How AI has evolved in the marketing industry – and where it’s headed next
Throughout history, the basic purpose of marketing hasn’t changed. As in generations past, the role of the marketer today is to encourage consumer engagement in hopes of driving purchases and building brand loyalty. But while the function largely remains the same, achieving the ultimate customer experience has dramatically evolved with the arrival of personalization and artificial intelligence (AI)-based technologies, equipping practitioners with an entirely new and sophisticated set of tools to best connect with their target audiences.
These advancements allow for the automatic delivery of relevant, tailored customer experiences in a much simpler, faster, and more efficient way than was ever possible before.
In fact, according to a Demandbase survey, by 2019, 84 percent of marketing and sales professionals were either already using AI as part of their business operations or were in various stages of planning and implementing their AI strategies.
But what has the journey looked like for brands as it relates to these intelligent systems, how have they influenced the marketing workflow, and where do we see AI continuing to reshape the industry?
Let’s trace its recent history through the lens of Dynamic Yield’s growth within the space and evaluate the lessons learned as well as our predictions for what the future has to hold.
In the late 2000s, AI, and more specifically, machine learning algorithms, were mostly found within theoretical discussions in academia or left to the large tech companies such as Google and Amazon.
It wasn’t until around 2009 that machine learning gained its legs and initiatives like the Netflix Prize, a content recommendations algorithm contest, catapulted the industry’s know-how forward and demonstrated its potential commercial applications within the martech landscape and beyond.
Soon after, new disruptive algorithms dubbed ‘Contextual Bandits,’ ‘Collaborative Filtering,’ and more offered marketers greater accuracy, efficiency, and scale in how they delivered, analyzed, and optimized experiences across the customer journey.
These would eventually become the industry standard, but despite the many benefits and positive impacts on performance, early adopters wanted to better understand how the technology worked.
After all, if AI and machine learning were going to replace the deep product knowledge and domain expertise gained by marketers throughout the years, it only made sense that they would want to know exactly what went into the calculations and why, for example, a certain user was served a particular piece of content over another.
Simply put, AI could not function as a black box.
To meet the marketers’ growing demand for greater control over and understanding of the outputs, the algorithms implemented and technology had to adapt.
