This dataset explores the relationship between advertising expenditures across various channels (TV, radio, and newspaper) and sales performance. It provides insights into how different types of advertising spending impact product sales, allowing for data-driven analysis of marketing effectiveness. This dataset is commonly used for linear regression analysis to determine the influence of each advertising channel on sales outcomes.

Dataset Overview:

TV Advertising Spend: Amount spent on TV advertisements for a given period. Radio Advertising Spend: Amount spent on radio advertisements. Newspaper Advertising Spend: Amount spent on newspaper advertisements. Sales: Total sales generated within the same period, serving as the target variable. Columns:

TV: Advertising budget allocated to TV in thousands of dollars. Radio: Advertising budget allocated to radio in thousands of dollars. Newspaper: Advertising budget allocated to newspapers in thousands of dollars. Sales: Product sales in thousands of units, which is the outcome variable to be predicted. Possible Use Cases:

Marketing Spend Analysis: Determine which advertising channel has the greatest impact on sales. Sales Prediction: Use linear regression to predict sales based on advertising spend across different channels. Channel Effectiveness: Compare the effectiveness of each advertising channel and optimize future marketing budgets. Business Strategy: Identify trends in sales based on historical advertising spending to inform business decisions. This dataset is ideal for students, data analysts, and marketing professionals interested in understanding the impact of advertising on sales performance. It offers a simple structure suitable for exploratory data analysis (EDA), regression modeling, and predictive analysis in marketing.

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Description:

The advertising dataset captures the sales revenue generated with respect to advertisement costs across multiple channels like radio, tv, and newspapers.

It is required to understand the impact of ad budgets on the overall sales.

Acknowledgement:

The dataset is taken from Kaggle

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

In the first half of 2021, Chinese online fast fashion retailer Shein invested ** million U.S. dollars in digital advertising in the United States. Swedish H&M spent ** million on online ads in the same period. The only beauty brand in the data set, Ulta Beauty, which is a U.S. chain of beauty stores, had a digital ad expenditure of ** million. The company's total ad expenses stood at ***** million in the fiscal year 2020.

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. ​

Success.ai’s Consumer Marketing Data for Food, Beverage & Consumer Goods Professionals Globally provides a comprehensive dataset tailored for businesses seeking to connect with decision-makers and marketing professionals in these dynamic industries. Covering roles such as brand managers, marketing strategists, and product developers, this dataset offers verified contact details, decision-maker insights, and actionable business data.

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3. Data Acc...

During a 2024 survey among marketers worldwide, approximately 83 percent selected increased exposure as a benefit of social media marketing. Increased traffic followed, mentioned by 73 percent of the respondents, while 65 percent cited generated leads.

              The multibillion-dollar social media ad industry

              Between 2019 – the last year before the pandemic – and 2024, global social media advertising spending skyrocketed by 140 percent, surpassing an estimated 230 billion U.S. dollars in the latter year. That figure was forecast to increase by nearly 50 percent by the end of the decade, exceeding 345 billion dollars in 2029. As of 2024, the social media networks with the most monthly active users were Facebook, with over three billion, and YouTube, with more than 2.5 billion.

              Pros and cons of GenAI for social media marketing

              According to another 2024 survey, generative artificial intelligence's (GenAI) leading benefits for social media marketing according to professionals worldwide included increased efficiency and easier idea generation. The third place was a tie between increased content production and enhanced creativity. All those advantages were cited by between 33 and 38 percent of the interviewees. As for GenAI's top challenges for global social media marketing,
              maintaining authenticity and the value of human creativity ranked first, mentioned by 43 and 40 percent of the respondents, respectively. Another 35 percent deemed ensuring the content resonates as an obstacle.

This dataset is also known as Exceptions to cross-government moratoria on spend or exceptions to spending controls. From Q2 (Finance year 2020-21) BEIS will only publish a single csv format file with mandated details. Files previously released list items of spend that have been allowed via the Cabinet Office Approval process by departments since the announcement (24 May 2010) of six cross-government moratoria: ICT spend above £1m; Advertising and marketing; Consultancy; Commercial; Property leases & lease extensions; and Civil Service recruitment. Exceptions to spend requests may not be required for each sector every quarter by the Department. Datasets for Departments for Energy and Climate Change; and Business, Innovation and Skills are available as separate entity names.

