US Sales and Marketing Analytics Market is Segmented by Deployment (Cloud-based and On-Premise), Application (Online Marketing, Email Marketing, Social Media Marketing, Content Marketing, and Other Applications) and End-User (Retail, BFSI, Healthcare, Manufacturing, Travel and Hospitality, and Other End-Users). For each segment, the market sizing and forecast have been done based on the value (in USD million)

The Report Covers the Global Top Marketing Analytics Companies and the Market is Segmented by Deployment (Cloud and On-Premises), Application (Online Marketing, E-Mail Marketing, Content Marketing, and Social Media Marketing), End User (Retail, BFSI, Education, Healthcare, Manufacturing, Travel, and Hospitality), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East and Africa). The Market Size and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

The Digital Marketing Analytics Market Size Was Worth USD 6.8 Billion in 2023 and Is Expected To Reach USD 31.3 Billion by 2032, CAGR of 18.5%.

According to Cognitive Market Research, the global Marketing Analytics Software market size is USD 5.7 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 20.6% from 2024 to 2031. Market Dynamics of Marketing Analytics Software Market Key Drivers for Marketing Analytics Software Market Growing demand for data-driven marketing- One of the key forces driving the Marketing Analytics Software market is the increased demand for data-driven marketing tactics. In today's digital age, businesses are overwhelmed with data from a variety of sources, including social media, websites, and client contacts. Marketing analytics software allows businesses to collect, analyze, and interpret data in order to acquire important insights into customer behavior, preferences, and market trends. Businesses may use these insights to make better decisions, optimize marketing initiatives, and increase consumer engagement. Rise of social media and Digital Marketing Key Restraints for Marketing Analytics Software Market Data Privacy Concerns Price Volatility of Raw Materials Introduction of the Marketing Analytics Software Market Marketing analytics software refers to the tools and platforms that assist firms in collecting, measuring, analyzing, and interpreting marketing data in order to acquire insights and make informed decisions. The marketing analytics software market is expanding rapidly, assisting firms in analyzing and interpreting data in order to make more informed marketing decisions. This type of software enables businesses to track and measure the efficacy of their marketing campaigns, enhance marketing strategies, and improve the total return on investment (ROI) of their marketing initiatives. The growing use of social media channels, as well as the increased use of big data analytics, are driving global market expansion. Furthermore, the increased necessity to measure customer behaviour has a beneficial impact on market growth

Importance of Digital Marketing Statistics

By leveraging them, you may remain competitive. Data can be used to discover what others are doing. It is always feasible to stay ahead of the competition. Using statistical data, you can prioritize your actions. To carry out a cross-marketing strategy, it is vital to compare the performance of various platforms.

When you have statistical support, it is easier to make effective decisions. Using digital marketing analytics can provide confidence in knowing what works. Reduce your time spent strategizing. With the time saved, it is able to accomplish other critical activities such as SEO or auditing.

As of June 2024, around 63 percent of marketing professionals surveyed worldwide rated their data-driven strategies somewhat successful. Approximately 32 percent considered them very successful, and five percent as unsuccessful. According to the same study, targeting segmented audiences and real-time decision-making were among the top challenges for executing a data-driven strategy.

Business Analytics Market was valued at USD 84.42 Billion in 2024 and is projected to reach USD 176.14 Billion by 2031, growing at a CAGR of 9.63% from 2024 to 2031.

Global Business Analytics Market Drivers

The market drivers for the Business Analytics Market can be influenced by various factors. These may include:

Growing Adoption of Big Data Analytics: In order to extract meaningful insights from their data, organizations are progressively using big data analytics in response to the exponential expansion of data. Making educated decisions through data analysis is facilitated by business analytics.
Growing Need for Data-driven Decision Making: In order to obtain a competitive edge, businesses are realizing the significance of data-driven decision making. The methods and instruments for data analysis and significant insights extraction for improved decision-making are offered by business analytics.
Growing Need for Predictive and Prescriptive Analytics: Predictive and prescriptive analytics are becoming more and more in demand as a means of projecting future trends and results. Businesses can use business analytics to prescribe activities to achieve desired outcomes and forecast future outcomes based on previous data.
Growing Emphasis on Customer Analytics: As e-commerce and digital marketing gain traction, companies are putting more of an emphasis on comprehending the behavior and preferences of their customers. In order to increase consumer engagement and personalize marketing efforts, business analytics is used to analyze customer data.
Emergence of Advanced Technologies: The use of advanced analytics solutions is being propelled by developments in fields like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Businesses may now analyze data more effectively and gain deeper insights thanks to these technologies.
Operational Efficiency and Cost Optimization Are Necessary: Companies are always under pressure to increase operational efficiency and reduce costs. Business analytics promotes market expansion by assisting in the identification of opportunities for process and cost-cutting enhancements.
Compliance and Regulatory Requirements: The use of business analytics solutions for risk management and compliance reporting is being fueled by the growing regulatory requirements in a number of industries, including healthcare, banking, and retail.

