Analyzing a marketing campaign and predicting its success probability
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With a major chunk of the world’s population relying on the Internet for their day-to-day operations and spending, digital marketing is rapidly gaining prominence, especially in businesses with the B2C model. This has taken the digital marketing analytics market’s expected valuation to a staggering US$ 6,979.7 million for 2024. Dedicated teams in corporations, institutes, and certifications associated with digital marketing analytics have also bolstered market growth in recent times.
Attributes | Details |
---|---|
Market Value for 2024 | US$ 6,979.7 million |
Projected Market Value for 2034 | US$ 37,470.04 million |
Value-based CAGR of the Market for 2024 to 2034 | 18.30% |
Category-wise Insights
Attributes | Details |
---|---|
Solution | Software |
Market Share (2024) | 73.70% |
Attributes | Details |
---|---|
End User | Large Enterprises |
Market Share (2024) | 64.70% |
Country-wise Insights
Countries | CAGR (2024 to 2034) |
---|---|
China | 20.40% |
The United States | 18.30% |
Australia and New Zealand | 14.40% |
Japan | 13.80% |
Germany | 12.60% |
Over 40 percent of business-to-business (B2B) marketers surveyed in the United States planned to explore artificial intelligence (AI) and automation in 2023. Video marketing and influencer marketing were also among the strategies to be experimented, mentioned by 32 and 30 percent of the respondents, respectively.
Source on data.world
Source:
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
Data Set Information:
The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.
There are four datasets:
bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).
Attribute Information:
Input variables:
1 - age (numeric)
2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - housing: has housing loan? (categorical: 'no','yes','unknown')
7 - loan: has personal loan? (categorical: 'no','yes','unknown')
8 - contact: contact communication type (categorical: 'cellular','telephone')
9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14 - previous: number of contacts performed before this campaign and for this client (numeric)
15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
17 - cons.price.idx: consumer price index - monthly indicator (numeric)
18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)
19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
20 - nr.employed: number of employees - quarterly indicator (numeric)
Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
For more information about Relevant Papers and Papers That Cite This Data Set, please go here
Citation Policy:
If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository:
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Here is a BiBTeX citation as well:
@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }
A few UCI data sets have additional citation requests. These requests can be found on the bottom of each data set's web page.
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A carefully curated list of “Marketing Agency" in Las Vegas (NV). We strive to keep our customers satisfied, so they no longer have to worry about finding quality leads. This is why we offer a 100% data guarantee. If there is any information missing or incorrect we will replace it for you. Contact us immediately. What you will find below: - Contact Name - Company - Email - Website - Contact no. & more
Classroom Datasets
Las Vegas Marketing Agency,Nevada Marketing Agency,Las Vegas Digital Marketing Agency,Nevada Digital Marketing Agency,US Marketing Agency
279
$149.00
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Data of Marketing Budget and Sales from certain bussiness
This statistic provides information on the change in data driven marketing spending in 2018 and 2019. In 2018, 96.2 percent of respondents spent more on data-driven marketing than a year earlier, and 78.2 percent were planning to increase their spending in 2019.
During a survey conducted in 2022, 58 percent of responding marketing professionals said they used social media posting tools to leverage their content marketing efforts, while 30 percent said they utilized a content management system (CMS) for the same purpose.
This dataset was created by Sneha Vijayakumar_28
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Discover the power of our meticulously curated .csv B2B leads list, a goldmine of information spotlighting enterprises thriving in the dynamic realm of Marketing and Advertising across the United States. Crafted with precision, this dataset is meticulously organized by company size, encompassing a spectrum from 1 to 10, orchestrating the perfect stage for precision-targeted outreach tactics. At every entry, a symphony of indispensable contact and professional particulars unfolds, rendering this treasure trove an indispensable asset for orchestrating impactful marketing campaigns, fostering astute business development, and navigating the intricate tapestry of networking endeavors. Key Insights:
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Business Information & Financials
Marketing and Advertising leads,B2B leads,Business leads,US companies,Company size 1-10
57625
$2400.00
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Question Paper Solutions of chapter Content Marketing of Digital Marketing, 6th Semester , Bachelor of Computer Applications
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Marketing Automation Software Market size was valued at USD 5.81 Billion in 2023 and is projected to reach USD 14.42 Billion by 2031, growing at a CAGR of 12% from 2024 to 2031.
Marketing Automation Software Market: Definition/ Overview
Marketing automation acts as a strategic approach that uses the software to streamline repetitive marketing tasks and orchestrate multi-channel campaigns. Marketing automation software is used for digital marketing, combined with solid strategies, it emphasizes the concept of the Return on Investment (ROI) and achieves increased revenue.
Marketing automation software provides channel support through email, social media SMS, and landing pages, it also offers campaign creation and management, personalized engine, trigger-based actions, website, and app usage tracking, lead scoring and tracking, tracking and analytics, automatic segmentation, integration across channels, etc. The marketing automation software is an all-inclusive system that generates hyper-targeted marketing, maintains consumer interactions across channels, and expedites workflow. Consequently, marketers are witnessing a notable increase in productivity, enhanced customer understanding, and significantly boosted ROI.
Integration with artificial intelligence (AI) and machine learning (ML) marketing automation is expected to become more sophisticated. The features like predictive analytics for lead generating, automated content creation based on customer profiles, and real-time campaign optimization. Every industry and every organization is seamlessly striving for the customer experiences, which will increase the demand for robust customer marketing automation software, Marketing automation provides a potent remedy, which is increasing the consumer demand for customization. The software has the ability to leverage customer data for targeted campaign positions.
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Discover the advantages of digital marketing for the cosmetics industry. Learn how it helps to target specific audiences, create engaging content, optimize websites for search engines, and measure the success of marketing campaigns.
