In 2025, the cloud email and collaboration market is projected to be worth around 93 billion U.S. dollars. The cloud email and collaboration market includes cloud business email and collaboration platforms and services such as Microsoft Office 365, or Google Workspace.

The statistic shows the size of the e-mail encryption market worldwide in 2015, with a forecast to 2020. In 2020, global e-mail encryption is predicted to be worth around 1.55 billion U.S. dollars.

The graph presents the e-mail marketing performance metrics in selected countries of the Asia Pacific region in the first quarter of 2015. According to the source, marketing e-mails accounted for 44.1 percent of all e-mails sent in China in the measured period. The click-to-open rate for those e-mails reached 13.1 percent.

Overview

This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In email communication, messages can be sent to multiple recipients. In this dataset, nodes are email addresses at Enron, and a hyperedge is comprised of the sender and all recipients of the email. Only email addresses from a core set of employees are included. Timestamps are in ISO8601 format.

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.

The email dataset was later purchased by Leslie Kaelbling at MIT and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to no_address@enron.com when no recipient was specified.

Statistics

Some basic statistics of this dataset are:

• number of nodes: 148
• number of timestamped hyperedges: 10,885
• distribution of the connected components:

Component Size, Number

• 143, 1
• 1, 5

Source of original data

Source: email-Enron dataset

References

If you use this dataset, please cite these references:

• Simplicial closure and higher-order link prediction. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
• Enron Email Dataset, William Cohen, 2015.

This statistic displays the frequency of email usage in New Zealand from 2007 to 2015. In 2013, approximately ** percent of respondents in New Zealand answered to check their email several times a day.

Dataset

This dataset was created by City of San Jose Data

Released under CC0: Public Domain

Contents

  Over the period presented, the share of e-mail in the total online advertising market in Poland decreased. In the first half of 2024, e-mail accounted for *** percent of the online advertising market in the country. Growth of online advertising market While email advertising's share has decreased, the Polish online advertising market as a whole is thriving. In 2023, the sector was valued at ************ zloty and is projected to reach ** billion zloty by 2028, with an impressive compound annual growth rate of **** percent. This substantial expansion suggests that marketers are finding success with alternative digital advertising formats, compensating for the decline in email marketing's prominence. Consumer attitudes toward online ads The shift away from email advertising may be partially attributed to consumer perceptions of online ads in general. In 2023, over ** percent of Poles found online advertising annoying, with most respondents tending to ignore such ads. However, ** percent of those surveyed viewed online advertising as a valuable source of information. These mixed attitudes highlight the challenges marketers face in engaging Polish consumers effectively across various digital platforms, including email.

In 2000, Enron was one of the largest companies in the United States. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. The data has been made public and presents a diverse set of email information ranging from internal, marketing emails to spam and fraud attempts.

In the early 2000s, Leslie Kaelbling at MIT purchased the dataset and noted that, though the dataset contained scam emails, it also had several integrity problems. The dataset was updated later, but it becomes key to ensure privacy in the data while it is used to train a deep neural network model.

Though the Enron Email Dataset contains over 500K emails, one of the problems with the dataset is the availability of labeled frauds in the dataset. Label annotation is done to detect an umbrella of fraud emails accurately. Since, fraud emails fall into several types such as Phishing, Financial, Romance, Subscription, and Nigerian Prince scams, there have to be multiple heuristics used to label all types of fraudulent emails effectively.

To tackle this problem, heuristics have been used to label the Enron data corpus using email signals, and automated labeling has been performed using simple ML models on other smaller email datasets available online. These fraud annotation techniques are discussed in detail below.

To perform fraud annotation on the Enron dataset as well as provide more fraud examples for modeling, two more fraud data sources have been used, Phishing Email Dataset: https://www.kaggle.com/dsv/6090437 Social Engineering Dataset: http://aclweb.org/aclwiki

Label Annotation

To label the Enron email dataset two signals are used to filter suspicious emails and label them into fraud and non-fraud classes. Automated ML labeling Email Signals

Automated ML Labeling

The following heuristics are used to annotate labels for Enron email data using the other two data sources,

Phishing Model Annotation: A high-precision SVM model trained on the Phishing mails dataset, which is used to annotate the Phishing Label on the Enron Dataset.

