Machine Learning: Symbiosis Like Marketers Have Never Known Before

In this still-developing realm, marketers have all kinds of definitions for machine learning, but this is clear: The relationship between humans and machines can be mutually beneficial.

Machine Learning: Symbiosis Like Marketers Have Never Known Before

by Andy Betts

Posted on 09-08-2017

Machine learning is driving radical new targeting capabilities and creative processes, giving brands greater insight into individual needs and preferences. As marketers, it’s only over the more recent part of the past decade that we’ve sat up and taken notice of this subset of artificial intelligence and its potential in marketing and advertising.

As Frank Palermo, head of the Global Technical Solutions Group at VirtusaPolaris, told CMO.com: “AI can power a deep understanding of customer behavior and can provide decisions on what time, which channel, and which message variant to use for interaction. Leveraging AI tools to process data more efficiently and uncover new insights faster provides a significant competitive advantage.”

Are you and your team ready for this transition? The key, we’re discovering, is in mastering the balance between your human capital and the power of machine learning.

Back In The Day

Machine learning as a knowledge-based practice began back in 1950, when Alan Turing developed the Turing Test to determine if a person could distinguish whether a machine or another person was asking a set of questions.

By the 1990s, scientists were working on programs to allow computers to analyze large volumes of data and draw conclusions, marking a shift to data-driven machine learning. This is what really opened the door to machine learning’s application as an indispensable marketing technology.

In this still-developing realm, marketers have all kinds of definitions for machine learning, as a recent #AdobeChat on machine learning attests:

Machine learning takes artificial intelligence—computer programs that make data-based decisions and perform tasks we’d normally rely on human intelligence—a step further, in that the program “learns” and improves without humans constantly reprogramming and inputting new data. Google’s RankBrain algorithm (one facet of its overarching Hummingbird search ranking algorithm) is a great example in that it evaluates the intent and context of each search query, rather than just delivering results based on programmed rules about keyword matching and other factors. In fact, this machine-learning application has become the third most important ranking factor, according to Google.

Balancing Human Capital With Machine-Learning Power

We now have access to massive amounts of consumer data generated by countless interactions and transactions. The challenge is in learning to manage all of that data in order to make the smartest decisions. It’s become clear that humans don’t have the capacity to keep up.

Does that mean we’ve become redundant and should be replaced by machine overlords? Absolutely not. Machine learning will not replace human empathy, judgment, and creativity. What marketing needs now are creatives with both analytical thinking and creativity skills to drive the machines.

But you’ve heard this before, haven’t you? So let’s take it a step further: What’s truly excitingare the ways machines will help humans become better at their jobs. And that means more than machines performing automated tasks. With an application such as Adobe Sensei, for example, insights gleaned by the machine are then used to enhance the skills and creativity of the human. (Note: Adobe is CMO.com’s parent company.)

Are you ready for this supercharged environment? Here are three action items to get a head start:

Begin making the case for machine learning in your organization: Which complex customer experience challenges are you looking to solve, and what are your existing software and personnel challenges? Which events and behaviors might machine learning help you predict? Which existing functions could machine learning improve?

Assess your data assets and stewardship capabilities: Which data is available for you to learn from? Are there regulatory or other issues to consider? Is there data that’s more difficult or expensive to tap into than other sources? Selecting which data you’re going to use within the context of a machine learning platform allows you to allocate resources to keeping that data clean, properly formatted, and ready for action.

Determine what additional human capital you’ll need. Will you need data scientists? Data architects? What exactly will their roles entail? Who will they report to, and who reports to them? Do you have the capacity for full time, or will you be looking for contractors or consultants?

The proliferation of machine learning in marketing means we’re no longer looking for some unicorn human with perfectly balanced left- and right-brain skills and thought processes to input data and extract insights from an automation tool. We can empower our teams with tools that help fill in human-capital gaps. We can maximize the potential of our machines with human operators who understand their capabilities and are completely on board, rather than threatened.

And you can do all of this based on best practices and advanced knowledge gleaned from the automated evaluation of more data points than a human could sift through in a lifetime.

Topics: , CMO by Adobe

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