Artificial intelligence (AI) is the new black, the shiny new object, the answer to every marketer’s prayer and the end of creativity. The recent emergence of AI from the mysterious halls of science and the backrooms of data science has been prompted by stories of drones, robots and driverless cars being brought to life by tech giants like Amazon. Google and Tesla. But the hype goes beyond everyday reality.
AI has a fifty-year history of development, experimentation and thinking in mathematics and computer science. It’s not an overnight sensation. What makes it exciting is the confluence of large data sets, improved platforms and software, faster and more robust processing capabilities, and a growing cadre of data scientists striving to leverage a broader range of applications. Hard-nosed, everyday uses of artificial intelligence and machine learning will make a bigger difference in the lives of consumers and brands than the flashy applications touted in the press.
So consider this AI reality check:
Big data is messy. We create data and connect large datasets at extraordinary rates that multiply every year. The growth of mobile media, social networks, apps, automated personal assistants, wearables, electronic health records, self-reporting cars and devices, and the upcoming Internet of Things (IoT) create tremendous opportunities and challenges. In most cases, there is considerable and tedious work to match, normalize, populate, and join disparate data well before any analysis can begin.
Collecting, storing, filtering and connecting those bits and bytes to a specific person is difficult and obtrusive. Creating a so-called “golden record” requires significant computing power, a robust platform, fuzzy logic or deep learning to link disparate pieces of data, and adequate data protection. It also requires significant modeling skills and a cadre of data scientists able to see the forest, not the trees.
One-to-One is still ambitious. The dream of personalized one-to-one communication is on the horizon but still ambitious. The gating factors are the need to develop common protocols for identity resolution, protecting privacy, understanding individual sensitivities and permissions, identifying tipping points, and providing a detailed account of how individual consumers and segments are performing in their journey of necessity moving through time and space to brand preference.
We are in the early stages of testing and learning with AI, led by financial services, telecom and retail companies.
People Prize Predictive Analytics. Amazon trained us to expect personalized recommendations. We grew up online thinking, “If you liked this, you probably will.” Therefore, we expect favorite brands to know us and to responsibly use the data we share, knowingly and unknowingly, to make our lives easier, more convenient and better. For consumers, predictive analytics works when the content is personally relevant, useful, and perceived as valuable. Everything else is SPAM.
But making realistic, practical data-driven predictions is still more art than science. Humans are creatures of habit with some predictable interest and behavior patterns. But we are not necessarily rational, often inconsistent, quickly change our minds or act quickly and generally unconventionally. AI can help make some sense of this data by monitoring actions over time, aligning behaviors against observable benchmarks, and assessing anomalies, using deep learning techniques in which the algorithm trains itself.
proliferation of platforms. It seems every tech company is now in the AI space making all sorts of claims. With more than 3500 martech offerings in addition to countless legacy systems installed, it’s no wonder marketers are confused and IT folks handicapped. A recent survey conducted by Conductor found that 38 percent of marketers surveyed were using 6-10 martech solutions, and another 20 percent were using 10-20 solutions. Cobblering together a coherent IT landscape to serve marketing goals, refining the limitations of legacy systems and existing software licenses while processing massive amounts of data is not for the faint of heart. In some cases, AI needs to bypass installed Technology platforms.
Artificial intelligence is valuable and evolving. It’s not a silver bullet. It requires a combination of experienced data scientists and a powerful, modern platform, guided by a customer-centric perspective and a test-and-learn mentality. Operated in this way, AI will offer much more value to consumers than drones or robots.
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