What is the future of email deliverability and how will AI impact spam filtering?
Summary
What email marketers say13Marketer opinions
Email marketer from GMass shares that AI-driven tools can automatically optimize email campaigns for better deliverability by analyzing factors like subject line effectiveness, email content quality, and recipient engagement.
Marketer from Email Geeks says AI will enable deliverability of wanted mail and the email newsletter will comeback.
Email marketer from EmailGeeks Forum suggests that the future of deliverability faces challenges from increasingly sophisticated spam techniques and the need for continuous adaptation by both marketers and email providers.
Marketer from Email Geeks thinks neural networks will predict user interaction with emails before they are sent with high accuracy and ISPs will share more data with AIs.
Email marketer from Reddit (r/emailmarketing) shares that AI-powered spam filters are becoming more sophisticated at detecting subtle spam tactics like content spinning and link cloaking, making it harder for spammers to bypass filters.
Email marketer from Mailjet explains that AI helps in predicting and preventing deliverability issues by analyzing sending patterns, identifying potential spam triggers, and providing recommendations for optimization.
Marketer from Email Geeks believes the future of deliverability involves more AI in filters, requiring a better understanding of AI filters and adapting accordingly.
Email marketer from Litmus shares that the future of email deliverability involves increased personalization powered by AI, allowing marketers to send more relevant messages that are less likely to be marked as spam.
Email marketer from Sendinblue shares that AI is transforming email marketing by providing insights into customer behavior, automating repetitive tasks, and enabling hyper-personalization, which improves deliverability and engagement.
Email marketer from Validity (formerly ReturnPath) suggests that AI will play a significant role in evaluating sender reputation, taking into account factors like engagement metrics, complaint rates, and overall email quality to determine deliverability.
Email marketer from HubSpot indicates that focusing on providing value to recipients, segmenting email lists, and using personalization can improve email deliverability and reduce the likelihood of being marked as spam.
Email marketer from EmailVendorSelection.com shares that AI enhances email deliverability by optimizing sending times, personalizing content, and dynamically adjusting sending behavior based on real-time data, leading to improved inbox placement and engagement.
Marketer from Email Geeks predicts transparency in AI/ML decision-making via legislation will impact spam filtering and that there will be more TXT RRs.
What the experts say3Expert opinions
Expert from wordtothewise.com predicts that there will be legislation around making AI/ML decision making in general less opaque, which would indirectly affect how spam filtering services are delivered.
Expert from wordtothewise.com explains that as long as the cold outreach people keep adopting spammer tactics (using a different domain, IP address and relaying through <http://gmail.com|gmail.com> are the current tactics) to avoid the blocks placed against them, then nothing receivers do can stop it.
Expert from Email Geeks explains that as long as cold outreach senders adopt spammer tactics to avoid blocks, receivers can't stop it because cold outreach senders actively dodge filters.
What the documentation says4Technical articles
Documentation from Google Workspace Admin Help explains that Google uses AI and machine learning to continuously improve its spam filtering algorithms, adapting to new spam tactics and ensuring a high level of accuracy in identifying and blocking unwanted emails.
Documentation from Cisco discusses the use of AI and machine learning in their email security solutions to identify and block advanced email threats, including phishing attacks and malware, that can negatively impact deliverability.
Documentation from Spamhaus indicates that Spamhaus employs machine learning techniques to analyze and identify spam sources, contributing to the development of real-time blocklists (RBLs) that improve email deliverability.
Documentation from Microsoft Learn highlights that Microsoft utilizes AI-driven spam filtering in Exchange Online Protection (EOP) to analyze email content, sender reputation, and other factors to accurately identify and block spam messages.