Science Fiction or Reality?
“Machine learning” has moved out of science fiction and into real-life applications, like powering Tesla cars that run on autopilot and robots that can beat humans at the Japanese game of Go. For marketers, it gets them closer to their email nirvana: true 1:1 personalization on a mass scale.
Machine learning, at its simplest, is a method of data analysis that allows computers to learn – to analyze, predict and act – without explicit instructions or programming.
That last phrase – “without explicit instructions or programming” – highlights the difference between today’s rule-based marketing automation and systems that use machine learning.
Both machine learning and today’s marketing automation systems use algorithms to analyze data and set outcomes. However, even the most complex marketing automation programs rely on specific rules or conditions that users write for them. Those rules don’t change unless someone goes in to alter them.
Out in the real world, though, customer behavior is changing constantly. Also, no two customers are alike. Segmenting the database and targeting messaging help marketers address these differences, but the rules that govern those activities don’t recognize the subtle changes that build up over time into a mass movement.
Systems that use machine learning to analyze data can generate insights that constantly adjust and refine the content sent to different customers based on their different characteristics and behavior.
Instead of sending messages based on one point of past behavior, these systems continually take in data, analyze it and use those insights to personalize messaging without requiring marketers or their IT cohorts to keep tinkering under the hood to keep up with changes.
Machine learning in action
It’s all around you, but it usually works so seamlessly you don’t realize it’s there:
Netflix: Netflix’s recommendation engine analyzes data generated by three basic sources: your preferences (what you put on your list), your behavior (what you actually watch over time) and what other people are watching (“Trending Now”). Then it sifts through all that data gained by granular tagging of all scenes within the movies, to predict what you’ll want to watch and presents those predictions as viewing recommendations.
As you watch (or don’t watch) what Netflix suggests and change your personal viewing list, your recommendations list will change to reflect your behavior.
Twitter: Many social media listening tools use a combination of machine learning and linguistic rule creations to filter out nuggets of meaningful content from the fire hose of Tweets as they flash past, whether it’s to detect customer sentiment in general or to learn what they’re saying about you in particular.
Spam filtering: ISPs use a host of factors when deciding whether to route an email to the inbox, divert it to the junk folder or block it outright. User behavior such as clicking the “report spam” button, moving an email from the spam folder to the inbox and adding a sender’s name to an address book or safe-senders list helps increase filtering accuracy for each account.
Airbnb: The travel service uses a dynamic pricing model that helps site hosts figure out how much to charge. It incorporates neighborhood location, amenities, time of year, fluctuating demand and other data points to help hosts get the most reservations at the best prices.
Machine learning for email marketers
Your fellow email marketers have been putting machine learning to work in their own programs. Here are a few examples:
Subject line optimization: Machine learning and marketing automation come together to help marketers choose the best subject lines with less time lost in testing. Here are two ways email marketers can use this technology:
Touchstone uses a proprietary algorithm to predict likely open, click and bounce rates using a simulation of an actual email database and comparing results to billions of other tests – using real data to power the results. Phrasee‘s language analysis tool predicts which emotional triggers in subject lines will drive more responses. Both services use results to refine and improve predictions over time.
Delivery time optimization: Also called “send time optimization,” this service predicts and modifies email delivery times based on when recipients are most likely to see and open messages.
Copy optimization: Persado‘s persuasion automation platform uses algorithms to find the most persuasive language for direct-response marketing messages. Its Persado Go service analyzes message drafts (email, social posts, newsletters, ad copy, etc.) and suggests revisions using natural language processing and machine learning.
Newsletter creation: Alchemy Worx has developed a newsletter automation service that streamlines newsletter creation and delivers 1:1 personalized content using machine learning that continually optimizes content choice based on recipient actions.
Real-tme Content Optimization: Cordial uses machine learning and proprietary multi-armed bandit algorithms in coordination with real-time systems to rapidly find content which is most likely to drive a conversion for each individual subscriber. The system allows unlimited variants to be tested and self optimized while reducing lost conversions.
Take the next step toward ‘always on’ optimization
The advent of machine learning doesn’t mean you have to toss out all of your rules-based marketing automation. Instead, identify areas that would benefit from continuous optimization. That’s where automation based on machine learning will drive better results without a constant injection of staff time and money.
The best way to see how machine learning solves the twin problems of lack of resources for optimization and scalability for personalization is to talk with email industry people who are making it work for their brands, clients and customers.