Do we really need to remind anybody about the fact that email (and email marketing) isn’t going anywhere anytime soon? If we did, we’d just flash this study by 雷迪卡迪集团 on them, containing such zingers as…

到2019年底，全球电子邮件用户的数量将增加到超过29亿。到2019年底，全球超过三分之一的人口将使用电子邮件。

For those of us who work in email? Stats like that are pretty temptacious, as the kids say. But 今天的 email isn’t your mom or dad’s email. ǞǞǞ continuing success of email lies, in large part, to how its ability to evolve. Going mobile put email into a lot more pockets, for instance. 

现在，随着人工智能相关技术的到来，您的电子邮件campaigns 可以变得比以往任何时候都更加精准、吸引人和有效。

ǞǞǞ arrival of email AI? That’s so 2018 

At the end of 2018, 普华永道 said it had surveyed U.S. execs, and found 27% of them claiming to be 已经 implementing AI in multiple areas. 

On the global front, 全球30%的公司 will be using AI in at least one of their sales processes by 2020. But only 17% of email marketers considering automation tools gave any thought to incorporating AI.

The laggards might not realize the impact AI has already had on the email ecosystem. One very visible example was how Gmail handles email classification using Natural Language Processing (NLP) to filter incoming emails as Primary, Social, or Promotions messages. 

Here’s a 很好的解释 of how NLP does its job, presented as a primer for coders who want to hack up a spam filter. But if you aren’t interested in all the plumbing, that’s cool. One thing worth remembering, though? NLP and machine learning are only branches of the bigger, broader category “AI” and have specific goals.  

• NLP is intended to read, decipher, understand, and make sense of human language in a manner that’s useful in machine-human interaction. 

• 机器学习涉及到算法和统计模型的应用，因此计算机可以在没有明确指示的情况下，通过识别数据中的模式并得出推论，做出决定并执行任务。

目前，NLP 和机器学习有多种工具和策略可用于增强电子邮件程序。让我们来看看可以将它们整合到campaigns 中的一些地方，好吗？

测试

With machine learning, you can now execute 多臂匪徒测试. If you’re used to split testing, brace yourself: Now you’ll be able to run tests 不断 and put your findings to work 立即. Over time, you’ll gradually optimize your results, and simultaneously be able to test content and messaging while also sending your best-performing variant out to prospects or customers.

How’s it done? You set up a campaign and a few email variations, and machine learning does the rest, running tests throughout your campaign and fine-tuning it on the basis of test data. What can you test? Pretty much anything you’re already testing, from copy to design to images to timing. 

文案写作

机器学习和NLP--以及它的表亲，自然语言生成（NLG）--正被多个供应商利用，以提供能够实际生成主题行和其他副本的解决方案。

Take a company like 珀尔萨多, for instance: Its “message machine” applies its grasp of natural language to create copy that speaks in the marketer’s “brand voice,” leveraging a huge database of tagged and scored works in 25 languages, a database that evolves over time as machine learning delivers insights (and makes judgments) about which messages hold the most appeal for your target audience.

试金石, as another example, compares your subject line against a database of 21 billion emails, as well as industry trends, to predict its likely impression, click and conversion rates.

Rasa.io automated the newsletter creation process, and uses machine learning to optimize content based on each recipient’s behaviors to provide 1:1 personalization that’s “根据你的订阅者的独特兴趣和个性进行定制，而不需要花费时间来手动策划你的电子邮件"。

聘用

Want to pull off a little real-time content optimization to drive engagement? 堇色的 says it can “ingest and process customer event, behavior, and purchase data from 虚拟ly any source,” so messages can be dispatched across multiple channels, based on 即时的 behavioral data. So onboarding, re-engagement campaigns, and other triggered emails can be aligned with what they’re interested in this very moment.

Another way to engage? Add a personal touch. Well, a virtual personal touch: 沟通交流 proudly claims to deliver “personalized human touch at scale” through AI sales assistants that reach out to a user within minutes of him or her showing interest in your brand or inventory via email or SMS. 

If you’re worried the “conversation” reads like robo-copy, they claim the AI “empathizes” effectively by analyzing replies to tailor the right responses.  Moreover, the platform isn’t intended simply for initial engagement or onboarding but can handle routine dialogues throughout the entire customer journey.

分割

For companies investing in customer data management platforms, being able to milk the greatest possible insight and benefits from big data to deliver highly personalized user experiences, especially in email, is an obvious concern. 

A machine learning solution that’s connected to these potentially enormous pools of data can do insightful segmentation in ways no human being – or boiler room full of human beings – ever could, making continual adjustments and uncovering new associations, even generating product new segments where none were visible before.  模拟机 is one of these providers, calling their particular flavor “dynamic predictive segmentation.” 

预测性交付

If you haven’t heard of it before, that’s because 这是一个新的皱纹 in applying machine learning to email. By analyzing the behavior of trillions of emails, predictive analytics and machine learning are able to optimize delivery and the overall health of an email program.

This means real-time insights are available about deliverability and performance issues, problems can be identified before they happen, and data-driven recommendations can be made about how to optimize engagement and performance.  Outages can be avoided – while ROI is maximized.

And if you’ll allow just one self-plug? It’s new 到 game because this platform, 星火邮政的信号, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.

这是一场人工智能的爆炸

这些只是人工智能、NLP 和机器学习目前对email marketing 产生影响的几个领域。如果你认为这只是冰山一角，或者说是洪水中的第一股涓涓细流，那你就大错特错了。

One way to see how feverish a new technology segment is getting is to see how many companies and startups have hung out a shingle, using investor or job sites like AngelList. Right now, a search for “email AI” there 显示超过600家公司 in the space, and there’ll be more to come.

In other words, there’ll eventually be an AI add-on for every facet of your email program.  In the meantime? Putting today’s existing AI tools to work already offers 大量的 of potential for discovering how NLP and machine learning can improve the way you’re using a veteran marketing channel that’s just as leading-edge as ever.