Social Engineering Model Annotation: A high-precision SVM model trained on the Social Engineering mails dataset, which is used to annotate the Social Engineering Label on the Enron Dataset.

The two ML Annotator models use Term Frequency Inverse Document Frequency (TF-IDF) to embed the input text and make use of SVM models with Gaussian Kernel.

If either of the models predicted that an email was a fraud, the mail metadata was checked for several email signals. If these heuristics meet the requirements of a high-probability fraud email, we label it as a fraud email.

Email Signals

Email Signal-based heuristics are used to filter and target suspicious emails for fraud labeling specifically. The signals used were,

Person Of Interest: There is a publicly available list of email addresses of employees who were liable for the massive data leak at Enron. These user mailboxes have a higher chance of containing quality fraud emails.

Suspicious Folders: The Enron data is dumped into several folders for every employee. Folders consist of inbox, deleted_items, junk, calendar, etc. A set of folders with a higher chance of containing fraud emails, such as Deleted Items and Junk.

Sender Type: The sender type was categorized as ‘Internal’ and ‘External’ based on their email address.

Low Communication: A threshold of 4 emails based on the table below was used to define Low Communication. A user qualifies as a Low-Comm sender if their emails are below this threshold. Mails sent from low-comm senders have been assigned with a high probability of being a fraud.

Contains Replies and Forwards: If an email contains forwards or replies, a low probability was assigned for it to be a fraud email.

Manual Inspection

To ensure high-quality labels, the mismatch examples from ML Annotation have been manually inspected for Enron dataset relabeling.

Dataset Breakdown

Citations

Enron Dataset Title: Enron Email Dataset URL: https://www.cs.cmu.edu/~enron/ Publisher: MIT, CMU Author: Leslie Kaelbling, William W. Cohen Year: 2015

Phishing Email Detection Dataset Title: Phishing Email Detection URL: https://www.kaggle.com/dsv/6090437 DOI: 10.34740/KAGGLE/DSV/6090437 Publisher: Kaggle Author: Subhadeep Chakraborty Year: 2023

CLAIR Fraud Email Collection Title: CLAIR collection of fraud email URL: http://aclweb.org/aclwiki Author: Radev, D. Year: 2008

Release Date: 2017-05-25.Table Name All Sectors: Nonemployer Statistics by Legal Form of Organization and Receipts Size Class for the U.S., States, and Selected Geographies: 2015 Release Schedule The data in this file were released on May 25, 2017. Key Table Information Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series. Universe The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 300 of the nearly 1,200 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all three legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see Survey Methodology. Geographic Coverage The data are shown at the U.S. and State level for LFO and the U.S. level for Receipt Size Class. All other data is shown at the U.S., State, and County levels. Industry Coverage The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data. Data Items and Other Identifying Records This file contains data on the total number of firms and receipts. Sort Order Data are presented in ascending geography by NAICS code sequence then by Legal Form of Organization. FTP Download Download the entire table at https://www2.census.gov/programs-surveys/nonemployer-statistics/data/2015/NS1500NONEMP.zip. Contact Information U.S. Census Bureau Economy-Wide Statistics Division Tel: (301)763-2580 Email: ewd.nonemployer.statistics@census.gov .NOTE: Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableG - Low Noise; cell value was changed by less than 2 percent by the application of noiseH - Moderate Noise; cell value was changed by 2 percent of more but less than 5 percent by the application of noiseJ - High Noise; cell value was changed by 5 percent or more by the application of noiseFor a complete list, see the Nonemployer Glossary.Source: U.S. Census Bureau, 2015 Nonemployer Statistics.

