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 Le groupe Radicati on them, containing such zingers as…

D'ici la fin 2019, le nombre d'utilisateurs d'emails dans le monde passera à plus de 2,9 milliards. Plus d'un tiers de la population mondiale utilisera l'email à la fin de l'année 2019.

For those of us who work in email? Stats like that are pretty temptacious, as the kids say. But d'aujourd'hui email isn’t your mom or dad’s email. Le 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. 

Aujourd'hui, avec l'arrivée des technologies liées à l'IA, votre courriel campaigns peut devenir encore plus précis, engageant et efficace qu'il ne l'a jamais été.

Le 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 déjà implementing AI in multiple areas. 

On the global front, 30 % des entreprises dans le monde 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 bonne explication 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. 

• L'apprentissage automatique implique l'application d'algorithmes et de modèles statistiques afin que les ordinateurs puissent prendre des décisions et effectuer des tâches sans instructions explicites en reconnaissant des modèles dans les données et en tirant des conclusions.

À l'heure actuelle, il existe de nombreux outils et tactiques où le NLP et l'apprentissage automatique sont mis à profit pour améliorer les programmes d'e-mailing. Examinons quelques-uns des domaines dans lesquels vous pourriez les intégrer à votre site campaigns, si vous le voulez bien... ?

Essais

With machine learning, you can now execute test du bandit multi-armé. If you’re used to split testing, brace yourself: Now you’ll be able to run tests en permanence and put your findings to work immédiatement. 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. 

Rédaction de textes

L'apprentissage automatique et le traitement automatique des langues (NLP) - et son cousin, la génération en langage naturel (NLG) - sont exploités par de nombreux fournisseurs pour proposer des solutions capables de générer des lignes d'objet et d'autres textes.

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.

Touchstone, 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 “adaptés aux intérêts et aux personnalités uniques de vos abonnés, sans le temps qu'il faut consacrer à l'élaboration manuelle de vos e-mails."

Engagement

Want to pull off a little real-time content optimization to drive engagement? Cordial says it can “ingest and process customer event, behavior, and purchase data from virtuelly any source,” so messages can be dispatched across multiple channels, based on up-to-right-this-instant 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.

Segmentation

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.” 

Livraison prédictive

If you haven’t heard of it before, that’s because c'est une nouvelle ride 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 à la game because this platform, Signaux SparkPost, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.

C'est une explosion de l'IA pour le courrier électronique.

Ce ne sont là que quelques-uns des domaines dans lesquels l'IA, le NLP et l'apprentissage automatique ont un impact actuel sur email marketing. Si vous pensez qu'il s'agit de la partie émergée de l'iceberg - ou du premier filet d'eau qui franchit les vannes - vous avez raison.

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 montre plus de 600 entreprises 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 abondance 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.