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 Die Radicati-Gruppe on them, containing such zingers as…

Bis Ende 2019 wird die Zahl der weltweiten E-Mail-Nutzer auf über 2,9 Milliarden ansteigen. Mehr als ein Drittel der Weltbevölkerung wird bis Ende 2019 E-Mails nutzen.

For those of us who work in email? Stats like that are pretty temptacious, as the kids say. But die heutige email isn’t your mom or dad’s email. Die 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. 

Mit der Einführung von KI-Technologien kann Ihre E-Mail campaigns jetzt noch präziser, ansprechender und effektiver werden als je zuvor.

Die arrival of email AI? That’s so 2018 

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

On the global front, 30 % der Unternehmen weltweit 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 ziemlich gute Erklärung 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. 

• Beim maschinellen Lernen werden Algorithmen und statistische Modelle eingesetzt, damit Computer ohne ausdrückliche Anweisungen Entscheidungen treffen und Aufgaben ausführen können, indem sie Muster in Daten erkennen und Schlussfolgerungen ziehen.

Zurzeit gibt es mehrere Tools und Taktiken, bei denen NLP und maschinelles Lernen zur Verbesserung von E-Mail-Programmen eingesetzt werden. Schauen wir uns einige der Stellen an, an denen Sie sie in Ihr campaigns integrieren könnten...?

Prüfung

With machine learning, you can now execute mehrarmige Bandit-Tests. If you’re used to split testing, brace yourself: Now you’ll be able to run tests kontinuierlich and put your findings to work sofort. 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. 

Werbetexten

Maschinelles Lernen und NLP - und sein Cousin, Natural Language Genration (NLG) - werden von mehreren Anbietern genutzt, um Lösungen bereitzustellen, die tatsächlich Betreffzeilen und andere Texte generieren können.

Take a company like Persado, 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.

Prüfstein, 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 “die auf die individuellen Interessen und Persönlichkeiten Ihrer Abonnenten zugeschnitten sind, ohne den Zeitaufwand für die manuelle Zusammenstellung Ihrer E-Mails."

Verlobung

Want to pull off a little real-time content optimization to drive engagement? Herzliche says it can “ingest and process customer event, behavior, and purchase data from virtuellly any source,” so messages can be dispatched across multiple channels, based on bis-zu-gerade-diesem-zeitpunkt 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: Conversica 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.

Segmentierung

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.  SimMachines is one of these providers, calling their particular flavor “dynamic predictive segmentation.” 

Prädiktive Zustellung

If you haven’t heard of it before, that’s because es ist ein neues Phänomen 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 zum game because this platform, SparkPost-Signale, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.

Eine KI-für-E-Mail-Explosion

Dies sind nur einige der Bereiche, in denen KI, NLP und maschinelles Lernen heute einen Einfluss auf email marketing haben. Wenn Sie glauben, dass dies nur die Spitze des Eisbergs ist - oder das erste Rinnsal, das durch die Schleusen fließt - dann haben Sie recht.

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 zeigt über 600 Firmen 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 viel 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.