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This repository contains 41 anonymized Facebook ego networks used in Stolz and Schlereth "Predicting Tie Strength with Ego Network Structures" in Journal of Interactive Marketing (forthcoming). The Facebook ego network data was extracted in combination with a survey. The survey asked users about who they perceived as their closest friends, and who they had recently interacted with on the platform. Furthermore, the data contains a binary matching based on Facebook attributes of ego and alter to assess the similarity of users. These dyad-level binary attributes are stored as csv file. All 41 ego networks are stored as graphml files, named by the identifier of the user (i.e., ego) they belong to. Additionally, the first author's "seed" network is saved separately (without attributes), intended for replication of the sampled perspective. For anonymization, all Facebook identifiers were replaced by independent numeric identifiers.
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Abstract Marketing perspective in the provision of information services involves information providers, information users, and the context of the information environment. It values the business approach to information service provision for society development and increases the visibility of the breadth and comprehensiveness of Information Science application and usage. The objective of this article is to address concepts related to the marketing perspective in the management of these services with emphasis on the information business and market. The theoretical discussion was based on a literature review on the management of information services from the marketing perspective in the context of information science focusing on the studies carried out by Information Marketing Research Group members and on national and international research on this topic, including classic marketing studies. The present study discusses the actions arising from this perspective of management in information service agencies. It was concluded that the theoretical discussion of the concepts related to the marketing perspective contributed to broaden the understanding of users as information consumers, as well as the theoretical principles of marketing that are related to this approach to the management of information service provision.
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Merck & Co.’s Keytruda com achieved the highest traffic across US hepatocellular carcinoma (HCC) patient sites, with 2.1 million visits from August 2021 to July 2022. This was followed by Bristol Myers Squibb’s (BMS) Opdivo.com (1.2 million visits) and G Read More
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This content marketing market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by end-user (retail, automotive, financial services, telecom, and others), platform (blogging, videos, infographics, case studies, and others), objective (lead generation, brand awareness, thought leadership, and others), and geography (APAC, North America, Europe, South America, and MEA). The content marketing market report also offers information on several market vendors, including Adobe Inc., Contently, Inc., CoSchedule LLC, HubSpot Inc., Influence and Co., NewsCred, Inc., Scripted Inc., Seismic Software, Inc., Sprinklr Inc., and Upland Software, Inc. among others.
What will the Content Marketing Market Size be in 2021?
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Content Marketing Market: Key Drivers and Trends
Based on our research output, there has been a negative impact on the market growth during and post COVID-19 era. The rise in the number of users on social media is notably driving the content marketing market growth, although factors such as fraud related to digital advertisements may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the content marketing industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.
Content Marketing Market Key Driver
One of the key factors driving growth in the content marketing market is the rise in the number of users on social media. Social media is becoming an essential part of the Internet, and it is accessed by more than a billion people worldwide. In addition, the increase in the use of mobile devices and smartphones has driven the use of social media among users. The growing use of social media among consumers is encouraging enterprises to use this platform to approach their target audience. As the number of users is continuously increasing, enterprises are using social media platforms for content marketing, which helps them to manage their large target audience more effectively. Thus, with the increase in the use of social media among consumers, the demand for content marketing is also expected to increase during the forecast period.
Content Marketing Market Key Trend
The use of AI with social media management software is another major factor supporting the content marketing market. The digital transformation of businesses is an effort aimed at creating connected platforms and industry revenue streams. Consequently, several industries are opting for real-time solutions besides spending on IT infrastructure and analytical solutions to gain a competitive advantage in the market. Modern businesses are marked by an increase in the need for advanced business intelligence (BI). Artificial intelligence (AI) has enabled content marketing with significant technological advances. AI provides a centralized view of all business activities and associated information. Content automation is expected to drastically reduce the cost of content production.
Content Marketing Market Key Challenge
The fraud related to digital advertisements will be a major challenge for the content marketing market. Content advertisements through digital platforms are commonly considered as a form of advertisement that is done through the Internet to market products or services to consumers. With time, the term digital advertisements have expanded its dimensions. Furthermore, due to an increase in fraud-related digital advertisements, it is now treated as one of the major concerns for industry players. Fraud related to digital advertisements includes actions such as launching advertisements outside of a user’s view and creating fake advertisement traffic by using content scraping websites. The lack of maturity in the digital advertisement space helped in creating a favorable environment for digital advertisement fraud. Hence, fraud related to digital advertisements will impact the revenue generated by enterprises, thus impacting the market growth.
This content marketing market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.
Parent Market Dynamics
Tec
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The AI marketing tool market under FMI published a new report stating the current valuation of the market is US$ 4.9 billion in 2024. Over the time span of ten years, the market spiked to a valuation of US$ 16.23 billion by 2034. The new report suggests a CAGR of 12.60% from 2024 to 2034, showing consistent market growth.
Attributes | Details |
---|---|
AI Marketing Tool Market Value for 2024 | US$ 4.9 billion |
AI Marketing Tool Market Value for 2034 | US$ 16.23 billion |
AI Marketing Tool Market Forecast CAGR for 2024 to 2034 | 12.6% |
Historical Performance and Future Growth of the AI Marketing Tool Market
Historical CAGR | 11.8% |
---|---|
Forecast CAGR | 12.6% |
Category-wise Insights
Category | Market Share in 2024 |
---|---|
CRM Software | 28.8% |
Cloud-based | 60.3% |
Country-wise Insights
Countries | CAGR through 2034 |
---|---|
United States | 9.5% |
Germany | 4.1% |
Japan | 5.4% |
China | 13.1% |
Australia and New Zealand | 16.1% |
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Explore Relationship marketing-Management through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets
Analyzing a marketing campaign and predicting its success probability