The AI Revolution in Digital Marketing

Alexei Kounine
December 13, 2021

(A version of this article appeared on AIthority.com, December 21, 2021)

The rapid rise of artificial intelligence in the digital marketing field has been nothing short of revolutionary. And only a few years ago, it still took vision to realize this enormous potential. That’s because back then, 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 Marigold Recommendations, our own marketing-specific AI engine at Marigold Engage, following years of development. Instead of ‘adapting’ a generalist AI to marketing purposes, we built Marigold Recommendations from the ground up to think and act like a marketer, but at a super-human scale.

Turns out, we made the right bet.

Because since then, the number of marketers using AI has skyrocketed, as the technology went from cutting edge to status quo practically overnight. The early adopters who had the vision to trust in AI have been reaping the rich rewards ever since. And more are joining the revolution: 77% of retailers are moving to implement AI in 2021.

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 cross channels and devices at scale – with individualized engagement and journeys for every customer.

For marketers looking to get the full scoop on the evolution of AI engines in marketing, we have created a free eBook: The Artificial Intelligence (AI) Revolution in Digital Marketing. Available for download now, it delivers an overview of how AI and machine learning came to raise the bar in engagement marketing – to the point of becoming must-have tools in the marketing quiver. And it also offers successful strategies for using AI to navigate the new reality of life after the pandemic.

Quick Primer: Artificial Intelligence vs. Machine Learning

As a preview to the new eBook, let’s start with a quick primer about what distinguishes ‘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 which 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.” For all newly developed algorithms, our engineers spend a great amount of time testing and ensuring the quality of a feature using the algorithm – for instance, identifying the most effective marketing message for a specific audience – until it delivers the desired results.

The (Rapid) Evolution of Marketing-Specific AI Engines

Because of the self-optimizing nature of machine learning (ML) systems, the evolution of features and capabilities within marketing-specific AI engines has been rapid over the past years. At first, algorithms were developed to automate some of the most important tasks that marketers do on a daily basis, namely: identifying customer segments, optimizing customized journeys, and delivering personalized product recommendations with features like:

Smart Content, which lets marketers dynamically personalize content and offers, uniquely tailored to each consumer’s situation. The algorithms combine behavioral and contextual data for each customer with marketer-specific business logic to boost conversion rates by up to 30 percent.

Smart Audiences, 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. Based on all the data available in consumers’ profiles, the AI engine selects the right targets for specific offers and constantly monitors performance to fine-tune selections on the go.

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 based on engagement behavior and past interactions – and sends customized content at the right time.

While these features are changing the game for many companies, it’s important to keep in mind that only very few ‘marketing AI’ engines have been custom engineered to think and behave like marketers. That’s because the majority of AI engines are designed to make sense of data from a generalist perspective. But truly marketing-oriented engines are designed as sets of self-learning systems to solve specific marketer-focused tasks and gradually evolve over time.

The AI Revolution in Digital Marketing Continues

When it comes to evolving, an AI platform is only as ‘intelligent’ as the data it plugs into. So it’s crucial that an engine is integrated with the rest of a marketing platform – especially all the transactional and preferential data in customer profiles – so it can leverage all the data available to learn. At Selligent, these profiles – updated in real time – connect directly to our AI engine for super-charged relevance.

This platform-based approach – with AI and customer data at the center of Marigold Engage – not only lets us train our systems to ‘think’ like a marketer and boost marketer-defined KPIs. It also puts the platform on a course to keep learning and expanding its functionalities, which is why truly self-learning AI systems are the only way forward for companies looking to benefit from long-term ROI from their AI investments.

As we speak, investments in AI are at an all-time high. By the year 2025, AI will power 95% of all customer interactions according to Selligent partner Servion. And about a quarter of our Selligent clients are already using one or more of our Marigold Recommendations features. In that light, the self-improving nature of marketing AI platforms will help marketers to spot trends and respond with expanded functionality that keeps customers connected.

Ready to join the (r)evolution? If you enjoyed this quick-start guide, make sure to download the entire eBook, The Artificial Intelligence (AI) Revolution in Digital Marketing, for deeper insights into what’s next and proven digital marketing strategies to stay connected to your audiences in this new reality.