The Role of Collaboration in a Human Approach to AI

There is a palpable anxiety among marketing professionals wary that advanced technologies are coming for their jobs. Artificial Intelligence (AI) and machine learning, in their esteem, mean an army of robots and data scientists that will soon rule over the machine learning marketing technology stack with an iron fist, leaving little room for the marketing mortals among us to spread our creative wings.

“Look,” they say, “you have to be a data scientist to even grasp the sheer magnitude of the 2.5 quintillion bytes of data created worldwide every day, so what chance do the rest of us have to operationalize that type of volume in our own marketing organizations? Look at the estimated 50 to 60 percent gap between the current supply and the requisite demand of deep analytical talent. We’re doomed.”

Hyperbole aside, marketers should instead view AI and machine learning as tools that will better equip them to reach their goals, allies in the struggle to arrive at hyper-personalized, zero segment marketing. The brass ring of engaging with millions of customers in a personal, omnichannel experience is in fact unachievable without the use of advanced technologies.

Once marketers recognize this truth they will be in a position of strength and can better understand and appreciate how AI and machine learning can help them become even more strategic and creative. Just as a musician might purchase the latest effects pedals to amplify and embellish the tone of a guitar, the high-tech gear doesn’t replace or diminish the effort of playing the right chords and hitting the right riffs with their instrument in time and in tune. There will always be a need for a hands-on, human approach to reach rock star status for an optimal customer experience: Real-time customer engagement.

AI Does the Heavy Lifting

Let’s look at how AI and machine learning can benefit a concrete example for retailers, optimizing the path to purchase. With an individualized, segment-of-one approach a brand or retailer strives to provide each customer with a recommended and relevant next-best action at every touchpoint and across every channel. A real-time customer engagement solution powered by machine learning can do what humans cannot, which is to account for thousands of permutations along a path to purchase for millions of customers and generate that next-best recommended action in milliseconds.

A real-time solution does the heavy lifting for the marketer, while leaving room for creative input. Using business rules that orient decisioning for inbound and outbound channels, algorithms for model building and model training can automate recommendations that provide an optimal customer journey design path. Where, then, does that leave the marketing organization, or at least those without advanced degrees in data science?

Simplified Analytics for the Marketer

It’s a legitimate concern for marketing, troubled by the feasibility of creating a hyper-personalized customer engagement when automation seems to strip away the human component. What are the areas where marketers can make their impact felt?

First, there’s testing and oversight. Marketing still must have the ability to choose and test models, deploy the most effective model and alter model settings based on changing business rules. A real-time customer engagement solution that provides marketers with oversight capabilities helps to simplify analytics for marketing, because keeping it under the purview of the end-user helps them see analytics at work.

In a marketing-managed AI approach, guided screens take the end-user through options to construct models based on business goals. A user selects a schema from a menu of default settings, uploads their data, runs the model, and visualizes the results in an easy-to-use dashboard.

The above scenario is a collaborative effort where AI and machine learning are augmenting formerly human tasks to assist the marketing practitioner with delivering an engaging experience. In this spirit of collaboration with AI and machine learning, marketing will likely have to re-tool some processes, with the understanding that collaboration helps achieve a better end result – a hyper-personalized customer experience – than that which could be achieved either by machine or human alone.

Technology as a Collaborative Partner

With the mindset that AI is a collaborative partner, marketing can then start to appreciate the technology as an enabler of more powerful human-centric experiences. This speaks to the creative aspect, another area where marketers can make an impact.

On one hand, AI and machine learning facilitate the critical capabilities of delivering a real-time experience; identity resolution, contextual insight, delivering the right content, orchestration, and ongoing optimization. This is all done behind the scenes in real time unbeknownst to the customer. On the other hand, in the context of day-to-day marketing, marketers now have ready access to more data which can help unlock creativity, such as enable in-store clienteling experiences that recommend products based on preferences, history, and behavior patterns. In this context, marketing remains the creative force in developing AI-enriched content and plays a vital role in helping to determine which interactions, recommendations, or content are authentic approximations of human-centric engagement, and which are likely to alienate the target audience of one.

Once marketers dive into use cases and begin to appreciate AI and machine learning as collaborative and innovative partners, they’ll stop fearing obsolescence. Instead, they’ll recognize the advanced technologies for what they really are: analytics for the common man, arming them with just enough tools to be dangerous.


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