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.

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

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 30 September 2021.

--- 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 ---

This dataset provides information on the advertising practices of a large sample (844) of UK firms. Questionnaire data are recorded in the dataset under 91 variables (quantitative response categories) and all qualitative responses have been given numerical values according to the coding frames included in the user guide. The questionnaires used for the survey were sent to the Advertising Managers of 5234 firms. Each questionnaire was divided into three sections plus a supplementary question. The first question (q.1) asks simply whether the firm advertises or not. Responses to the rest of the first section (q.2 to q.5) deal with non-advertisers whose managers were asked about the reasons for not advertising and about any future plans to advertise. The second section is for advertisers (entitled 'About Your Advertising') and comprises the bulk of the questionnaire (q.6 to q.12). Managers who stated in question 1 that their firm does advertise were asked in this section about the level, motivation and content of their firm's advertising. In the light of the rapid growth in importance of the Internet, several questions ask specifically about the current and future levels of internet advertising. The final question in this section asks for the manager's conjectured advertising response to a series of hypothetical questions concerning trading conditions and rival companies. All respondents were asked to complete section three (q.13 to q.17) entitled 'About Your Company'. Here managers were asked to categorise their main product line or service within a particular sector of the economy and to list the geographical market at which their main product line is aimed. They are also asked to rank the most important modes of competition for their main product line and to identify the number of competitors for this product. Following this section managers are asked (q.18) to provide data on total advertising expenditure for up to ten financial years.

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.

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

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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 ...

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Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

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.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to produce quantitative data necessary for a critical evaluation of regulatory techniques. Main Topics: Attitudinal/Behavioural Questions Durable goods purchased, advertising (i.e. where product was advertised, content of advertisement, misrepresentation etc), where goods were purchased (i.e. shop/garage, mail order - 8 categories), contact made by seller (in particular, techniques used by salesman), information given by salesman/seller/agent etc (particularly whether this was misleading and whether respondent received the exact goods ordered). Respondent satisfaction with goods purchased and the sales transaction is recorded together with his knowledge of Which? magazine. Detailed information is available for: arrangements for return of goods or cancellation of the deal - if the respondent attempted to do one of these, how he went about it and the outcome are noted; complaints (i.e. from whom advice was sought, action taken, seller's reaction and outcome are given); the agreement (i.e. content, let-out clauses, terms of deposit, credit and interest, etc and surety required for credit are recorded). A section is included particularly on the purchase of motor vehicles. This includes: length of time respondent had been driving when he bought vehicle, age of vehicle bought and whether it had a current MOT Test Certificate, whether respondent had any mechanical check made, whether delivery date was met by seller, whether vehicle was in good condition (whether all accessories ordered were included), whether it was under guarantee (a list of defects is given together with a note of money spent on repairs since purchase and complaints made to the dealer about this). Data on loans: loan company money borrowed from, advertising used (accuracy noted), any contacts between the loan company and the seller of the goods purchased, amount borrowed by respondent (including interest rate, total amount repaid, etc). Content of agreement signed considered in detail and respondent dissatisfaction with loan transaction recorded. If payment became overdue, reason is given and the outcome noted (including a note of the technique used by the company to obtain money-particularly harrassment, court action, etc).Similar data for other sources of credit are also available. Background Variables Household composition, sex, age when ordered goods, occupation and social grade, fluency in English, whether respondent is obviously a member of a minority racial or ethnic group, type of accommodation (i.e. house, flat, etc) and length of time respondent had been resident at address when goods were purchased. A 2-stage probability sample. First stage: 60 sampling points allocated among the Registrar General's 10 standard regions in proportion to the adult population of each region. Within each standard region, a sampling frame was used that listed all the local authority areas in England and Wales stratified by area type (conurbations, non-conurbation urban areas, non-conurbation rural areas). Within each type by: conurbation; by high employment area/low employment areas, non-conurbation: by high population density/low population density. Within each of the resultant cells, local authority areas were arranged in descending order of socio-economic index derived from the 1966 census of population. Second stage: within each selected polling district, a sample of 25 addresses was drawn from the current Electoral Register by applying a fixed sampling interval to a random start Face-to-face interview

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.

Database and directory publishers in Canada have continued to undergo significant changes over the past five years, primarily driven by the migration from print to digital formats. As advertisers increasingly direct their budgets toward media platforms that mirror evolving consumer preferences, demand for traditional print advertisements, a key historical revenue and profit generator for the industry, has sharply declined. The widespread adoption of smartphones and improved internet connectivity continues to accelerate consumer access to online directories and has further diminished the relevance of print products, contributing to a persistent contraction in industry revenue. From 2020 to 2025, industry revenue has declined at an annualized rate of 3.1%, reaching an estimated $1.7 billion in 2025, which includes a 1.5% contraction during the year alone. The industry, once dominated by large and well-financed telecommunications companies, has experienced increased divestiture and restructuring activity as companies spin off or sell underperforming directory divisions. Though Thryv Inc. entered the Canadian market in 2022, this trend has largely continued, with major companies acquiring smaller or struggling businesses to broaden their offerings into adjacent markets, such as data analytics and database management. Moving forward, the industry will continue to face declines as digital advertising continues to eclipse print media. While overall advertising expenditure is forecast to rise, investment in print advertisements is expected to diminish significantly. Over the next five years, publishers will continue to invest in and prioritize digital platforms to compete with substitutes. However, the emergence of AI is expected to continue throughout the market, which will ultimately lead to less demand for industry services, on average. As a result, industry revenue is forecast to fall at an annualized rate of 1.8% to reach $1.6 billion by 2030, reflecting the ongoing obsolescence of industry databases, despite its push for digital offerings.

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

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.

Success.ai offers a powerful platform for accessing extensive EU company data, designed to meet the dynamic marketing and advertising needs across diverse industries. This specialized dataset includes detailed profiles of over 28 million companies, from burgeoning startups to established private firms, tailored to support precise data enrichment and targeted marketing.

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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.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

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

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 29 August 2021.

--- 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 ---

Analysis of ‘TourPackagePrediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sanamps/tourpackageprediction on 30 September 2021.

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

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

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