The science of marketing isn’t simple. Marketers have to understand current technology, commerce trends, and — above all — human psychology: what drives individuals as well as broader social trends. They have to intuit how to leverage new technology on both the micro and macro levels, all while constantly revising for improved performance. Marketing is pop psychology mixed with consumer technology, with a little bit of fortune-telling thrown in, too.

Today’s emergent “third-platform” technologies, such as data analytics, mobile devices, automation, and artificial intelligence (AI), are changing the way society and individuals interact on a fundamental level. One has to look no further than the use of marketing to find a microcosm of how new software solutions, machine learning, and big-data analytics are changing the game.

 
Discover the #1 marketing platform for consumer engagement at scale.

Marketing science essentially boils down to a cycle. Depending on the expert, the marketing cycle can have anywhere from three to a dozen or more different stages. However, it’s safe to say that there are at least three main stages to a marketing campaign: data collection, strategy development, and deployment. (Rinse and repeat.)

Advances in data capturing, aggregation, and analysis have upped marketers’ games enormously when it comes to the first stage, and every day brings new automation tools that make it easier to deploy campaigns through a variety of channels. It’s in that middle part — strategy development — where humans still have to do the heavy lifting. But thanks to artificial intelligence that may not be the case for much longer.

The history of artificial intelligence dates back to the 1950s. In Ivy League research labs and back rooms of government installations, some of the brightest minds in science were hammering out a concept called “neural networks.” Their aim was to create computer programmes that mimicked the complex, interconnected nervous systems that run the human brain and allow for nuanced decision-making processes. These scientists wanted to create systems that could identify patterns, recognise significant data, and categorise information based on its importance.

While this heavy science was being created out of sight of everyday Australians, a new technological craze was transforming the way marketers approached the business of creating desire around a product. In the 1950s, television broadened marketers’ reach and forced them to learn how to target audience segments, conduct sophisticated demographic research, and systematically apply psychological principles to marketing and advertising. These skills would be critical 60 years later with the advent of digital marketing.

Television legitimised marketing as a business science, and advertising revenues skyrocketed. In 1950, gross annual ad industry billings sat at around $1.3 billion. Ten years later, the industry was moving $6 billion a year in revenue. Marketing was officially big business.

Fast forward to 2006. Digital marketing was just getting into the swing of things with online content creation, email marketing, ecommerce, and A/B testing. Podcasting was brand-new, people were starting to count clicks, and there was a lot of excitement about this new gizmo called the RSS reader. Marketers thought they were getting the hang of this digital thing.

Meanwhile, unbeknownst to most marketers (or nearly anyone outside of computer technology), Geoffrey Hinton of the University of Toronto published a paper called “Learning Multiple Layers of Representation,” which presented artificial intelligence in terms of neural networks that could do more than just classify sensory data like speech or images. These new networks could be programmed to make associations around information in order to generate data. In essence, Hinton said, these neural networks could “learn.” It was the beginning of deep learning — and yet another game changer for marketing.

New AI applications increasingly narrow the technological gap between the data collection and deployment stages to provide solutions that help in the strategic decision-making process of marketing. The beauty of AI is that in many cases it’s self-teaching, or cognitive, meaning the longer it’s in use, the more accurate and beneficial its decisions. These applications are programmed not only to replicate how the human brain works, but also to continually evolve to better mimic intelligent processes and automate them.

Advances in the area of big data have helped in the early stages of the marketing cycle by collecting and aggregating the data points that marketers need to develop strategy. At the opposite end of the cycle, automated technologies can deploy campaigns to laser-targeted demographics in a multitude of ways at near instant speeds.

But artificial neural networks can take this magic a step further by using unstructured data to draw conclusions about causes and effects within data. Because of its ability to detect and extrapolate upon patterns, AI can identify opportunities and automatically act upon them. And the more a neural network goes through this process, the faster and more accurate it becomes. These cognitive approaches are already being applied to numerous aspects of the marketing sciences, with new applications emerging every day.

Like any other science, marketing puts forth a hypothesis (proposed campaign) and runs it through rigorous testing. From web copy to design elements, calls to action to responsive design, AI can run campaigns through algorithms to enable much faster and more thorough A/B testing. The efficiency factor is a selling point in and of itself, but since these algorithms learn the more they’re used, every time a site is put through the paces the process becomes more intuitive and the results more insightful. 
The line between sales and marketing has never been more blurred. These days, these fields are grouped together under phrases like “customer experience” or “customer service.” The majority of the buyer’s journey is completed online, so consumers are looking for easy, self-serve ways to find the information they need to make an educated purchasing decision. Cognitive systems can leverage that opportunity in the form of virtual assistants (such as chatbots) that provide around-the-clock customer support. These agents can point consumers in the direction dictated by marketers, harness user data to inform sales and advertising efforts, and free up customer service agents for tasks with a higher cost-benefit ratio.
Lead generation is one of the areas of marketing where the explosion of big data was celebrated — until marketers began to panic. It’s all well and good having a massive list of leads, but how were marketers supposed to qualify hundreds of thousands of potential customers? Today, there are cognitive applications that cross-reference online consumers’ social media trends, web interactions, and mentions in public records to build a robust profile for each lead. Some algorithms can even tailor marketing messages to each lead, predict which leads are likely to convert, and execute a follow-up action if a lead chooses to go with a competitor.
Somehow it’s easier to understand AI working magic with unstructured data than with words, but there are no sacred marketing roles untouchable by cognitive processes. Now that natural language processing is a widely accepted technology, and machine learning enables algorithms to become increasingly accurate each time they’re executed, AI is being used to create marketing content and interpret users’ reactions to it. No computer can yet rival Philip Roth or Maya Angelou, but there are programs that can whip up everything from personalised content suggestions to ad copy, subject headlines to calls to action. In fact, Gartner predicts that machines will create 20% of commercial content by 2018.
 
Learn how customer experience is reshaping marketers’ mindsets in our latest survey of 3,500 marketing leaders worldwide.

No doubt, it’s a little daunting. Marketers are asking the same question that most other industries are asking: In an age when machines can do our jobs, what will there be left for us to do? Ideally, cognitive marketing processes will free marketers from busywork, allowing more time for creativity and thoroughness. Imagine how much one team could accomplish without having to spend endless hours on multivariate testing or chasing cold leads.

Every day, companies — including your competitors — are using artificial intelligence software to optimise their own processes, reduce overhead, decrease turnaround time, and improve output. Technology is evolving at an unprecedented rate, and teams already making the move to marketing AI software are at a distinct advantage to jump on the next innovation.

Kristy Blackmon has over a decade of experience writing and editing, working in daily journalism, long-form non-fiction, marketing, research, and policy.
 <a href="https://www.salesforce.com/ap/products/marketing-cloud/best-practices/marketing-ai" target="_blank"><img src="https://www.salesforce.com/content/dam/web/en_us/www/images/marketing-cloud/hub/marketing-ai/how-ai-software-is-reshaping-digital-marketing-right-now-embed.jpg" alt="How AI Software is Reshaping Digital Marketing Right Now"></a> 

Ask about Salesforce products, pricing, implementation, or anything else — our highly trained reps are standing by, ready to help.