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

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. ​

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.

Sales Analytics Software Market Outlook

The global sales analytics software market size was valued at approximately USD 3.4 billion in 2023 and is projected to reach around USD 10.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.8% during the forecast period. The growth of this market is being driven by several factors, including the increasing adoption of advanced analytics platforms by organizations to gain competitive advantages and the rising demand for data-driven decision-making processes.

One of the primary growth factors for the sales analytics software market is the increasing emphasis on data-driven decision-making within enterprises. Modern businesses are increasingly leveraging big data and analytics to gain insights into customer behavior, sales trends, and market dynamics. This data-centric approach allows companies to make informed decisions, optimize sales strategies, and improve overall business performance. As a result, the demand for sophisticated sales analytics tools is on the rise, propelling the market forward.

Additionally, the rapid digitization across various industries is significantly contributing to the market growth. With the proliferation of digital channels and e-commerce platforms, companies now have access to vast amounts of data generated from online transactions, customer interactions, and social media activities. Sales analytics software helps organizations to sift through this data, identify trends, and derive actionable insights. This capability is particularly crucial in the retail and e-commerce sectors, where understanding consumer preferences and buying patterns can directly impact sales and profitability.

Another crucial growth factor is the increasing integration of artificial intelligence (AI) and machine learning (ML) technologies within sales analytics solutions. These advanced technologies enhance the predictive and prescriptive capabilities of sales analytics software, enabling businesses to anticipate market trends, forecast sales performance, and personalize customer experiences. As AI and ML continue to evolve, their integration into sales analytics tools is expected to further drive market growth by providing more accurate and actionable insights.

Data Analytics Software plays a pivotal role in the evolution of sales analytics solutions. As organizations strive to harness the power of data, the integration of comprehensive data analytics software becomes essential. These tools not only facilitate the analysis of vast datasets but also enable businesses to derive meaningful insights that drive strategic decision-making. By leveraging data analytics software, companies can enhance their understanding of market trends, customer behaviors, and sales performance, leading to more informed business strategies. This integration is particularly beneficial in industries with complex sales processes, where the ability to quickly analyze and interpret data can provide a competitive edge.

From a regional perspective, North America is expected to hold a significant share of the sales analytics software market. The region's dominance can be attributed to the presence of major technology companies, high adoption rates of advanced analytics solutions, and a robust digital infrastructure. Furthermore, the increasing focus on improving customer experiences and the willingness of enterprises to invest in innovative technologies are likely to sustain the market's growth in this region.

Component Analysis

The sales analytics software market can be segmented by component into software and services. The software segment encompasses various types of analytics software, including descriptive, diagnostic, predictive, and prescriptive analytics tools. These tools help organizations to analyze historical sales data, identify patterns, and predict future sales trends. The growing need for real-time analytics and the ability to integrate these tools with existing CRM systems are driving the demand for sales analytics software.

Within the software segment, cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness. Cloud-based sales analytics software allows businesses to access data and insights from anywhere, facilitating remote work and collaboration. Additionally, the continuous advancements in cloud technology, such as enhanced security features and increased storage capabilitie

The technology scouting software market is projected to be valued at $3.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $9.7 billion by 2034.

• What makes our data unique? Our datasets cover the entirety of individuals' data: be it data from social media, e-commerce, health, fitness tracker, browser, on-chain wallet, cell phone gprs or meta. A unique allocation is already made when the data is collected by the respective owner of the data.

• How is our data sourced? The data is obtained directly from the respective platform or the respective original data collector, either via a manual download/upload process or by granting permission for real-time data retrieval via an api interface.

• What are primary use-cases? There are no limits to the possibilities for analysis through our in-house analysis platform. The process takes place in three basic and a potential fourth additional step.

1. Filter for the necessary data sets for your own analysis, based on the desired criteria (e.g. purchases of product X on Amazon, age, region & gender in combination with fitness metrics at level Y).

2. Display how many data sets with the desired criteria are available for an analysis.

3. Create an individual analysis to e.g. get a market segmentation or target persona for this specific customer group

4. All users of the WeDataNation app automatically receive a wallet to manage the revenue share and other tokens held on it. Direct airdrops can be made for coupons or other marketing campaigns, thus enabling direct interaction with the target group that has filtered and analyzed itself. This enables 100% traceability and reduces costs through lower scattering losses in executed marketing campaigns.

• Process of the analysis The entire analysis is carried out in compute-to-data model, whereby the original data is only available in anonymized form and the privacy of the owners of the data, who participate in the sales via opt-in models, is thereby guaranteed. This generates a high level of willingness and consent among potential data suppliers to provide data.

