American economist and political scientist, Herbert Simon, was ahead of his time. In the early 1970s, he realized that information would no longer be a scarce resource in an era of media saturation. Instead, people’s attention would be in demand.

It doesn’t matter whether you’re a startup, enterprise, entertainer, or politician–anyone who is seeking to monetize their profile or brand is vying for elbow room in the attention economy. At this point, it’s almost an impossible task. Our attention is fragmented, and our patience is thin.

Figuring out how to monetize attention is both a challenge and an opportunity. Even traditional industry players–such as advertisers, media outlets, and the entertainment industry–are finding themselves in uncharted waters amongst a sea of niche, digital competitors. Ratings are falling, and meaningful engagement is challenging to measure.

The hard truth is, if you don’t capture the attention of your audience and consumers, your brand will not survive. Attention is power. Luckily, capturing it is possible.

Attention can be bought, and hype can be manufactured. If you’re a business with brand equity, it’s easier to grab attention. But gaining attention is only one part of the equation; once you have it, keeping it and making meaningful engagement is far more complicated. It needs to be earned.

The deeper you can engage, the stronger the emotional connection you’ll be able to foster with your audience, and the higher the brand loyalty you will build. Successful brands today are part of a cultural narrative that defines us.

As a data analyst and data translator, much of my work has been in understanding audience engagement through big data. We analyze petabytes of open source, topical content (i.e., language-based data) in the online universe. We read, analyze, contextualize, and categorize every piece of content online that interests you.

Polls and metrics like impressions, page views, followers, and ‘likes’ have limitations because they are blunt measures of engagement. They describe a slice of what is happening, but they don’t tell the story of ‘why.’

With machine learning and open-source datasets, we can analyze the entire market in its natural environment (i.e., not being asked a question), what content it’s reading and watching, and analyze–through contextual understanding based on natural language processing–how it feels and the intensity of the emotion. As humans, we make decisions based on emotion, and the more intensely we feel about something, the higher the likelihood we will change our behavior.

If I love iPhones, then I will sleep outside the store for days to be one of the first to buy it–I’m engaged. If I hate a brand, then I will tell everyone I know to stay away–I’m engaged. If I think a brand is ‘okay,’ then it means I ‘don’t care’–I’m not engaged.

Our ability to accurately measure emotions without human bias to predict behavior can unlock so many insights into audience engagement strategies.

In January 2018, we worked to understand which narratives incited the most profound engagement about climate change. There were hundreds of topics in this space, from renewable energy, to sustainability, to electric vehicles. Content creators, brand strategists, product developers, and policymakers all want to know: what’s the most compelling story?

We uncovered several threads of engagement. One of them involved school children, as we identified their deep emotional connection to environmental activism.

Two events then took place. In March 2019, high school students across Australia orchestrated a climate change protest. In September 2019, the global climate change strike took place, which was led by the young and inspiring Greta Thunberg.

We measured the emotion, uncovered what was deeply engaging, and predicted behavior. Imagine the world of possibilities that is now open for any industry, brand, and business. If your audience is human, you need to understand their emotions. There are better ways to drink soup. Put down the fork.