Norway number dataset will help you generate sales leads. At the present time, people can create text with product info and descriptions and send customers through this lead. Further, running a marketing campaign is necessary for business success. Similarly, you can directly call and message with the help of this Norway number dataset. Also, the Norway number dataset is important to let your audience know of the features and uses of your product. Most importantly, by doing it anyone can know your services or products true value. Hence, everybody can create a bond with the audience and earn their trust with this mobile cell phone number lead. Norway phone data has the potential to get valuable customers. As a dealer will be able to exhibit your products to a large client base without spending too much on ads. The use of an SMS marketing plan has made this attainable to run promotions cheaply here. So, take the contact number directory and try the database for your service. Norway phone data will sustain your telemarketing with useful details. Mostly, if you need to reach anybody as soon as possible, then the cell phone number is the best option. Similarly, with this library, you can instantly send messages to their inbox. Accordingly, the numbers on our Norway phone data will aid your marketing efforts greatly. You can utilize the List To Data website for your product publicity so that you can find curious buyers among them. Norway phone number list is a top-notch mobile database. Also, our List To Data website is obstinate about giving our clients the best service for their money. Especially, we have organized a 24/7 active customer support team to confirm that. Thus, people can ask them anything about this database package, or even get 95% accurate samples of the library from them. Both your branding and sales will improve with this Norway phone number list. Even, make the right conclusion for your business and order this lead right now. Further, the Norway phone number list will let you continue to boost your products all across the country. The user count of these venues is so big that even that delivers you such a big customer base. Indeed, this will surely increase the possibility of finding interested clients for any brand and services.

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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

Digital Marketing Analytics

This dataset provides an in-depth look at customer interactions and campaign performance within the digital marketing realm. It includes key metrics and demographic information that are crucial for analyzing marketing effectiveness and customer engagement. The dataset comprises the following columns:

Customer Id:

Unique identifier for each customer, facilitating individual tracking and analysis.

Age:

Customer's age, offering insights into demographic segmentation and targeting strategies.

Gender:

Customer's gender, useful for understanding gender-based preferences and behavior.

Income:

Customer's income level, providing context on purchasing power and conversion potential.

Campaign Channel:

The medium through which the marketing campaign was delivered (e.g., email, social media).

Campaign Type:

The nature of the marketing campaign (e.g., promotional offer, product launch), helping to assess campaign effectiveness.

Ad Spend:

Amount spent on advertisements, crucial for evaluating cost-efficiency and ROI.

Click Through Rate (CTR):

Ratio of clicks to impressions, indicating ad engagement and effectiveness.

Conversion Rate:

Percentage of users who complete a desired action after interacting with an ad, reflecting the success of the campaign in driving actual sales or goals.

Website Visit:

Number of visits to the website by the customer, measuring engagement and interest.

This dataset is ideal for exploring customer behavior, optimizing marketing strategies, and evaluating the success of various campaigns. Perfect for data scientists and marketers looking to derive actionable insights from digital marketing data.

Please refer to the "README.rtf" and "Data Details for Replication.pdf" files.Paper AbstractSocial media platforms have become vital channels for businesses to reach consumers through advertising. But in the U.S., the digital advertising market in which these platforms operate is dominated by a few major players, raising concerns for antitrust regulators. In such a concentrated market, the entry or exit of a single platform can reallocate billions in ad spending, affecting businesses and users. TikTok's temporary suspension in the U.S. in January 2025 provides a unique natural experiment to examine how the removal of a major player would shift advertising demand and supply on competitors, specifically Facebook and Instagram, revealing the degree of substitutability across platforms and the intensity of competition. Using a difference-in-differences approach comparing advertising activity in the U.S. to other countries, we find that Meta ad volume and spend rose by 6.3% and 22.4%, as a result of the outage, without a corresponding increase in ad impressions. Consequently, Meta ad prices, as measured by cost per thousand impressions, jumped by 12.1%. Shifts in demand were three times greater for larger advertisers relative to smaller ones, suggesting that Meta platforms and TikTok are closer substitutes for larger firms and that a TikTok ban would therefore impose greater challenges on smaller businesses.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

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4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

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manager email list is affordable and easy to use in any CRM system. Thus, you save time, reduce marketing costs, and target the right audience effectively. So, by choosing our service, you get the correct information that truly helps your business succeed. Therefore, whether you run a small company or a large enterprise, you can benefit. As a result, your marketing can bring better leads, higher engagement, and increased profits. In conclusion, our risk managers email lists give you the right contacts for the right opportunities. So, by choosing our service, you invest in accurate information that helps your business grow and succeed. It is available now at List to Data. Risk manager email database provides the best solution for your business. It allows you to easily reach out to risk managers all over the world. Most importantly, it helps you expand your business and find new clients. We ensure our database is highly reliable by verifying all the information we collect. That makes your marketing efforts much more effective. With the email addresses in this database, you can directly inform risk managers about your products or services. Furthermore, you can easily get our database in either Excel or CSV file format. Moreover, this makes it simple to use the data according to your specific needs. In conclusion, purchasing our dataset will help your business grow faster. We are always committed to providing you with the best possible service.

