Over the last years, Coca-Cola has spent an average of ************* dollars a year on advertising worldwide, aside from 2020, with only about *** billion U.S dollars, and 2024, with over ************* dollars spent. Spending in the United States accounts for over ** percent of that cost, totally *** million U.S. dollars in 2022. Advertising innovation Soft drinks still made up the overwhelming majority of Coca Cola’s sales volume in 2017. The company spent *** million dollars advertising its flagship brand, Coke, in 2022, more than any other non-alcoholic beverage brand in the United States in that year. Coca-Cola has a long history of innovative and appealing advertising campaigns, going back to art done by American painter Norman Rockwell, to the iconic polar bears of Christmas advertising. Dominance across mediums The Coca-Cola brand’s image is not only maintained through traditional advertising mediums but social media as well. It ranked fifth on Facebook, in terms of fans, as of September 2023, with almost *** million fans. Coca-Cola’s YouTube channel was equally popular with roughly **** billion views accrued by the channel.

Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

These datasets are used to analyse a supermarket in United States for the purpose of increasing revenue.

1. 50_Supermarket_Branches.csv contains the information of 50 supermarket branches such as their spending on the advertisement, administration and promotion, states and profits.

2. Ads_CTR_Optimisation.csv is based on the Click-Through Rates (CTR) from 10000 users in 10 different advertisements.

3. Market_Basket_Optimisation.csv . This dataset contains 7500 sales transactions in a week.

4. Supermarket_CustomerMembers.csv . This dataset can be used for customer segmentation.

These datasets in 'U.S. Supermarket Data' are available and legal for everyone who needs it for any kind of analytics project.

Envestnet®| Yodlee®'s Retail Sales Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Marketing Analytics Market Outlook

According to our latest research, the global marketing analytics market size in 2024 stands at USD 5.8 billion, demonstrating robust momentum driven by the increasing adoption of data-driven decision-making across industries. The market is projected to register a CAGR of 13.2% from 2025 to 2033, reaching an estimated market size of USD 17.1 billion by 2033. This accelerated growth is primarily attributed to the proliferation of digital channels, the surge in big data, and the imperative for organizations to achieve higher ROI from their marketing investments. The marketing analytics market is evolving rapidly, with advanced analytics tools enabling businesses to gain actionable insights, optimize campaigns, and enhance customer engagement across diverse sectors.

One of the most significant growth factors for the marketing analytics market is the exponential increase in data generation from multiple digital touchpoints. The rise of omnichannel marketing strategies has resulted in vast and complex datasets, encompassing customer interactions from social media, websites, mobile applications, and email campaigns. Businesses are increasingly leveraging marketing analytics solutions to aggregate, process, and analyze this data in real time, gaining deeper insights into customer behavior, preferences, and purchase patterns. The ability to transform raw data into actionable intelligence is empowering marketers to personalize campaigns, improve targeting accuracy, and maximize conversion rates, thereby fueling the demand for sophisticated analytics platforms.

Another critical driver is the growing emphasis on measuring marketing effectiveness and optimizing marketing spend. As organizations face mounting pressure to justify marketing budgets and demonstrate tangible ROI, marketing analytics tools have become indispensable. These solutions enable marketers to track key performance indicators (KPIs), attribute revenue to specific channels, and identify underperforming campaigns. The integration of artificial intelligence and machine learning into marketing analytics platforms is further enhancing predictive capabilities, allowing businesses to forecast trends, automate campaign adjustments, and refine customer segmentation. This technological evolution is driving widespread adoption across both large enterprises and small and medium businesses.

The surge in regulatory requirements and data privacy concerns is also shaping the marketing analytics market. With the implementation of stringent data protection regulations such as GDPR and CCPA, organizations are compelled to adopt analytics solutions that ensure compliance while maintaining data integrity and security. Modern marketing analytics platforms are incorporating advanced data governance features, encryption, and anonymization techniques, enabling businesses to harness the power of analytics without compromising customer trust. This focus on compliance, coupled with the increasing need for transparency in marketing practices, is accelerating the adoption of analytics tools across regulated industries such as BFSI and healthcare.

