The increasing sophistication of digital marketing and advertising has transformed how enterprises connect with consumers and ultimately improved how products are sold.
Artificial intelligence, for example, now provides marketers and advertisers with an unmatched opportunity to leverage existing database marketing concepts, connecting them with AI concepts and models such as machine learning, to accomplish this. What's unique about AI in the advertising/marketing space is the introduction of the "reasoning" element, which leverages performance by a computer and an algorithm instead of a human.
AI-based marketing provides a set of tools and techniques that play well into behavioral targeting - one of the hottest digital trends of the past decade. "Behavioral" is essentially based on a technology cycle of perception, reason, and action similar to what occurs in cognitive science. In the context of marketing, it comes in the form of collect, reason and act. Let's take a closer look.
In the collection phase, the aim is to capture any and all activities which provide some data on the customer or prospect. This can be gathered both online and offline, and then saved into a database. Within the reasoning phase is where the collected data is transformed into information and eventually, when done right, intelligence and insight. This is really the core of what AI/artificial intelligence is and taps into the real power of machine learning. Finally, the act phase fully leverages the information/intelligence gathered, and assists marketers in their pursuit of influence over a prospect or customer's purchase decision, often using some incentive driven message. AI has a significant role in the action phase of course. Essentially what these systems provide is an opportunity to take advantage of computer algorithms ability to learn and it has been a powerful driver in generating more effective behavioral targeting.
The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory. Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data.
So what companies are leading the way in artificial intelligence in the digital marketing and advertising space? Most companies are now expert at collecting information, but only those with machine learning algorithms in place are those that can be designated as providing some AI capabilities. RocketFuel, for example, has made big waves in the digital advertising industry but there are many other programmatic advertising exchanges that taking advantage of machine learning to provide the intelligence required to intelligently recommend information to users and provide an optimal experience - one based on previous behaviors and current trends.