In 2021, the global social media analytics market was valued at roughly seven billion U.S. dollars. It was expected to grow to 8.5 billion in 2022 and surpass 26 billion dollars in 2028. Social media analytics tools are used, among others, to manage customer experience, as well as marketing management, and to gain competitive intelligence.

During a survey carried out in 2024, roughly one in three marketing managers from France, Germany, and the United Kingdom stated that they based every marketing decision on data. Under 10 percent of respondents in all five surveyed countries said they struggled to incorporate data analytics into their decision-making process.

Question Paper Solutions of chapter Introduction to Marketing Analytics of Marketing Analytics, 6th Semester , Bachelor in Business Administration 2020 - 2021

The data analytic market size is projected to grow from USD 69.40 billion in the current year to USD 877.12 billion by 2035, representing a CAGR of 25.93%, during the forecast period till 2035.

The Big Data Analytics in the Manufacturing Industry Report is Segmented by End-User Industry (Semiconductor, Aerospace, Automotive, And Other End-User Industries), Application (Condition Monitoring, Quality Management, Inventory Management, And Other Applications), And Geography (North America, Europe, Asia-pacific, And Latin America). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Question Paper Solutions of chapter Strategic Marketing Analytics of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

Digital Marketing Analytics Market was valued at $3.67 Billion in 2023, and is projected to reach $USD 11.20 Billion by 2032, at a CAGR of 13.2% from 2023 to 2032.

Envestnet®| Yodlee®'s Consumer Purchase 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

The size and share of the market is categorized based on Application (Customer analytics, Risk analytics, Claims analytics, Marketing analytics) and Product (Fraud detection, Risk assessment, Customer retention, Product development, Regulatory compliance, Marketing optimization) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Data Analytics Market Valuation – 2024-2031

Data Analytics Market was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2031, growing at a CAGR of 30.41% from 2024 to 2031.

Data Analytics Market Drivers

Data Explosion: The proliferation of digital devices and the internet has led to an exponential increase in data generation. Businesses are increasingly recognizing the value of harnessing this data to gain competitive insights.

Advancements in Technology: Advancements in data storage, processing power, and analytics tools have made it easier and more cost-effective for organizations to analyze large datasets.

Increased Business Demand: Businesses across various industries are seeking data-driven insights to improve decision-making, optimize operations, and enhance customer experiences.

Data Analytics Market Restraints

Data Quality and Integrity: Ensuring the accuracy, completeness, and consistency of data is crucial for effective analytics. Poor data quality can hinder insights and lead to erroneous conclusions.

Data Privacy and Security Concerns: As organizations collect and analyze sensitive data, concerns about data privacy and security are becoming increasingly important. Breaches can have significant financial and reputational consequences.

As of 2019, forecasts suggest that the predictive analytics market will reach over six billion U.S. dollars in total revenue. By 2022 the market is expected to reach nearly 11 billion dollars in annual revenue as an increasingly large number of businesses make use of predictive analytics techniques for everything from fraud detection to medical diagnosis.

Predictive analytics

The field of predictive analytics involves the use of various statistical methods and models within businesses to make predictions about a wide range of future outcomes. Predictive analytical analysis is already one of the most widely adopted intelligent automation technologies in the world, with over 80 percent of major enterprises deploying smart analytics that include predictive analytics. As business interactions around the world become increasingly digitalized, massive amounts of data are created which can be evaluated through predictive analytics tools in order to give users a better understanding of market dynamics and underlying trends. Considering this, it is no surprise that predictive models rank as the one of the top big data technology trends around the world.

The global data analytics outsourcing market size reached USD 14.39 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 131.32 Billion by 2033, exhibiting a growth rate (CAGR) of 25.06% during 2025-2033. There are several factors that are driving the market, which include the growing need for enhanced decision-making, the thriving finance sector, collaborations between key players, and rising focus on scalable and flexible solutions in various industries.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on type, application, component, and vertical.

Google Data Analytics Capstone Project

Cyclistic Dataset

Case Study: How Does a Bike-Share Navigate Speedy Success?

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams.

Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.

Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Data overview

ride_id: It is a distinct identifier assigned to each individual ride. rideable_type: This column indicates the type of bikes used for each ride. started_at: This column denotes the timestamp when a particular ride began. ended_at: This column represents the timestamp when a specific ride concluded. start_station_name: This column contains the name of the station where the bike ride originated. start_station_id: This column represents the unique identifier for the station where the bike ride originated. end_station_name: This column contains the name of the station where the bike ride concluded. end_station_id: This column represents the unique identifier for the station where the bike ride concluded. start_lat: This column denotes the latitude coordinate of the starting point of the bike ride. start_lng: This column denotes the longitude coordinate of the starting point of the bike ride. end_lat: This column denotes the latitude coordinate of the ending point of the bike ride. end_lng: This column denotes the longitude coordinate of the ending point of the bike ride. member_casual: This column indicates whether the rider is a member or a casual user.