Regionally, North America dominates the marketing analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to the presence of major analytics vendors, high digital adoption, and substantial marketing expenditure by enterprises. However, the Asia Pacific region is poised for the fastest growth over the forecast period, driven by rapid digital transformation, expanding e-commerce ecosystems, and increasing investments in marketing technology. Latin America and the Middle East & Africa are also witnessing steady growth as organizations in these regions recognize the strategic value of data-driven marketing.

Component Analysis

The marketing analytics market is segmented by component into software and services, each playing a vital role in the overall ecosystem. The software segment dominates th

A comprehensive dataset providing insights into the advertising industry for 2025, highlighting global advertising spending, digital and traditional marketing trends, the influence of social media advertising, mobile ad growth, advertising impact on consumer behavior, and the rise of programmatic advertising.

Analysis of ‘U.S. Supermarket Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sindraanthony9985/marketing-data-for-a-supermarket-in-united-states on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

These datasets are used to analyse a supermarket in United States for the purpose of increasing revenue.

1. 50_Supermarket_Branches.csv contains the information of 50 supermarket branches such as their spending on the advertisement, administration and promotion, states and profits.

2. Ads_CTR_Optimisation.csv is based on the Click-Through Rates (CTR) from 10000 users in 10 different advertisements.

3. Market_Basket_Optimisation.csv . This dataset contains 7500 sales transactions in a week.

4. Supermarket_CustomerMembers.csv . This dataset can be used for customer segmentation.

These datasets in 'U.S. Supermarket Data' are available and legal for everyone who needs it for any kind of analytics project.

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

Context Problem Statement

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers. It makes it easier for them to modify products according to the specific needs, behaviors, and concerns of different types of customers.

Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only to that particular segment.

Content Attributes

People

ID: Customer's unique identifier Year_Birth: Customer's birth year Education: Customer's education level Marital_Status: Customer's marital status Income: Customer's yearly household income Kidhome: Number of children in customer's household Teenhome: Number of teenagers in customer's household Dt_Customer: Date of customer's enrollment with the company Recency: Number of days since customer's last purchase Complain: 1 if the customer complained in the last 2 years, 0 otherwise Products

MntWines: Amount spent on wine in last 2 years MntFruits: Amount spent on fruits in last 2 years MntMeatProducts: Amount spent on meat in last 2 years MntFishProducts: Amount spent on fish in last 2 years MntSweetProducts: Amount spent on sweets in last 2 years MntGoldProds: Amount spent on gold in last 2 years Promotion

NumDealsPurchases: Number of purchases made with a discount AcceptedCmp1: 1 if the customer accepted the offer in the 1st campaign, 0 otherwise AcceptedCmp2: 1 if customer accepted the offer in the 2nd customer accepted the offer in the 2nd campaign, 0 otherwise AcceptedCmp3: 1 if the customer accepted the offer in the 3rd campaign, 0 otherwise AcceptedCmp4: 1 if customer accepted the offer in the 4th customer accepted the offer in the 4th campaign, 0 otherwise AcceptedCmp5: 1 if the customer accepted the offer in the 5th campaign, 0 otherwise Response: 1 if customer accepted the offer in the last campaign, 0 otherwise Place

NumWebPurchases: Number of purchases made through the company’s website NumCatalogPurchases: Number of purchases made using a catalog NumStorePurchases: Number of purchases made directly in stores NumWebVisitsMonth: Number of visits to the company’s website in the last month Target Need to perform clustering to summarize customer segments.

Inspiration happy learning….

I hope you like this dataset please don't forget to like this dataset

Empower Your Business With Professional Data Licensing Services

Discover a 360-Degree View of Worldwide Solution Buyers and Their Needs Leverage over 70 insights that will help you make better decisions to manage your sales pipeline, target key accounts with customized messaging, and focus your sales and marketing efforts:

Here are some of the types of Insights, our data licensing services can provide are:

Technology Insights: Discover companies’ technology preferences, including their tech stack for essential investments such as CRM systems, marketing and sales automation, email security and hosting, data analytics, and cloud security and providers.

