Only a few years ago, very few companies believed that marketing-specific AI engines would be where marketing was going. In 2018, a mere 29% of marketers used AI in their programs. This was the same year that we launched Selligent Cortex, our own marketing-specific AI engine at Selligent Marketing Cloud, following years of development.
Since then, the number of marketers using AI has skyrocketed. It recently increased to a new high of 84% over the course of 2020, as the technology went from cutting edge to status quo practically overnight. And more are joining the revolution: 77% of retailers are moving to implement AI in 2021.
Top AI ML Trends: AI Will Have a Record Year in 2022, but Not the Way You Think
The fundamental attraction of marketing AI has remained the same: AI lets marketers draw on real-time customer data to deliver ultra-personalized, highly relevant customer experiences across channels and devices at scale – with individualized engagement and journeys for every customer. But as our engineering teams at Selligent spent the last few years building and training such algorithms (with trial, errors, and learnings at every step), I can honestly say that all ‘marketing AI’ is not created equal.
ARTIFICIAL INTELLIGENCE VS. MACHINE LEARNING
Starting from the beginning, there is still some degree of confusion regarding the terms – Artificial Intelligence and Machine Learning.
From a technical standpoint, artificial intelligence is basically an umbrella term for everything related to making machines perform tasks as human brains do. It is about reasoning, planning, learning, decision-making, and so on. To make it happen, computers rely on algorithms that analyze data, derive statistics from it, study performance metrics, and adjust future behaviors – like humans would.
Now, the interesting part is that humans don’t need to ‘program’ or ‘instruct’ artificial intelligence engines to perform specific tasks. Instead, the engines can rely on machine learning and figure it out themselves. Machine learning uses special calculations (algorithms) to process data, find trends in this data, and finally use these trends to predict.
In that sense, machine learning is radically different from giving precise instructions to a machine to make it perform a specific task. In machine learning, the “learning” part simply means that a mathematical model is created from data by an algorithm to perform a specific function. In the next step, the model is simply used in the software code to make real-time predictions until a new model is created from the next “learning phase.”
THE (RAPID) EVOLUTION OF MARKETING-SPECIFIC AI ENGINES
Because of the self-optimizing nature of machine learning (ML) systems, the evolution of marketing-specific AI engines has been rapid and now includes ‘smart’ features like:
- Automated, personalized content creation, which lets marketers dynamically personalize content and offers, uniquely tailored to each consumer’s situation. To make it happen, the algorithms combine behavioral and contextual data for each customer with marketer-specific business logic.
- AI-based audience definition, which puts rocket boots on customer segmentation by predicting who the right target audience is going to be for specific content and initiatives the marketer is looking to push.
- Send Time Optimization (STO): Wouldn’t it be nice to target an email or message to the exact time when an individual customer tends to be most receptive to messaging? This feature calculates the ‘sweet spot’ when a specific customer is most engaged.
In terms of upcoming new features, two things are on the immediate horizon. First, the use of textual data from customer chats, messages and emails to learn more about their specific preferences and motives and make predictions on what kind of content would resonate with them. AI engines could then use these insights to automatically tailor subject lines of emails to every individual customer.
And second, marketers need to increasingly be able to respond to customer demands in real-time. This means capturing all transactions – in real life and e-commerce – instantly and tailoring a smart, automated response based on actionable data.
THE NEXT CHAPTER OF MARKETING AI
The evolution of marketing AI continues. The Covid-19 crisis has accelerated processes of digital transformation by several years, in some cases decades. Amid the disruptions of the global pandemic, 88% of small businesses and 80% of large enterprises said that AI helped their companies during the Covid-19 outbreak according to a KPMG report.
Looking ahead, the self-improving nature of marketing AI platforms will help marketers to spot trends and respond with expanded functionality to what customers really want. All that marketing teams need is frequent software updates. Your marketing AI engine will do the rest for you.
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[To share your insights with us, please write to sghosh@martechseries.com]
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