Sales Prediction Dataset The dataset provided contains information about the advertising expenditures of a company on various platforms (TV, Radio, newspapers) and the corresponding sales of a product. Here's an explanation of the dataset:

1. TV: This column represents the amount of money spent on advertising the product on television. TV advertising is a traditional and widely used medium for reaching a broad audience.

2. Radio: This column indicates the advertising expenditure on radio. Radio advertising is known for its ability to target specific demographics and local audiences.

3. Newspaper: This column shows the advertising cost spent on newspaper advertising. Newspaper advertising is often used for targeting specific geographic regions or demographics.

4. Sales: This column represents the number of units sold corresponding to the advertising expenditures on TV, Radio, and newspapers.

Questions: 1. What is the average amount spent on TV advertising in the dataset? 2. What is the correlation between radio advertising expenditure and product sales? 3. Which advertising medium has the highest impact on sales based on the dataset? 4. Plot a linear regression line that includes all variables (TV, Radio, Newspaper) to predict Sales, and visualize the model's predictions against the actual sales values. 5. How would sales be predicted for a new set of advertising expenditures: $200 on TV, $40 on Radio, and $50 on Newspaper? 6. How does the performance of the linear regression model change when the dataset is normalized? 7. What is the impact on the sales prediction when only radio and newspaper advertising expenditures are used as predictors?

This dataset explores the relationship between advertising expenditure and the corresponding sales quantity of a product.

It consists of two columns: Advertising Costs : The amount spent on advertising campaigns for the product, measured in thousands. Value range: 1-10 thousand. Sales Quantity (units): The number of units of the product sold during the corresponding advertising campaign period. Value range: 100-1000 units.

Purpose:

This dataset is suitable for training and evaluating linear regression models. It can be used for:

• Predicting sales quantity based on advertising costs.
• Analyzing the correlation between advertising expenditure and sales performance.
• Learning fundamental machine learning concepts, particularly linear regression.

Features:

Small Size: The dataset contains 100 records, making it easy to handle and analyze for educational purposes.

Potential Linear Relationship: Advertising costs and sales quantity are hypothesized to have a linear relationship, making this dataset ideal for exploring linear regression techniques.

Simulated Business Scenario: The data simulates a common business scenario where understanding the impact of advertising on sales is crucial.

Applications:

This dataset can be beneficial for: - Students learning about machine learning and linear regression. - Researchers exploring the effectiveness of advertising campaigns. - Business analysts seeking to understand the relationship between marketing spend and sales outcomes.

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

This dataset explores the relationship between TV advertising expenditure and product sales. It contains 200 data points with two key attributes:

TV: Advertising budget (in thousands of dollars). Sales: Product sales (in thousands of units). The dataset is ideal for practicing regression analysis, linear modeling, and data visualization techniques. It can be used to understand how advertising influences sales and build predictive models.

Column Description-

TV: Description: Advertising budget allocated to TV campaigns. Unit: Thousands of dollars. Data Type: float. Range: Minimum = 0.7, Maximum = 296.4.

Sales: Description: Product sales resulting from the advertising efforts. Unit: Thousands of units sold. Data Type: float. Range: Minimum = 1.6, Maximum = 27.0.

Content

Includes information on a specific product from an undisclosed brand. Each row in the dataset represents the sales volume for a week, along with details about the marketing campaigns and promotional methods used for the product throughout the two-year duration. The specific product and corresponding years for this data remain unknown.

Data Dictionary Sale: This variable contains numerical data representing the number of product sales for each observed week.

Price: The observed week's base price for the product.

Radio: The number of radio advertisements or campaigns promoting the product for the observed week.

InStrSpending: The average expenses associated with promoting the product in stores for the observed week.

Discount: The discount rate applicable for the observed week.

TVSpending: The average expenditure on television campaigns during the observed week.

StockRate: The stock-out rate, calculated as the number of times the product was out of stock divided by the total number of product visits.

OnlineAdsSpending: The online ads spending, calculated the total amount of spend on online advertising.

Licensor grants Licensee a non-exclusive, non-transferable, revocable license to access and use the provided data solely for academic use and learning purposes. This license is limited to the duration of the Licensee's academic program or learning activities.

Remark All price value calculated is based on usd.