Artificial intelligence (AI) is the new black, the new shiny object, the answer to all marketers’ prayers, and the end of creativity. The recent emergence of AI from the arcane halls of academia and the back rooms of data science has been fueled by stories of drones, robots, and self-driving cars being undertaken by tech giants like Amazon. Google and Tesla. But the hype outweighs the day-to-day reality.
AI has a fifty-year history of mathematical and computational development, experimentation, and thought. It is not an overnight sensation. What makes it exciting is the confluence of big data sets, improved platforms and software, faster and more robust processing capabilities, and a growing pool of data scientists eager to exploit a broader range of applications. The prosaic everyday uses of artificial intelligence and machine learning will make a bigger difference to the lives of consumers and brands than flashy apps hyped in the press.
So consider this AI reality check:
Big data is messy. We are creating data and connecting large data sets at an extraordinary rate, multiplying every year. The growth of mobile media, social networks, apps, automated personal assistants, wearable devices, electronic medical records, self-reporting cars and appliances, and the upcoming Internet of Things (IoT) create tremendous opportunities and challenges. In most cases, there is considerable and lengthy work to align, normalize, fill in, and connect disparate data long before any analysis can begin.
Collecting, storing, filtering, and connecting these bits and bytes to any given person is complicated and intrusive. Compiling the so-called “golden record” requires considerable computing power, a robust platform, fuzzy logic or deep learning to link disparate data, and adequate privacy protections. It also requires considerable skill in modeling and a cadre of data scientists capable of seeing the forest rather than the trees.
One on one is still aspirational. The dream of personalized one-on-one communication is on the horizon, but it remains an aspiration. Triggers are the need to develop common protocols for identity resolution, privacy protections, an understanding of individual sensitivities and permissions, the identification of tipping points, and a detailed chart of how individual consumers and segments move to through time and space on his journey from necessity. to brand preference.
Using AI, we are in an early test-and-learn phase led by companies in the financial services, telecommunications, and retail sectors.
People’s Choice Award for Predictive Analytics. Amazon trained us to expect personalized recommendations. We grew up in line with the idea that “if you liked this, you probably like that”. As a result, we expect our favorite brands to get to know us and responsibly use the data we share, knowingly or unknowingly, to make our lives easier, more comfortable and better. For consumers, predictive analytics works if the content is personally relevant, useful, and perceived as valuable. Anything less than that is SPAM.
But making realistic and practical predictions based on data is still more of an art than a science. Human beings are creatures of habit with some predictable patterns of interest and behavior. But we are not necessarily rational, frequently inconsistent, quick to change our minds or change our course of action, and generally idiosyncratic. AI, which uses deep learning techniques in which the algorithm trains itself, can help make sense of this data by monitoring actions over time, aligning behaviors with observable benchmarks, and evaluating anomalies.
Platform proliferation. It seems like every tech company is now in the AI space making all sorts of claims. With over 3,500 Martech offerings plus countless legacy systems installed, it’s no wonder marketers are confused and IT technicians are locked in. A recent Conductor survey revealed that 38 percent of marketers surveyed used Martech 6-10 solutions and another 20 percent used 10-20 solutions. Cobbling together a cohesive IT landscape in service of marketing objectives, perfecting the throttling of legacy systems and existing software licenses while processing massive data sets is not for the faint of heart. In some cases, AI must work around installed technology platforms.
Artificial intelligence is valuable and evolving. It’s not a silver bullet. It requires a combination of skilled data scientists and a powerful contemporary platform driven by a customer-centric perspective and test-and-learn mindset. Operated in this way, AI will deliver much more value to consumers than drones or robots.