The Real AI Superpower: Moving from Look-Back Analytics to Look-Forward Predictive Marketing

The marketing industry has spilled a tremendous amount of ink discussing the ways in which artificial intelligence and machine learning are and will continue to change how marketers work, from brand strategy and creative ideation to media execution and attribution and measurement.

But that’s missing the forest for the trees. The real shift that’s underway is far more fundamental to not just what marketers do, but how they think. In the coming years, the industry is going to pivot from being one built on “look-back” analytics to one driven by “look-forward” predictive marketing.

That’s easy to say, but harder to fully comprehend. To grasp how artificial intelligence will change our industry (and our world), we first need to consider how human intelligence works.

The Questions Marketers Ask Today

Human brains are amazing at predicting the future based on the past. Today, that’s how marketers are spending their time. Our industry has spent vast time and resources developing studies, thinking frameworks, and methodologies designed to figure out the most likely business outcomes based on past experiences (i.e., data). Marketers are using these approaches to answer key questions like:

• How can we get people to know and desire our brand?

• How can we ensure that people can find our products in stores and online?

• What is the right price for our product?

• How much should we invest in this effort or campaign?

• Who should our brand target with its ads?

• What message will convince people to purchase our product?

• What is the right media mix to reach the audiences that matter?

In relatively short order, marketers aren’t going to have to spend their time and brain power on such questions—because AI will do it for them.

Why AI Is Better Equipped to Answer Those Questions 

With the latest technology developments in AI, we are now reaching a point where the above predictions can be handled by artificial brains called neural networks — with similar or superior accuracy. We’re at a tipping point and about to witness a drastic change in both the marketing industry and our everyday lives.

Why? 

Our brains are slow and get tired easily, but computers don’t.

The revolution in which machines shattered past manual work requirements due to their ability to move mountains has now come to thinking. As we harness machine thinking power, our teams will evolve and restructure around AI to optimize our businesses. The rhythm of work, once based on human thinking, will completely change. The slow iterative marketing cycle that gives teams time to gather information, analyze past results, make decisions, and implement next steps will give way to an always-on environment of micro-optimizations. Marketers will pilot important variables, spend minimal time to analyze the past, and instead focus their energy on predicting the future.

Our input and output are limited, but computers’ are not.

Humans can only read, listen to, or watch a limited amount of information as input for our thought processes. Then we have to output what we think in concepts and languages that others can understand. Ultimately, the amount of useful thinking we produce is very limited. Machines, on the other hand, can ingest, process, and implement relevant output at incredible superhuman scale and speed.

Our memory is limited, but computer memory is not.

Humans during their very short lifespans can remember only a small amount of information and events, and without a lot of details. Because of our relatively shallow thought processes, we use conceptual shortcuts to guesstimate outcomes, whereas machines can refine the same prediction by processing very large amounts of data and computing a multitude of scenarios.

Manpower is expensive, but computers are not.

Humans eat, sleep, get sick, and take vacations. Sometimes they even decide suddenly to spend unproductive time with their spouses and kids — or just go surfing!  We’re expensive and not always available. That’s why we should be applying the cost of human work to areas where smart automation is not an option today, such as jobs that require long strides of coherence versus short “heavy lifting” tasks. The majority of what is perceived today as high-value work — research, strategy, creativity, media planning, ad operations, reporting and analytics — will become heavily automated and quickly a commodity. Marketers will instead spend their human power on understanding, in great detail, the needs of their businesses in order to help them apply, maintain, and optimize automation in a way that avoids its danger and drives responsible and sustainable business growth.

The Questions Marketers Will Ask Tomorrow

That brings us back to the questions that absorb a marketer’s time. Today, we’re focused on questions that require us to look back in time. Tomorrow, we’ll be applying our human predictive intelligence to new questions that will allow us to predict the future, like:

• What should we automate or improve first with artificial intelligence?

• What should we evolve (business models, teams, structures) to embrace this power?

• How can we build and refine our predictive models?

• How do we source and leverage qualitative, affordable, and relevant data?

• How do we do all of this in a smart, secure, responsible, and sustainable way?

What’s Holding Back This Transition from Look-Back to Look-Forward Marketing? 

This transition from look-back analytics to look-forward predictive marketing? Like winter, it’s coming. But how quickly we get there will depend on a few needed elements falling into place.

First, let’s talk about money! Every transformation comes at a price, and jumping into the “AI-powered marketing era” is no exception. It takes training, talent, and software. For a smooth, successful transition, it is paramount to draft a realistic roadmap that prioritizes AI for operational efficiency. Doing so, you can quickly save — and loudly communicate to your organization — the amount of money you need to (re)inject in more ambitious transformative projects. It’s a great way to bring your CFO along for the ride: Test fast and small, allow imperfections on non-critical parts of the business, and learn what works.

Then, let’s remember that AI can’t learn to help you with a task if you’re not clear about what you want. That means we need to be putting effort toward training AI to think and act like a marketer. To date, very few companies in our industry have made the investment and taken the time to systematically and thoroughly solidify and document their ideal processes in a way that can be learned by a machine.

Likewise, given the near-unlimited capacity of AI to process information and identify patterns, access to reliable data has become more crucial than ever. The art and science of sourcing great (and affordable) data and organizing it in a way that can be leveraged by AI to gain competitive advantage will be at the forefront of our industry evolution.

Over time, our industry is going to see dramatic shifts in the type of data that fuels it. AI’s ability to extrapolate the information it is fed means we’re going to become far less dependent on the personal, deterministic data of individuals. (That’s great news on the privacy front.) Meanwhile, new types of non-marketing data are going to become relevant to predicting marketing outcomes and building plans—data sets like weather, traffic patterns, local context, event schedules, and more. AI’s ability to connect dots among seemingly unconnected elements will broaden the range of what kind of data is useful for marketers. Finally, when and where we need to increase the amount of data we need, its granularity, or even close the gap on data we don’t have, we will leverage “synthetic data,” a new breed created for machines by machines to accurately match real data.

Digilant’s Role in the AI-Driven Future of Marketing 

AI capabilities continue to grow among the Metas, Apples, Microsofts, and Googles of the world. But to truly bring the marketing industry into a transparent, sustainable, beneficial AI-driven future, we need independent players, like ISPD and Digilant, to develop neutral and honest AI models that seamlessly integrate and leverage the power of our industry giants but without any bias toward specific media or publishers. It’s imperative that we ensure that data, creativity, and media directly serve clients’ best interests, brands and agencies alike.

As a part of ISPD, Digilant’s AI-driven marketing solutions include:

• Advanced qualitative research tools that leverage large language models (LLMs) to impersonate specific consumer personas and deep dive in their lives, behaviors, interest, and purchase decisions.
• A groundbreaking marketing intelligence platform that accurately replicates your category consumers with synthetic data to understand how your past actions contributed to your results and to inform your future decisions with forecasts.
• A new generation of brand health tracking that measures awareness, intent, and purchase at each category entry point through machine learning instead of lengthy cumbersome studies.

We’re committed to building the future of AI-driven marketing by serving as an independent, agnostic player that drives your business growth in an increasingly complex ecosystem of platforms, walled gardens, ad tech, and media. We’re excited to unlock a new path forward for marketers — one where our immense human capabilities are leveraged to their full potential, for the betterment of our brands, ourselves, and our world.