• How does this Data Product fit into our broader data offering? The market entry is focused on europe. However, data is collected worldwide and an analysis is theoretically also possible for sub-regions of the eu or other regions worldwide. Our in-house analysis platform can also be used for this purpose.

• Status of product development? The development of the analysis platform is currently being carried out in close consultation with potential interested users.

• Are you interested in our service? Contact us via Partner@WeDataNation.io and become an early partner too, so that we can take your needs into account when developing the functionalities of our platform.

Comprehensive market data and analytics for VOID - MEDIUM PLAY MTG INVASION including pricing distribution, seller metrics, and market trends.

This dataset is part of a Sales Analytics Power BI Project based on over 10,000 rows of transactional data. It was designed to help users practice and demonstrate skills relevant to Microsoft’s PL-300 Power BI Data Analyst Associate certification.

The dataset includes data related to:

📅 Sales Transactions (with dates and revenue)

🧾 Orders (order count, customer ID)

💰 Profit & Cost

📦 Products and Categories

🧍 Customers

All visuals and metrics were built in Power BI Desktop, including KPIs, time intelligence, and dynamic filters

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Sales Performance Management Market size was valued at USD 2.25 Billion in 2024 and is projected to reach USD 6.41 Billion by 2032, growing at a CAGR of 14.00% from 2026 to 2032. The sales performance management (SPM) market is driven by the need for organizations to optimize sales processes, enhance productivity, and achieve revenue targets through effective performance tracking and coaching. With the increasing complexity of sales operations, companies are adopting SPM solutions to streamline compensation planning, set achievable quotas, monitor performance metrics, and foster sales talent development. The rise of data analytics, artificial intelligence (AI), and machine learning enables these platforms to deliver deeper insights into sales patterns and individual performance, supporting more informed decision-making and strategic adjustments. Furthermore, the shift towards remote work and hybrid sales models has amplified the demand for tools that facilitate transparency, improve team collaboration, and motivate sales personnel in distributed environments. Compliance needs, especially for regulated industries, also play a role in driving adoption, as SPM solutions provide structured, auditable frameworks to meet regulatory standards.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Advanced Health Intelligence (AHI): Revolutionizing Healthcare through AI and Data Analytics?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Global CRM Analytics Market Size Was Worth USD 9.85 Billion in 2023 and Is Expected To Reach USD 27.72 Billion by 2032, CAGR of 12.18%.

Global Spend Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision EUR is an aggregated transaction feed that includes consumer transaction data on 6.7M+ Europe-domiciled payment accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 4.4K+ brands and 620 symbols including 490 public tickers. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

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

This data sample illustrates how Consumer Edge data can be used to understand a company’s growth by country for a specific time period (Ex: What was McDonald’s year-over-year growth by country from 2019-2020?)

Inquire about a CE subscription to perform more complex, near real-time global spend analysis functions on public tickers and private brands like: • Analyze year-over-year spend growth for a company for a subindustry by country • Analyze spend growth for a company vs. its competitors by country through most recent time

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Global Spend Analysis

Problem A global retailer wants to understand company performance by geography to identify growth and expansion opportunities.

Solution Consumer Edge transaction data can be used to analyze shopper behavior across geographies and track: • Growth trends by country vs. competitors • Brand performance vs. subindustry by country • Opportunities for product and location expansion

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key growth drivers by geography for company-wide reporting • Refine strategy in underperforming geographies, both online and offline • Identify areas for investment and expansion by country • Understand how different cohorts are performing compared to key competitors

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

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

Success.ai’s Identity Data for Media Executives in North America provides a robust dataset tailored for organizations looking to connect with key decision-makers in the media industry. Covering roles such as CEOs, content directors, producers, and senior executives, this dataset offers verified LinkedIn profiles, professional histories, and actionable contact details.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, marketing, and business strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution enables you to thrive in the competitive and dynamic North American media landscape.

Why Choose Success.ai’s Identity Data?

1. Verified LinkedIn Profiles for Precision Engagement

   • Access verified LinkedIn profiles, professional histories, and contact details for media executives across North America.
   • AI-driven validation ensures 99% accuracy, reducing inefficiencies and improving communication outcomes.

2. Comprehensive North American Coverage

   • Includes executives from leading media hubs such as New York, Los Angeles, Toronto, and Chicago.
   • Gain insights into regional media trends, audience engagement strategies, and organizational shifts.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, professional affiliations, and media market dynamics.
   • Stay aligned with industry trends to capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access LinkedIn profiles for media executives and professionals across North America.
• Leadership and Professional Insights: Connect with CEOs, directors, and content strategists driving media innovation.
• Verified Contact Details: Gain work emails, phone numbers, and decision-maker profiles for precise targeting.
• Professional Histories: Understand career trajectories, expertise, and organizational contributions to guide your outreach.

