Do You Know Your Andres?

There is a grocery store a few miles from my house. It’s small and older, at least thirty years in its current location. Usually, the shelves are poorly stocked with a limited selection compared to the newer stores surrounding it. Despite these facts, the store manages to stay in business which is somewhat hard to comprehend given the cut throat, low margin nature of the industry. It survives because it has a secret weapon.

His name is Andres. He’s a cashier and has been at the store for twenty plus years. Andres speaks five languages and knows most of the customers by name, typically, greeting them in their native language. He knows where everything is, or isn’t, and if it’s not there he knows when it will arrive. He is the store.

While some customers, like my wife, frequent the store because it’s convenient, and quick, as long as the item is on the shelve. The majority of the customers go because of Andres. The store is in an affluent international neighborhood with many retirees. These core customers have time to shop and chat with Andres. For them, a trip to the store is an experience, not an errand. I haven’t seen the numbers, but I would guess that the revenue per square foot is why it survives.

The interesting thing, having worked with B2B companies for the past twenty years, is that many of my past clients also have an “Andres.” His, or her name may be different, but their role inside their organizations are not unlike Andres. They know the customers, how to get things done, where the “dead bodies” are buried, and how to navigate the complexity of the organization. They are the company.

As organizations rapidly move to “digitalization” and look for AI to play a larger role in customer interactions, they need to consider the importance of these essential employees. Like the grocery store, there are customers who may be highly profitable that aren’t doing business with your company because it’s convenient or fast. They are and have been customers, because of the experience. And a good portion of that experience is shaped by the “Andres” of the organization.

As other grocery stores move quickly to eliminate cashiers, Andres’s store has no self-checkout or online store pickup. Management seems to recognize the importance of the shopping experience, which seems to make up for the lack of selection and inventory. As your organization moves toward the future, does the management team fully understand that not all customers are the same, or want the same things. They may also speak separate languages and while self-service may work well for some, others want the full experience, which may include a personal conversation with their “Andres.”

How Endpoint Computing Could Dehumanize Communication

Where does the signal to pull your hand away from heat originate? If your answer is the brain, you’ve already been burned. Instinctively, we pull our hand back without conscious thought, because the response to the stimulus takes a short cut and originates in the spinal cord because the need for quick action.

According to venture capitalist Peter Levine the need for this same type of short cut may be happening soon with computing. Mr. Levine said thathe saw a shift in computing coming from the cloud (centralized) to the return of edge computing (decentralized) because the wave of innovations from IoT, and AI, are driving the need to have decisions made in milliseconds.

As Mr. Levine points out, a connected car is basically a data center on wheels “it has 200 plus central processing units…doing all of it’s computations at the endpoint and only pass back to the cloud.” Just like you hand doesn’t have time to send a signal to the brain, autonomous vehicles need to react instantaneously to the situation.

Data, insight, and now action, will be moving to the point of engagement in this future view. Now think about the potential challenges that present marketers in staying on brand, and controlling the message with thousands, or even millions, of touchpoints acting independently. Today, the best messaging and value proposition work can (and usually does) go off the track the moment it makes its way to sales and service reps.

Marketers live with the daily issue of cross channel attribution, add cross channel communication to the mix and we better have really good tracking tools! Sure, we can pre-set the messages, designed algorithms to present them at the right moment in the buying cycle, but controlling and tracking the delivery of each message in the context of an overall brand story will be the challenge.

And keep in mind, machines aren’t the only things that learn. As research has shown, the buying process is a highly emotional roller coaster. With machines entering that process we risk driving efficiency at the expense of dehumanizing the experience. As machines learn, we also begin to sense whether we are dealing with a human or a machine.

For example, do you really get the “warm fuzzies” from all those “HBD” messages on Facebook, or the “Congrats on the New Job” on LinkedIn? Machines have been great at helping us be more informed, but they have also have made it easy to turn highly personalized interactions into transactional tasks, void of any emotional connection.

The first wave of machine learning has been about improved efficiencies, productivity and predictability. As Jeff Bezos stated in his brilliant letter to shareholders,  “Machine learning drives our algorithms for demand forecasting, product search ranking, product and deal recommendations…much of the impact of learning will be of this type – quietly, but meaningfully, improving core operation.”

As the next wave approaches, we should be cautious on how it is applied to the buying process. The focus should be on making humans more human, becoming more instinctive, so potential customers don’t getting burned.

Why We Are Ripe For AI

It’s coming, the “futurists” are saying that the hype about Artificial Intelligence is real. The reason according to Andrew Ng, chief scientist at Baidu, is that AI is no long a “magical thing” but is now creating real value for companies, like Google and Baidu. Companies are now finding “pockets of opportunity” to invest in AI. But there is also something else at play that is also making the timing right for AI.

Americans are now living in highly polarized political environment. We’ve seen it play out in TV commercials, “resistance movements,” and daily news coverage.

At the same time, researchers have recently shown that it’s more than a person’s mindset that determines their political beliefs; it’s their actual mind itself. More specifically, the physical structure of the brain of those people on the ”right” and the “left” are different, and it impacts how information is interpreted, decision are made and how you see the world.

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People who describe themselves as “liberals” tend to have a larger anterior cingulate cortex, the area that is responsible for taking in new information and that impact of the new information on decision-making. Meanwhile, “conservatives” tend to have a larger right amygdala being a deeper brain structure that processes more emotional information, in particular, fear-based information.

As a result, the adult world is made up of, to a certain degree, two hard-wired types of people, who see and interpret the world differently. In fact, according to the Pew Research Center there has been a dramatic political polarization of Americans over the last 20 years (see the graph below).

Put it all together and you have a perfect scenario for AI machine learning. Machines look for consistency in patterns to make predictions, and apparently we have become more predictable than ever before. Using psychographic segmentation along with online research tools, machines can more accurate and effective target and message to unique audience segments.

Our minds are already predisposed to interpret information differently. Layer on that our opinions and beliefs are becoming more distinctly aligned with other like individuals and you’re seeing the “middle” is disappear.

These distinct groups also use unique channels for information and communication that reinforce their beliefs and opinions, making it easier to find and message to them. In the end, the target, channel and message are all becoming increasingly more defined as a result.

While polarization is making it more difficult for one group to understand the other, it is making humans a lot easier for machines to understand.