According to Cognitive Market Research, the Global Email Marketing Software Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031

The global Email Marketing Software market will expand significantly by XX% CAGR between 2024 and 2031.
The B2B Channel segment accounts for the largest market share and is anticipated to a healthy growth over the approaching years.
The cloud-based segment was dominating the market and had a market share of about XX% in 2023.
The small & medium enterprises sector holds the largest share and is expected to grow in the coming years as well.
Email lead-generating sales category is the market's largest contributor and is anticipated to expand at a CAGR of XX% during the projected period. 
The retail segment dominated the market and had a market share of about XX% in 2023.

Market Dynamics of the Email Marketing Software

Key Drivers of the Email Marketing Software Market

Increased Demand For Email Marketing From Various Sectors Is Driving Market Expansion

Incorporating email marketing into marketing strategies can bring substantial advantages. Foremost, the tool allows an individual to develop and nurture customer relationships which fosters brand trust and loyalty. In addition, email marketing is cost-effective for driving conversions and generating leads. Due to these reasons, many industries are adopting email marketing which leads to market growth.

Demand For Personalized And Targeted Communication Is Accelerating

Consumers require and anticipate relevant, personalized content and experience, both online and offline. To meet these demands, marketers are prioritizing email personalization to deliver a 1:1 experience that exceeds customer expectations and sets them apart. from the competition. Therefore, 74% of marketers acknowledge that focused personalization increases customer engagement and culminates in an average 20% boost in sales. (Source-https://www.campaignmonitor.com/resources/guides/personalized-email/) For Instance, According to McKinsey & Company, 71% of consumers expect personalized marketing while 76% express frustration when it's absent. They also found that brands using personalization generate 40% more revenue as compared to others. (Source-https://www.mailjet.com/blog/email-best-practices/personalized-emails/#chapter-2) According to Statista reports, 78% of marketers are using email for personalized communication (Source-https://www.mailjet.com/blog/email-best-practices/personalized-emails/#chapter-2)

Key Restraints of the Email Marketing Software Market

Data Security Issues May Impede Industry Growth 

One of the major challenges for this market is the cyber threats that include phishing attacks, malware distribution, unauthorized access to sensitive information, and more. The cybercriminals often exploit the email platforms to infiltrate networks, compromise data, and execute malicious activities. Email phishing increased by 1265% after the launch of ChatGPT in November 2022 (Slashnext,2023). Therefore, data breaches have an extensive impact on brand reputation which hampers the industry's growth. For Instance, On January 29, 2015, Anthem reported that it discovered unauthorized access to consumer information, including member names, health identification numbers, and income data. The breach was discovered by a database administrator, who noticed credentials were being used without consent. Anthem shut down the database access and required employee password resets. this data breach could affect up to 80 million people. (Source-https://www.insurance.ca.gov/0400-news/0100-press-releases/anthemcyberattack.cfm)

Opportunites in the Email Marketing Software Market

AI Integrated marketing technologies are rapidly becoming prevalent in a broad spectrum of businesses 

AI in email marketing utilizes machine learning algorithms to personalize content, optimize send times, and segment audiences. This will allow marketers to create highly targeted campaigns that address directly the needs and interests of each customer segment. AI tends to refine and update the segments to ensure that marketing efforts remain relevant and effective. Therefore, utilizing these AI-driven tools opens up new possibilities for developing compelling email c...

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services

All the real-world data sets are employed in the paper "Competition Between Homophily and Information Entropy Maximization in Social Networks", which will be published in PLOS ONE 2015. Three soical networks are included, in which CA-HepPh .txt is a collaboration network from the e-print arXiv(http://www.arxiv.org) and covers scientific collaborations between authors of papers submitted to High Energy Physics, neworleans-links-connected.txt is the giant component of the Facebook network in New Orleans (all node ids are converted to random numbers), jure_Email-Enron.txt is an email communication network that covers all the email communication within a data set of around half million emails. In each file, one line represtnes an edge and two nodes are seperated by a Tab. The demo code to read the graph can be found in test.py. These datasets are obtained from public available soruces in the Internet and their original download links or contacts can also be found as follows: CA-HepPh: http://snap.stanford.edu/data/ca-HepPh.html NewOrleans: http://socialnetworks.mpi-sws.org/datasets.html Email-Enron: http://snap.stanford.edu/data/email-Enron.html

This bar graph illustrates the percentage range of decreased internal emails team owners and administrators of paid Slack teams have perceived in 2015, after having adopted Slack. Approximately ** percent of respondents said that they have experienced a reduction of internal emails of ** to ** percent. Approximately ***** percent said that they have not seen any reduction in emails.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee.

