Summary
The future of email deliverability is inextricably linked to advancements in Artificial Intelligence (AI). AI's role in spam filtering is expected to grow significantly, with systems becoming more sophisticated at detecting subtle spam tactics, continuously improving algorithms, and adapting to new threats. This includes analyzing email content, sender reputation, and engagement metrics. AI enables greater personalization, optimized sending times, and dynamic adjustments to sending behavior to improve inbox placement and engagement. Predictive analysis also plays a key role in preventing deliverability issues. However, challenges remain, including the need for marketers and email providers to adapt to increasingly sophisticated spam techniques. Transparency in AI/ML decision-making and legislative action are expected to indirectly impact spam filtering. Furthermore, persistent spammer tactics, like cold outreach using deceptive measures, require adaptive strategies. Ultimately, providing value to recipients, properly segmenting lists, and maintaining good sender reputation are essential for optimal deliverability.
Key findings
- AI-Driven Spam Filtering: AI and machine learning are integral to modern spam filtering, adapting continuously to new tactics and employing sophisticated analysis.
- Personalization and Optimization: AI enhances deliverability through increased email personalization, optimized sending times, and dynamic campaign adjustments.
- Sender Reputation Management: AI plays a crucial role in evaluating sender reputation based on engagement metrics, complaint rates, and overall email quality.
- Predictive Analysis for Prevention: AI helps predict and prevent deliverability issues by analyzing sending patterns and identifying potential spam triggers.
- Spammer Adaptability: Spammers consistently adapt tactics, requiring ongoing enhancements in AI-driven detection and prevention methods.
- AI/ML Transparency: Future legislation around AI/ML transparency will impact spam filtering policies.
Key considerations
- Continuous Adaptation: Adaptation to evolving spam techniques by both marketers and filtering systems is crucial for maintaining deliverability.
- Value-Driven Content: Focusing on providing value to recipients, segmenting email lists, and using personalization are key to improved deliverability.
- Combating Deceptive Tactics: Receivers and security systems must adapt to the persistent deceptive tactics employed by cold outreach and other spam sources.
- Legislative Impacts: Be prepared for potential legislative changes affecting AI/ML transparency and their subsequent impact on spam filtering strategies.
What email marketers say
13 marketer opinions
The future of email deliverability is heavily influenced by artificial intelligence (AI). AI is expected to play a crucial role in spam filtering, becoming more sophisticated in detecting spam tactics and adapting to new threats. It will enable personalized content, optimized sending times, and dynamic adjustments to sending behavior, thereby improving inbox placement and engagement. Furthermore, AI is expected to assess sender reputation based on engagement metrics, complaint rates, and email quality. However, challenges remain, including sophisticated spam techniques, transparency in AI/ML decision-making, and the need for continuous adaptation.
Key opinions
- AI in Spam Filtering: AI-powered spam filters are becoming increasingly sophisticated, adapting to new spam tactics and detecting subtle techniques such as content spinning and link cloaking.
- Personalization: AI enables increased personalization of email content, making messages more relevant and less likely to be marked as spam.
- Sender Reputation: AI will play a significant role in evaluating sender reputation based on engagement metrics, complaint rates, and overall email quality.
- Predictive Analysis: AI helps in predicting and preventing deliverability issues by analyzing sending patterns, identifying potential spam triggers, and providing recommendations for optimization.
- Automation: AI-driven tools can automate the optimization of email campaigns by analyzing factors like subject line effectiveness, email content quality, and recipient engagement.
- Transparency: Transparency in AI/ML decision-making via legislation will impact spam filtering.
Key considerations
- Adaptation: Continuous adaptation is necessary to combat increasingly sophisticated spam techniques and maintain email deliverability.
- Value and Engagement: Focusing on providing value to recipients, segmenting email lists, and using personalization can improve email deliverability.
- Data Sharing: ISPs will share more data with AI systems to drive accuracy in predicting user interaction with emails.
Marketer view
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.
4 Feb 2022 - GMass
Marketer view
Marketer from Email Geeks says AI will enable deliverability of wanted mail and the email newsletter will comeback.
4 Sep 2022 - Email Geeks
What the experts say
3 expert opinions
Experts agree that as long as cold outreach senders continue to employ spammer tactics, such as using different domains, IPs, and relays to avoid blocks, it's difficult for receivers to effectively stop them. One expert predicts potential future legislation aimed at making AI/ML decision-making more transparent, which could indirectly impact spam filtering services.
Key opinions
- Spammer Tactics: Cold outreach senders are actively adopting spammer tactics to evade filters, making it challenging for receivers to block them.
- AI/ML Transparency: Potential legislation could increase transparency in AI/ML decision-making, indirectly affecting spam filtering.
Key considerations
- Adaptive Strategies: Email receivers and security systems must continuously adapt to the evolving tactics used by cold outreach senders.
- Legislative Impact: Future legislation regarding AI/ML transparency could have significant implications for the operation of spam filtering services.
Expert view
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.
18 Jun 2022 - wordtothewise.com
Expert view
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.
2 Apr 2022 - wordtothewise.com
What the documentation says
4 technical articles
Major email and security providers like Google, Microsoft, Spamhaus, and Cisco are actively using AI and machine learning to enhance their spam filtering capabilities. These technologies are employed to analyze email content, sender reputation, and other factors to identify and block unwanted emails, including spam, phishing attacks, and malware. The emphasis is on continuous improvement and adaptation to new spam tactics to ensure accurate and effective filtering.
Key findings
- AI-Driven Spam Filtering: AI and machine learning are core components of modern spam filtering solutions.
- Continuous Improvement: Email providers are continuously improving their spam filtering algorithms to adapt to new spam tactics.
- Comprehensive Analysis: AI is used to analyze various factors, including email content, sender reputation, and threat intelligence data.
- Threat Detection: AI is used to detect advanced email threats, such as phishing attacks and malware.
Key considerations
- Evolving Threats: The constant evolution of spam and phishing tactics requires continuous investment in and refinement of AI-driven spam filtering technologies.
- Collaboration: Collaboration between email providers, security firms, and threat intelligence organizations is crucial for staying ahead of spammers and cybercriminals.
- Multi-layered Security: Effective email security relies on a multi-layered approach that combines AI-driven filtering with other security measures, such as sender authentication and user education.
Technical article
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.
14 Jan 2024 - Google Workspace Admin Help
Technical article
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.
30 May 2024 - Cisco
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