Key Features of the Dataset:

1. Comprehensive Media Executive Profiles

   • Identify and connect with decision-makers overseeing content strategies, audience engagement, and digital transformations in the media sector.
   • Target leaders responsible for advertising, programming, and innovation in media and entertainment.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by media type (broadcast, digital, print), geographic location, or job function.
   • Tailor campaigns to align with specific needs, such as promoting technology adoption or forming strategic partnerships.

3. Regional and Sector-specific Insights

   • Leverage data on North American media consumption trends, digital innovations, and organizational challenges.
   • Refine strategies to align with industry priorities and regional demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Media Outreach

   • Promote media technologies, advertising solutions, or consulting services to media executives in North America.
   • Use verified contact data for multi-channel outreach, including email, phone, and LinkedIn.

2. Partnership Development and Collaboration

   • Build relationships with media companies, production studios, and digital platforms exploring strategic alliances.
   • Foster collaborations that enhance visibility, content distribution, or audience engagement.

3. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers seeking executives for leadership roles in media, content creation, or digital strategy.
   • Provide workforce optimization tools or training solutions tailored to the media industry.

4. Market Research and Competitive Analysis

   • Analyze North American media trends, audience engagement metrics, and content strategies to refine offerings.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality identity data at competitive prices, ensuring strong ROI for your outreach, marketing, and strategic initiatives.

2. Seamless Integration

   • Integrate verified LinkedIn data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost engagement rates in campaigns.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus o...

Sales Performance Analytics Market Outlook

According to our latest research, the global sales performance analytics market size in 2024 stands at USD 5.8 billion, with a robust compound annual growth rate (CAGR) of 13.2% projected through 2033. By the end of the forecast period, the market is expected to reach a value of USD 17.1 billion. The primary growth factor driving this expansion is the increasing adoption of data-driven decision-making processes across various industries, which is significantly enhancing the demand for advanced sales performance analytics solutions worldwide.

The growth of the sales performance analytics market is being propelled by the rising need for organizations to optimize their sales processes and improve overall revenue generation. Businesses are increasingly leveraging analytics to gain actionable insights into sales trends, customer behavior, and sales team performance. The integration of artificial intelligence (AI) and machine learning (ML) technologies into analytics platforms is further enabling organizations to predict sales outcomes, identify high-value leads, and streamline sales operations. As companies strive to remain competitive in a rapidly evolving digital landscape, the ability to harness real-time data for strategic decision-making has become a critical differentiator, fueling the adoption of sales performance analytics solutions globally.

Another significant driver for the market is the growing emphasis on customer-centric strategies across industries such as BFSI, retail & e-commerce, and healthcare. Organizations are increasingly focusing on understanding customer preferences, purchasing patterns, and engagement levels to tailor their sales approaches and maximize customer lifetime value. Sales performance analytics platforms provide granular customer insights, enabling businesses to personalize interactions, anticipate customer needs, and improve customer satisfaction. This shift towards customer-centric sales strategies is not only enhancing sales effectiveness but also contributing to higher retention rates and increased profitability, thereby boosting market growth.

Furthermore, the proliferation of cloud computing and mobile technologies is facilitating the widespread adoption of sales performance analytics solutions. Cloud-based platforms offer scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to organizations of all sizes, including small and medium enterprises. The ability to access real-time sales data from any location and device empowers sales teams to make informed decisions on-the-go, driving agility and responsiveness in sales operations. Additionally, the integration of sales analytics with other enterprise systems such as CRM and ERP is streamlining workflows and enhancing the overall value proposition for end-users, further accelerating market expansion.

From a regional perspective, North America continues to dominate the sales performance analytics market, accounting for the largest share in 2024, driven by the high adoption of advanced technologies, strong presence of key market players, and a mature digital infrastructure. However, the Asia Pacific region is witnessing the fastest growth, with a CAGR exceeding 15% during the forecast period, fueled by rapid digitalization, increasing investments in analytics solutions, and the burgeoning e-commerce sector. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by rising awareness of the benefits of sales analytics and the ongoing digital transformation initiatives across various industries.

Component Analysis

The component segment of the sales performance analytics market is bifurcated into software and services, both of which play pivotal roles in shaping the industry landscape. The software segment encompasses a wide