It is recognised that the email volumes recorded do not reflect the total number of emails received by the council as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee.

It is recognised that the email volumes recorded do not reflect the total number of emails received by the council as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services

The objective of this audit was to provide reasonable assurance to Shared Services Canada (SSC) regarding the accuracy of the invoicing reports provided by the service provider of the Email Transformation Initiative (ETI) services. The scope of the audit included the service provider’s invoicing processes and controls, including all records and systems in support of ETI invoicing and billing from February 1, 2015, to July 31, 2016. The audit focused on four main areas of the billing process: Mailbox reporting, BlackBerry management services, Service level targets, Change request approval process.

Proportion of population having electricity (in %)Data Source: Lao Population and Housing Census 2015Contact: Ministry of Planning and Investment, Lao Statistics Bureau, Dongnasokneua Village, Sikhottabong District, Vientiane Capital Email: lstats@lsb.gov.la ; Tel: (+85621) 214740, Fax: (+86521) 242022ອັດຕາສ່ວນຂອງປະຊາກອນທີ່ມີໄຟຟ້າ (ເປັນ%)ການສຳຫລວດສຳມະໂນປະຊາກອນ 2015ກະຊວງແຜນການ ແລະ ການລົງທຶນ, ສູນສະຖິຕິແຫ່ງຊາດ ບ້ານດົງນາໂຊກເໜືອ, ເມືອງສີໂຄດຕະບອງ, ແຂວງນະຄອນຫລວງວຽງຈັນ. ໂທ: (+856 21)214740, ແຟັກ: (+856 21)242022. ອີເມລວ: lstats@lsb.gov.la

Proportion of population having a telephone (in %)Data Source: Lao Population and Housing Census 2015Contact: Ministry of Planning and Investment, Lao Statistics Bureau, Dongnasokneua Village, Sikhottabong District, Vientiane Capital Email: lstats@lsb.gov.la ; Tel: (+85621) 214740, Fax: (+86521) 242022ອັດຕາສ່ວນຂອງປະຊາກອນທີ່ມີໂທລະສັບ (ເປັນ%)ການສຳຫລວດສຳມະໂນປະຊາກອນ 2015ກະຊວງແຜນການ ແລະ ການລົງທຶນ, ສູນສະຖິຕິແຫ່ງຊາດ ບ້ານດົງນາໂຊກເໜືອ, ເມືອງສີໂຄດຕະບອງ, ແຂວງນະຄອນຫລວງວຽງຈັນ. ໂທ: (+856 21)214740, ແຟັກ: (+856 21)242022. ອີເມລວ: lstats@lsb.gov.la

Changes between 2005 and 2015 in the proportion of 11-20 years old children not attending schoolData Source: Lao Population and Housing Census 2005-2015Contact: Ministry of Planning and Investment, Lao Statistics Bureau, Dongnasokneua Village, Sikhottabong District, Vientiane Capital Email: lstats@lsb.gov.la ; Tel: (+85621) 214740, Fax: (+86521) 242022ການປ່ຽນແປງລະຫວ່າງປີ 2005 ແລະ 2015 ໃນອັດຕາສ່ວນເດັກອາຍຸ 11-20 ປີບໍ່ໄດ້ເຂົ້າໂຮງຮຽນການສຳຫລວດສຳມະໂນປະຊາກອນ 2005-2015ກະຊວງແຜນການ ແລະ ການລົງທຶນ, ສູນສະຖິຕິແຫ່ງຊາດ ບ້ານດົງນາໂຊກເໜືອ, ເມືອງສີໂຄດຕະບອງ, ແຂວງນະຄອນຫລວງວຽງຈັນ. ໂທ: (+856 21)214740, ແຟັກ: (+856 21)242022. ອີເມລວ: lstats@lsb.gov.la