AI and the Black Box Problem

5 minute read

Upland Admin

It’s been said that marketing sits on the largest data stores in any company, and it stands to reason. Customer and prospect databases, activity records, scoring, attribution, and the myriad ways we engage with customers and prospects add significantly to our daily data volume. In the past five years, we’ve become more accustomed to, and reliant upon, measuring everything.

But can we?

The Black Box Problem

Even with advanced analytics and business intelligence available in most (if not all) applications, there are still moments when we confront the black box of the unmeasurable. And it’s around our content. Yes, we know how it performs, when and how it’s used, and by whom.

While these are by no means easy to measure, or even gather the data in one place, it’s still possible. But there’s one attribute that remains elusive: What on earth does our content say? Perhaps more importantly, does it say what we want it to say? As we create our content strategies, craft our go-to-market plans, and enable our teams on message, do we really know what’s happening to our content once it’s released into the wild?

To paraphrase an old PSA, “It’s eight o’clock, do you know where your content is?”

Hold that thought as we consider artificial intelligence (AI), which is getting a lot of attention these days and touted by some as a kind of magic solution to all our problems. Can AI tell us what’s in the black box?

Andrew Moore, vice president of AI at Google Cloud, explains it this way: “AI is currently very, very stupid. It is really good at doing certain things which our brains can’t handle, but it’s not something we could press to do general-purpose reasoning involving things like analogies or creative thinking or jumping outside the box.” Fair enough—we still have to do the thinking. But it can also tell us some things our brains wouldn’t be able to figure out. That’s encouraging.

Technically, a lot of the “AI” we’re seeing in the MarTech stack is actually machine learning (ML), which provides us with the ability to train our data to learn. With each transaction and behavior, the ML models can identify, decipher, and apply patterns to not just predict what will happen, but to suggest what should occur. In the parlance of analytics, we’ve moved from predictive models to prescriptive ones. We’re now able to apply learning to our large data sets to determine best paths forward.

A very compelling use case of ML is to apply it to something that is currently unmeasurable. How can we, as humans, assess the consistency and cohesion of our messaging? Even in small batches, we’re guessing at the underlying themes and messages of content. Today, we may simply be reporting on what we want it to be, rather than what it truly is.

How AI can Measure the Unmeasurable

We’ve developed our strategies to deliver intended results, created fabulous content, distributed it to the right people at the right time, and analyzed performance metrics. Yet the question of all the content holding together is not one we’ve asked, because we couldn’t objectively answer it. Here’s a perfect place to apply machine learning. At a pace—and an understanding—that outstrips human capacity, algos can crawl thousands of individual content pieces to determine similarities and the underlying message themes. We’ve previously done this with tagging and other filters, but those are intent-based views of the data.

By defining the tags ahead of time, we can’t help but bias the results; but a data-driven ML view is agnostic—the data speaks for itself.

Using ML as a pathway to measure something we’ve previously only hoped for can transform our content operation. It can give us a much clearer picture of what we’re saying, where we stand with our applied content strategy, and how we can improve over time.

As customers learn more during sometimes long buy cycles, they can be assured of clarity and consistency of voice regardless of source. We promise. We deliver on that promise. The holy grail of frictionless customer experience through personalized, targeted content with a cohesive brand and message is within reach. Now that’s a worthy use of machine learning, and a smart use of data.

What’s important in using advanced tech is to find the best use case for it. AI and ML are not yet ready to tackle the true creativity in marketing, but can guide us by revealing the previously unknown. We can be intentional in our strategies, versus hoping for the best. Now that we’re smarter about using what AI offers, what’s next?

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