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Funding Insights: Keep updated of the latest funding, dates, types, and lead investors, providing you with a clear understanding of a company’s potential for growth investments.

Mobile Application Insights: Find out if the company has a mobile app or web app, enabling you to tailor your pitch effectively.

Website traffic and advertising spend metrics: Customers can leverage website traffic and advertising data to gain insights into competitor performance, allowing them to refine their marketing strategies and optimize ad spending.

Access unlimited data and improve conversation by 3X

• Leverage the data for your Account-Based Marketing (ABM) strategy

• Leverage ICP (industry, company size, location etc) to identify high- potential Accounts.

• Utilize GTM strategies to deliver personalized marketing experiences through
  Multi-channel outreach (email, Cell, social media) that resonate with the target audience.

Who can leverage our Data:

B2B marketing Teams- Increase marketing leads and enhance conversions.

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Talent sourcing/Staffing companies- Leverage our data to identify and engage top talent, streamlining your recruitment process and finding the best candidates faster.

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Technology companies: Leverage our Technographic data to reveal the technology stack and tools used by companies, helping tailor marketing and sales efforts.

Data Source:

The Database, sourced through multiple sources and validated using proprietary methods on an ongoing basis, is highly customizable. It contains parameters such as employee size, job title, domain, industry, Technography, Ad spends, Funding data, and more, which can be tailored to create segments that perfectly align with your targeting needs. That is exactly why our Database is perfect for licensing!

FAQs

1. Can licensed data be resold or redistributed? Answer: No, The customer shall not, directly or indirectly, sell, distribute, license, or otherwise make available the licensed data to any third party that intends to resell, sublicense, or redistribute the data. The Customer must take reasonable steps to ensure that any recipient of the licensed data is using it for internal purposes only and not for resale or redistribution. Any breach of this provision shall be considered a material breach of this Order Form and may result in the immediate termination of the Customer's rights under this agreement, as well as any applicable remedies available under law.

2. What is the duration of the data license and usage terms? Answer: The data license is valid for 12 months (1 year) for unlimited usage. Customers also have the option to license the data for multiple years. At the end of the first year, Customers can renew the license to maintain continued access.

3. What happens if the customer misuses the data? Answer: The data can be used without limits for a period of one year or multiple years (depending on the contract tenure); however, Thomson Data actively monitors its usage. If any unusual activity is detected, Thomson Data reserves the right to terminate the account.

4. How frequently is the data updated? Answer: The data is updated on a quarterly basis and fresh records added on a monthly basis

5. What is the accuracy rate of the data? Answer: Customers can expect 90% accuracy for all data points, with email accuracy ranging between 85% and 90%. Cell phone data accuracy is around 80%.

6. What types of information are included in the data? Answer: Thomson Data provides over 70+ data points, including contact details (name, job title, LinkedIn profile, cell number, email address, education, certifications, work experience, etc.), company information, department/team sizes, SIC and NAICS codes, industry classification, technographic detai...

The transition from printed databases and directories to online formats has left the Canadian Database and Directory Publishing industry reeling, with revenue decreasing over the five years to 2023 because of this transition and COVID-19. From the advertisers' perspective, marketing costs are allocated to the media channels that most accurately reflect consumer behaviour. As more consumers shift to digital directory and database substitutes, demand for print advertisements, previously the industry's largest revenue source and profit indicator, has declined. Over the five years to 2023, the number of consumers with smartphones, which come with online directory capabilities via their ability to connect to the internet, has risen alongside internet connectivity. This has coincided with declining demand for industry products. Consequently, industry revenue has been declining an annualized 10.2% over the past five years, and is expected to reach $1.4 billion in 2023. This includes a decrease of 3.6% in 2023 alone.The industry has historically been dominated by several players, mainly telephone companies with access to consumer and business contact information. As the industry has contracted, companies have spun off their directory divisions. This was exemplified by the industry-defining event of Bell Canada handing off what would become Yellow Media Limited to KKR & Co. Inc. and the Ontario Teachers' Pension Plan Board. Over the past five years, this trend has continued, with companies selling off their failing segments to larger companies. The purchasing companies have used merger and acquisition activities to diversify their service and product offerings, entering various third-party fields, including market research, data processing and analytics, and database management.Over the five years to 2028, the industry will likely continue its downward spiral. During this period, total advertising expenditure is expected to rise. However, total print advertisement expenditure will likely decline as a share of total spending. The use of print advertisements will likely continue to become obsolete over the next five years. The most significant contributing factor to this decline is expected to be the growing use of digital advertisements. Consequently, IBISWorld forecasts industry revenue will decrease an annualized 3.4% to $1.2 billion over the five years to 2028.

