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The fundamental purpose of marketing has not changed throughout history. As in previous generations, the marketer's role today is to encourage consumer engagement in the hope of driving purchases and building brand loyalty. While the function remains largely unchanged, achieving the ultimate customer experience has dramatically evolved with the introduction of personalization and AI-based technologies, providing practitioners with an entirely new and sophisticated set of tools to best connect with their target audiences. These breakthroughs enable the automatic delivery of relevant, tailored customer experiences in a much simpler, faster, and more efficient manner than was previously possible.
According to a Demandbase survey, by 2019, 84% of marketing and sales professionals were either already using artificial Intelligence in their business operations or were in various stages of planning and implementing their AI strategies. But, how has the journey for brands been with these artificial intelligence systems, how have they influenced the marketing workflow, and where do we see AI continuing to reshape the industry? Let us examine its recent history through the lens of Dynamic Yield's expansion within space, evaluating the lessons learned as well as our predictions for what the future holds.
Artificial Intelligence and more specifically, machine learning algorithms, were mostly found in theoretical discussions in academia or left to large tech companies like Google and Amazon in the late 2000s. It wasn't until around 2009 that machine learning gained traction, with initiatives such as the Netflix Prize, a content recommendations algorithm competition, catapulting the industry's know-how forward and demonstrating its potential commercial applications in the martech landscape and beyond. Soon after, new disruptive algorithms such as 'Contextual Bandits,' 'Collaborative Filtering,' and others provided marketers with greater accuracy, efficiency, and scale in how they delivered, analysed, and optimised customer experiences across the customer journey.
These would eventually become industry standards, but despite the numerous benefits and positive effects on performance, early adopters wanted to learn more about how the technology worked. After all, if AI and machine learning were to replace marketers' years of deep product knowledge and domain expertise, 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 specific piece of content over another. Simply put, AI cannot operate as a black box.
To meet marketers' growing demand for greater control and understanding of outputs, algorithms and technology had to evolve. At Dynamic Yield, for example, we introduced new algorithms that were simple to explain, understand, and predict. This resulted in widespread adoption and, as a result, numerous improved experiences that had a significant business impact. We also allowed marketers to A/B test algorithms against the existing control or other algorithms, with the marketer having the final say on which strategy to use (based on the business results yielded from each algorithm). At Dynamic Yield, our Predictive Targeting solution was designed to "detect" personalization opportunities, i.e., data-backed suggestions that teams could "click to apply" for additional projected revenue gains, rather than automatically applying what the algorithm should recommend. By 2017, more and more brands began to see AI as a vital tool for augmenting the decision-making process, and gaining the trust of marketers. And, ironically, as AI began to permeate many aspects of daily life in the form of personal voice assistants, smart home devices, web search results, self-driving vehicles, and expanded content and product recommendations, a shift in mindset occurred once more.
