records

KPI's: 1. Analyze Profit Margins per Brand 2. Sales by Region 3. Price per unit 4. Operating Profit 5. Additional Analysis

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains Power Query, Q&A visual, Key influencers visual, map chart, matrix, dynamic timeline, dashboard, formatting, text box.

Snapshot img

Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

What will be the Size of the Sales Intelligence Market during the forecast period?

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer persona deve

Analysis of ‘Sales data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mysarahmadbhat/sales-data on 27 August 2021.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Description: Explore a comprehensive dataset of e-commerce sales, encompassing a variety of product categories, pricing, customer reviews, and sales trends over the past year. This dataset is ideal for analyzing market trends, customer behavior, and sales performance. Explore into the data to uncover insights that can optimize product listings, pricing strategies, and marketing campaigns.

Columns:

product_id: Unique identifier for each product. product_name: Name of the product. category: Product category. price: Price of the product. review_score: Average customer review score (1 to 5). review_count: Total number of reviews. sales_month_1 to sales_month_12: Monthly sales data for each product over the past year. Potential Analyses:

Identify top-performing product categories. Analyze the impact of pricing on sales and customer reviews. Discover seasonal sales trends and patterns. Evaluate customer satisfaction based on review scores and counts.

This is a dataset, containing different products sold in different countries, the profits, cost, revenue that it brought It also illustrates different customers that bought the goods as well as the age of people it help the company able to forecast demand of different products in different areas

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

The Sales Data Fusion market is poised for significant expansion, projected to reach an estimated $25,000 million in 2025, driven by a robust Compound Annual Growth Rate (CAGR) of 18% through 2033. This burgeoning growth is primarily fueled by the increasing demand for unified customer views across diverse data sources to enhance sales strategies, personalize customer interactions, and optimize marketing spend. Organizations are increasingly recognizing the strategic imperative of consolidating disparate sales, marketing, and customer service data to gain a holistic understanding of their clientele. This fusion empowers businesses to identify high-value customer segments, predict purchasing behavior, and deliver targeted interventions, ultimately leading to improved conversion rates and customer lifetime value. Key market drivers include the proliferation of digital customer touchpoints, the escalating volume and complexity of sales-related data, and the growing adoption of AI and machine learning for advanced analytics. The market is segmented into two primary application categories: Large Enterprises and Small and Medium Enterprises (SMEs). While large enterprises, with their extensive data repositories and sophisticated analytical needs, represent a substantial share, the adoption within SMEs is rapidly accelerating due to the availability of more accessible and affordable data fusion solutions. The market is further categorized by service types: Managed Services and Professional Services. Managed services are gaining traction as businesses seek to outsource the complexities of data integration and ongoing management, while professional services cater to specialized implementation and strategic consulting needs. Geographically, North America is expected to lead the market in 2025, followed closely by Asia Pacific, which is exhibiting strong growth potential due to rapid digitalization and increasing data-driven initiatives. Europe also presents a significant market, with countries like the United Kingdom and Germany at the forefront of data fusion adoption. This report offers a comprehensive analysis of the Sales Data Fusion market, examining its current landscape and projecting its trajectory through 2033. It delves into the intricate dynamics of data integration and utilization for sales enhancement, providing actionable insights for stakeholders. The study encompasses a historical period from 2019 to 2024, a base year of 2025 for estimation, and a forecast period from 2025 to 2033. The global Sales Data Fusion market is expected to witness robust growth, with estimations pointing towards a significant increase in revenue, potentially reaching millions of units by the end of the forecast period.

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision Europe includes consumer transaction data on 6.7M+ credit cards, debit cards, direct debit accounts, and direct transfer accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 5K+ merchants, 3K+ brands mapped to 600 global parent companies (500 publicly traded), and deep geographic breakouts with demographic breakouts coming soon for UK. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 5 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Blinkit Sales Dataset: Analyzing Online Grocery Trends

This dataset contains sales transaction data from Blinkit, an online grocery delivery platform. It provides valuable insights into customer purchasing behavior, product demand, revenue trends, and sales performance over time.

Dataset Overview:

• Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps.
• Useful for demand forecasting, price optimization, trend analysis, and business insights.
• Helps in understanding customer behavior and seasonal variations in online grocery shopping.

Potential Use Cases:

• Time Series Analysis: Analyze sales trends over different periods.
• Demand Forecasting: Predict future product demand based on historical data.
• Price Optimization: Identify the impact of pricing on sales and revenue.
• Customer Behavior Analysis: Understand buying patterns and preferences.
• Market Trends: Explore how different factors affect grocery sales performance.

This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects.

The Department of Finance (DOF) maintains records for all property sales in New York City, including sales of family homes in each borough. This list is a summary of all neighborhood sales for Class 1-, 2- and 3-Family homes Citywide. This list includes all sales of 1-, 2-, and 3-Family Homes', whose sale price is equal to or more than $150,000. The Building Class Category for Sales is based on the Building Class at the time of the sale.

The global sales engagement platform software market size was valued at 1,807.9 million in 2025 and is projected to grow at a CAGR of 12.2% from 2025 to 2033. The increasing adoption of cloud-based software, the growing need for sales automation, and the rising demand for personalized customer experiences drive market growth. The on-premises segment held the largest market share in 2025 owing to its high security and data control features. However, the cloud-based segment is expected to witness significant growth during the forecast period due to its lower cost, scalability, and flexibility. North America was the largest regional market in 2025 and is anticipated to maintain its dominance over the forecast period. The region's early adoption of advanced technologies, the presence of major vendors, and the existence of a large number of small and medium-sized businesses in the region contribute to its dominance. The Asia-Pacific region is expected to grow at a substantial CAGR during the forecast period due to the increasing penetration of mobile devices, the growing number of internet users, and the presence of a large population of young people with disposable income. Blueboard, VanillaSoft, SalesLoft, Reply App, and SalesHandy are some prominent companies in the Sales Engagement Platform Software market.

The pharmaceutical industry is undergoing a digital transformation, driving significant growth in the demand for sophisticated forecasting software. This market, currently estimated at $2 billion in 2025, is projected to experience robust expansion, with a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the increasing complexity of drug development and regulatory processes necessitates more accurate and timely demand forecasting. Secondly, the growing adoption of data-driven decision-making within pharmaceutical companies is pushing the need for advanced analytics and forecasting capabilities. Thirdly, the emergence of new technologies such as AI and machine learning are enhancing forecasting accuracy and efficiency, leading to greater market adoption. Major players like IQVIA, SAS Institute, and Oracle are already well-established, but the market also presents opportunities for smaller, specialized firms offering niche solutions. The market's growth is not without challenges. Stringent regulatory compliance requirements and the high cost of implementation can act as barriers to entry for some organizations. Furthermore, data security concerns and the need for robust data integration across various systems pose significant hurdles. However, the overall trajectory remains positive, driven by the industry's constant pursuit of efficiency and optimization. Segmentation within the market includes solutions catering to different organizational sizes and specific needs within the pharmaceutical supply chain (e.g., demand planning, sales forecasting, and inventory management). Regional growth will likely be driven by increased adoption in North America and Europe initially, followed by expansion in Asia-Pacific and other emerging markets as these regions further develop their pharmaceutical sectors and digital infrastructures.