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

Marketing Analytics Tools Market Outlook

The global marketing analytics tools market is experiencing significant growth, with a market size estimated at $2.5 billion in 2023 and projected to reach $6.9 billion by 2032, reflecting a compound annual growth rate (CAGR) of 11.7%. This growth is primarily fueled by the rising demand for data-driven marketing strategies among businesses seeking to enhance customer engagement, improve decision-making, and optimize marketing ROI. As digital transformation continues to accelerate across industries, marketing analytics tools have become indispensable for organizations aiming to maintain a competitive edge in an increasingly digital-centric marketplace.

A key driver of this market's growth is the exponential increase in data generation facilitated by digital channels. With the proliferation of social media platforms, mobile applications, and e-commerce websites, the volume of consumer data available to businesses has surged. This data offers invaluable insights into consumer behavior and preferences, enabling companies to tailor their marketing strategies more effectively. Consequently, the demand for advanced analytics solutions that can process and analyze vast datasets in real-time is expected to grow, further boosting the market for marketing analytics tools.

Another significant growth factor is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in marketing analytics. AI and ML are transforming how businesses interpret and utilize data by providing predictive insights and automating complex data analysis processes. These technologies enable marketers to identify trends, forecast consumer behaviors, and optimize campaigns with greater precision, thereby enhancing the overall effectiveness of marketing efforts. As AI and ML capabilities become more sophisticated, their integration into marketing analytics tools is expected to drive further market expansion.

The shift towards personalized customer experiences is also propelling the growth of the marketing analytics tools market. TodayÂ’s consumers expect personalized interactions with brands, and businesses are increasingly leveraging analytics to deliver customized content and offers. Marketing analytics tools help organizations understand individual customer journeys and preferences, allowing them to craft personalized marketing strategies that resonate with their target audience. This trend towards personalization is anticipated to continue, driving the demand for advanced analytics tools that can manage and analyze complex customer datasets.

Regionally, North America holds a significant share of the marketing analytics tools market, owing to the early adoption of advanced technologies and the presence of key market players. The region's strong focus on digital marketing and data-driven strategies has accelerated the adoption of marketing analytics solutions. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digital transformation of businesses and increasing investments in analytics infrastructure. The burgeoning e-commerce sector in countries like China and India is also contributing to the growth of the marketing analytics tools market in this region.

Component Analysis

The marketing analytics tools market can be segmented by component into software and services. The software segment encompasses various analytics platforms and solutions designed to collect, process, and analyze marketing data. These software solutions offer functionalities such as data visualization, predictive analytics, and real-time reporting, enabling marketers to make informed decisions. The demand for marketing analytics software is driven by the need for businesses to process large volumes of data efficiently and extract actionable insights that can enhance marketing strategies and outcomes.

Marketing Attribution Software is becoming increasingly vital for businesses aiming to understand the effectiveness of their marketing efforts across multiple channels. This software helps organizations allocate credit to various touchpoints in a customer's journey, providing insights into which marketing strategies are driving conversions. By leveraging marketing attribution software, companies can optimize their marketing spend and improve ROI by focusing on the most impactful channels. As the marketing landscape becomes more complex wit

Analysis of ‘Automobile Customer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/automobile-customer on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Content

An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers.

