Introducing marketing science – a truly personalized marketing framework powered by AI

Marketing automation and advertising technologies are often presented as the silver bullet to all your marketing needs. However, the reality is often disappointing, as messaging is targeted at predefined segments and sets of rules, falling short of actual individual user needs and wants. This isn’t true personalization, as it doesn’t scale to the individual, and it isn’t capable of predicting the context, needs, behavior (or aberrations in behavior) of a single human being.

Fortunately, with the rise of sophisticated artificial intelligence capabilities, particularly machine learning, this can all change. We’re already seeing some of the more advanced players move away from formulaic segment-based messaging towards a more truly personalized way of marketing. A way where a new kind of creativity has room, and where we can employ AI to do the heavy lifting.

Marketing science combines AI capabilities with creativity – enabling results at scale

Marketing science = AI capabilities + creativity + scale

What we need is a move towards a more compassionate way of communicating that takes human contexts into consideration. This is the essence of marketing science: We attempt to understand and model the unique circumstances of each individual in real time – every time. Thanks to advances in processing power, the collection and use of data, machine learning, and artificial intelligence, this means that we are inching closer to making the age-old ideal of “segment of one” possible on all levels and in all channels. Understanding and recognizing the important moments for each individual customer, and then offering relevant content and solutions based on that context, is paramount.

With a marketing science approach, we’ll be able to use more data-based insights in creative decision-making. This means that instead of spending time on routine tactical work, like which content variant to target to which individual, humans can do what we’re best at: the putting together of disparate ideas to form creative solutions. AI is far from a threat to human creativity; instead, it’ll enable a super-charged, super-efficient creativity to emerge.

These new kinds of marketing teams will also be more diverse than ever before, as marketing science brings together creative fields with new data-driven knowledge; moreover, it has the potential to give rise to totally new kinds of hybrid competences. In the future, distinctions between different competences could well become more fluid, and we’ll see new combinations of skills. We’ll also be working in teams that consist of more than the traditional advertising art director and copywriter. What matters the most is that we’re all interested in what the person next to us is doing. This means that we could see a data scientist with content skills working together with a software engineer and a team of content specialists to jump-start creative work with insights; or a developer-art-director working together with a technology consultant and service designer on new concepts that deliver business value.

Practical data – helping you make everyday marketing decisions

Many companies are plagued by a classic dashboard orientation problem: The results of analysis are often visible on a dashboard somewhere, but they aren’t refined into insights or made actionable. The data in and by itself is just a starting point, and it should be made actionable on two levels. First, what can be automated, like hypersegmentation, will be fully automated. Second, where we need human insight and creativity, data is processed to be human-readable and understandable. This understandable data can then be refined into inputs and insights for creative work – and this work should produce more data that can be turned into inputs to close the feedback loop.

Moreover, any technological solutions for the production of insights should not be the purview of just a few specialists. Instead, they need to usable by everyone on your team. This way, insight-based modes of working are available to everyone and bottlenecks can be eliminated.

Focusing on shared, usable data has far-reaching consequences for businesses. Whereas traditional marketing approaches may change the way the marketing department is organized, marketing science has the potential to change the way the company is run as a whole. The power of marketing science comes from evolving businesses to be more customer-centric. It can help you gain significantly improved results in sales, customer experience and customer retention. Truly scalable results and continuous learning are possible when decision-making is supported by company-wide shared data and artificial intelligence applications.

Building AI capabilities requires company-wide transparency and a will to experiment

The time for a marketing science approach is ripe. AI has the potential to disrupt not only our IT systems, but how we set up and govern our organizations. We should all start preparing for these changes now and embracing the potential that they hold.

So where do you start? Fortunately, many companies already have systems in place that can be evolved towards a marketing science approach. What you keep and what you let go – in short, your business architecture – should be dictated by your business model and competitive advantages; it should not be viewed as just a collection of fixed systems, technologies or products. When you start to evolve your marketing towards a marketing science approach, the assets and services of each department will become available to the whole company to use. This in turn helps your whole company share insights and understanding, create new value and experiment with new possibilities. In this context, what matters is the capability development of your ecosystem and their value creation potential, not individual tool licences.

AI has the potential to be hugely disruptive to our companies and economies. In order to harness it, we must have courage, creativity and an ability to learn. Simply waiting for AI to “be ready” or for someone else to solve the problems won’t do – we must start implementing and using different kinds of machine-learning solutions bit by bit, learning and iterating as we go. This requires a keen will to experiment and learn, an open culture, and investment in new ways of organizing our companies. This is the foundation on which you can start building a true marketing science capability.

This is the first post in our series on marketing science. Our next post will be published soon.

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