Hoe Natural Language Processing en Machine Learning te gebruiken in uw e-mailprogramma

Hoe Natural Language Processing en Machine Learning te gebruiken in uw e-mailprogramma

How to use Natural Language Processing and Machine Learning in your E-mail Program

Jul 29, 2019

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Gepubliceerd door

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Categorie:

Categorie:

E-mail

Email

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Hoe Natural Language Processing en Machine Learning te gebruiken in uw e-mailprogramma

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 De Radicati Groep on them, containing such zingers as…

Tegen eind 2019 zal het aantal e-mailgebruikers wereldwijd toenemen tot meer dan 2,9 miljard. Meer dan een derde van de wereldbevolking zal eind 2019 e-mail gebruiken.

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

Nu, met de komst van AI-gerelateerde technologieën, kan je e-mail campaigns nog preciezer, boeiender en effectiever dan ooit tevoren worden.


De 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 al implementing AI in multiple areas. 


On the global front, 30% van de bedrijven wereldwijd 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 vrij goede uitleg 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. 

  • Bij machinaal leren worden algoritmen en statistische modellen toegepast zodat computers zonder expliciete instructies beslissingen kunnen nemen en taken kunnen uitvoeren door patronen in gegevens te herkennen en conclusies te trekken.

Op dit moment zijn er meerdere tools en tactieken waarbij NLP en machine learning worden ingezet om e-mailprogramma's te verbeteren. Zullen we eens kijken naar een aantal plaatsen waar je ze in je campaigns zou kunnen integreren?


Testen

With machine learning, you can now execute multi-armige bandiet test. If you’re used to split testing, brace yourself: Now you’ll be able to run tests continu and put your findings to work onmiddellijk. 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. 


Copywriting

Machine learning en NLP - en het neefje daarvan, Natural Language Genration (NLG) - worden door verschillende aanbieders ingezet om oplossingen te leveren die daadwerkelijk onderwerpregels en andere kopij kunnen genereren.

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 “afgestemd op de unieke interesses en persoonlijkheden van uw abonnees, zonder de tijd die het kost om uw e-mails handmatig samen te stellen."


Engagement

Want to pull off a little real-time content optimization to drive engagement? Cordiaal says it can “ingest and process customer event, behavior, and purchase data from virtuelely 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.


Segmentatie

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.” 


Voorspellende levering

If you haven’t heard of it before, that’s because het is een nieuwe rimpel 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 naar de game because this platform, SparkPost signalen, is the first and only email intelligence platform of its kind in the industry, and we’re proud to be offering it.


Het is een AI-voor-email explosie

Dit zijn slechts een paar van de gebieden waarop AI, NLP en machine learning een hedendaagse impact hebben op email marketing. Als je denkt dat dit het topje van de ijsberg is - of het eerste druppeltje door de sluizen - dan heb je gelijk.

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 toont meer dan 600 bedrijven 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 veel 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.

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Your new standard in Marketing, Pay & Sales. It's Bird

The right message -> to the right person -> aan de right time.