The dataset provides the details of the existing and potential customers of the company based on the purchase history and the corresponding segments they have been classified into.

Variable description

• CustomerID : unique customer ID
• Gender : gender of the customer
• Married : marital status of the customer
• Age : age of the customer
• Graduated : specifies whether the customer a graduate?
• Profession : profession of the customer
• WorkExperience : work experience of the customer in years
• SpendingScore : spending score of the customer
• FamilySize : number of family members of the customer (including the customer)
• Category : anonymised category for the customer
• Segmentation : (target variable) customer segment of the customer

Inspiration

The dataset is ideal for anyone looking to practice customer segmentation.

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

Analysis of ‘Customer Segmentation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/abisheksudarshan/customer-segmentation on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Content

An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers.

You are required to help the manager to predict the right group of the new customers.

Acknowledgements

Credits to AV

Inspiration

Beginner dataset for multiclass classification

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

Demographics 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 Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. 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 compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

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: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups 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 ...

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Context

Most of the small to medium business owners are making effective use of Gmail based Email marketing Strategies for offline targeting of converting their prospective customers into leads so that they stay with them in Business

Content

we have different aspects of emails to characterize the mail and track the mail is ignored; read; acknowledged by the reader

Acknowledgements

corefactors.in

Inspiration

amount of advertising dollars spent on a product determines the amount of its sales, we could use regression analysis to quantify the precise nature of the relationship between advertising and sales. here we want everyone to experiment with this fun data , what value we can derive from email as a tool for compaign marketing in a multi channel marketing strategy of a Small to Medium Businesses

Context

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Content

An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers.

You are required to help the manager to predict the right group of the new customers.

Acknowledgements

Credits to AV

Inspiration

Beginner dataset for multiclass classification

Context

You are a Data Scientist for a tourism company named "Lets Travel". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base. A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient. You as a Data Scientist at "Visit with us" travel company have to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Objective

To predict which customer is more likely to purchase the newly introduced travel package

About the Data

Customer details: 1. CustomerID: Unique customer ID 2. ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes) 3. Age: Age of customer 4. TypeofContact: How customer was contacted (Company Invited or Self Inquiry) 5. CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3 6. Occupation: Occupation of customer 7. Gender: Gender of customer 8. NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer 9. PreferredPropertyStar: Preferred hotel property rating by customer 10. MaritalStatus: Marital status of customer 11. NumberOfTrips: Average number of trips in a year by customer 12. Passport: The customer has a passport or not (0: No, 1: Yes) 13. OwnCar: Whether the customers own a car or not (0: No, 1: Yes) 14. NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer 15. Designation: Designation of the customer in the current organization 16. MonthlyIncome: Gross monthly income of the customer

Customer interaction data: 1. PitchSatisfactionScore: Sales pitch satisfaction score 2. ProductPitched: Product pitched by the salesperson 3. NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch 4. DurationOfPitch: Duration of the pitch by a salesperson to the customer

Online advertising revenue in the United States grew by 15 percent in 2024 compared to 2023, from 225 billion to 259 billion U.S. dollars. The figure first surpassed 100 billion dollars in 2018 and 200 billion in 2022, owing to the emergence of new channels and formats including digital audio (podcasts and streaming) and digital video (streaming and CTV) as well as strong growth from retail media. Online advertising at a glance Search is the dominating internet advertising format in the United States, accounting for 40 percent of the country's digital advertising revenue. Display follows, accounting for 29 percent of ad revenue, while 24 percent is attributed to digital video ads. However, it is spending on two specific types of platforms that is booming. Social media, with Instagram and TikTok, and retail media, with Amazon and Walmart, harvest the fruit of winning users’ attention. Consumer attitudes to online ads Consumers most often come across online ads on social media and in video content (both on streaming services such as Netflix or Amazon Prime and on video portals, such as YouTube). However, they believe that they were most receptive to ads while shopping online and consuming news content. What internet users did not appreciate at all, were ads based on their browsing history and on their social media behavior, which they considered the most invasive.

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like South America and the Americas.