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 El Grupo Radicati on them, containing such zingers as…

A finales de 2019, el número de usuarios de correo electrónico en todo el mundo aumentará a más de 2.900 millones. Más de un tercio de la población mundial utilizará el correo electrónico a finales de 2019.

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

Ahora, con la llegada de las tecnologías relacionadas con la IA, su correo electrónico campaigns puede ser aún más preciso, atractivo y eficaz que nunca.

En 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 ya implementing AI in multiple areas. 

On the global front, 30% de las empresas del mundo 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 muy buena explicación 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. 

• El aprendizaje automático implica la aplicación de algoritmos y modelos estadísticos para que los ordenadores puedan tomar decisiones y realizar tareas sin instrucciones explícitas mediante el reconocimiento de patrones en los datos y la extracción de inferencias.

En la actualidad, existen múltiples herramientas y tácticas en las que se utilizan la PNL y el aprendizaje automático para mejorar los programas de correo electrónico. Echemos un vistazo a algunos de los lugares donde podrías integrarlos en tu campaigns, ¿te parece?

Pruebas

With machine learning, you can now execute prueba del bandido de brazos múltiples. If you’re used to split testing, brace yourself: Now you’ll be able to run tests continuamente and put your findings to work inmediatamente. 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. 

Redacción publicitaria

El aprendizaje automático y la PNL -y su primo, la Generación del Lenguaje Natural (NLG)- están siendo aprovechados por múltiples proveedores para ofrecer soluciones capaces de generar líneas de asunto y otros textos.

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.

Piedra de toque, 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 “adaptados a los intereses y personalidades únicos de sus suscriptores, sin el tiempo que se tarda en curar manualmente sus correos electrónicos."

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 virtually any source,” so messages can be dispatched across multiple channels, based on al instante 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.

Segmentación

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.” 

Entrega predictiva

If you haven’t heard of it before, that’s because es una nueva arruga 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 a la game because this platform, Señales SparkPost, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.

Es una explosión de IA para el correo electrónico

Éstas son sólo algunas de las áreas en las que la IA, la PNL y el aprendizaje automático están teniendo un impacto actual en email marketing. Si piensa que es la punta del iceberg, o el primer goteo a través de las compuertas, estaría en lo cierto.

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 muestra más de 600 empresas 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 abundante 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.