Leveraging Marketing Intelligence Automata and the Human Interface
Consumer technologies have led to unpredictable behavior between consumers and brands. The pace of change in marketing is accelerating as the adoption curve of consumer technology steepens with mobile technology creating an opportunity to reach consumers at nearly any point throughout the day. An increasing number of sensors carried by people and products create vast amounts of data making complex marketing decisions possible, leading to seismic shifts from mass communication to personalized, individualized and contextualized customer experiences.
Traditional modalities within companies have been slow to adapt to this interconnected marketing world. Reliance on software and algorithms is quite different than relying on the wetware of a Don Draper-type marketer. The necessity to have a set of rules governing each message and interaction with consumers is the antithesis of marketers having awareness of consumer motivations and behaviors in a millisecond. The ability of the marketer to know everything our systems can learn in real-time is far inferior to the potential of the machine. More importantly, we evolve through trial and variation, making marketing decisions slow to evolve if reliant mostly on human decisions. Human iteration into the next marketing variation is much harder than that of software, giving to the rise of algorithms and marketing decisions driven by the learning machine.
The architecture supporting marketing cognition is taking many forms and evolving quickly. We’ll get much better at building algorithms that can perform recursive self-improvement, dependent on increasing sophistication in discovering emotions, context, data and its structure through consumer sensory inputs. We’re already seeing a more open approach to making algorithms available for commercial use. The outsourcing of some marketing cognition will accelerate the ability to apply proprietary contexts and data in a marketing moment -- pushing current limits of marketing decisioning. Companies that succeed will be those that encourage an environment of trial and error, or more succinctly failure. Not failure instrumented by marketing executive’s decision, but rather the constant iterations of failures and successes happening millions of times over by a machine.
For marketers living in this melting pot of algorithms and machines, the human interface is critical to evolving appropriately. Integrating compassion and ingenuity is necessary. While there are many ways the human has to influence the marketing intelligence automata, consider these three:
Augmentation of our cognitive abilities
Seeing this as an augmentation of our own cognitive abilities gives us a chance to explore and exploit. This is an extension of the personality of the marketer and your company’s brand.
Enable software visualization of the data
Humans have a highly evolved form of extracting meaning from data that’s been difficult to fully replicate in a machine. This is done through impressive integration between our brain’s visual cortex and an array of specialized cognitive functions. Leverage this advantage by enabling software visualization of marketing data. There are many software advancements in this area already and the financial barrier to leveraging some visualization is nearly non-existent for most companies.
Understand consumer motivation
Marketing technologists should create a “goal ballast” underneath the marketing automata. Take the point of view of the consumer and discover their motivations. These motivations should become integrated into the goal ballast of the algorithms. Rather than starting from “how much of product can I sell in this moment?” create discrete algorithmic services to answer “how can I better understand the motivation of this customer at this moment to better service them?”
As much attention that’s paid to evolving data management techniques, there is no sufficient solution to enable an open, service-based, self-adapting marketing intelligence engine that’s combined with appropriate policy control enabling the “goal ballast.” We need to create an abstraction of the learning modules our analytics wonks create that hides the complexity that exists in the machine. Right now, it’s left to our brightest Data Scientists, Data Architects, Software Architects and Systems Architects to patch a solution together.
While algorithms exist for marketing purposes today (programmatic ad bidding/buying, product recommendation engines, etc.), what I’m suggesting is, the way marketers identify and message to consumers will be less controlled by the human and instead augmented to a higher degree by machines. There’s also a trend that would indicate that the machinations behind the scenes will operate like knowledge collective to make better and better decisions. If the cognitive architectures are constructed by embedding the appropriate “goal ballast” beneath its use and consistently allow for the human interface to adapt and survey the intentions and outcomes, we’ll enter a golden age of marketing intelligence that benefits companies and helps consumers achieve their goals.