Industry executives and experts share their predictions for 2018.  Read them in this 10th annual VMblog.com series exclusive.

Contributed by Doug Randall, CEO of Protagonist

Corporations Will Define a New Role for Themselves: Social Leaders - And They'll Do it With Data

In 2017, business got political. It happened primarily in two very different ways--social activism and public scandal. Prominent companies and industries made headlines for diversity and harassment, and also for taking powerful stands for or against issues the brands cared about. 2017 was a year of brands entering largely uncharted and usually complicated conversations with the larger public. 2018 will be a year of turning to data to make more informed decisions around these loaded topics.

For years, companies have used big data to streamline operations, monitor resources and--where possible--increase revenue. Modern analytics have permanently changed the way that businesses function. But now, the landscape has gotten more complicated and there's more at stake for companies that want to protect their reputation, add more meaning to their brand and simply act ethically. We're going to see marketing teams, HR and leadership start operating like data scientists, using hard facts to make decisions about which stance to take, how to frame those arguments, and identify potential risk areas. Businesses will lean more on science to understand beliefs.

2017 was dominated by scandals like the Google employee's anti-diversity memo, which forced the company to navigate the line between protecting its safe work environment and respecting free speech, and appalling harassment problems in both Hollywood and Silicon Valley. The pressure for companies to understand the viewpoints of their employees is unignorable. If there are undercurrents of discontent or conflicting viewpoints in the workforce, those conflicts are likely to surface one way or another. By the end of 2018, it could be commonplace for employers to monitor employee social networks and internal communications for changing sentiments, run analytics, or to set up alert systems for potential problematic behavior. The art will lie in finding ways to respect privacy while still protecting the interest of the organization and its employees.

On the other end of the spectrum, those same data practices will come into play as more companies contemplate following in the footsteps of activist leaders like Patagonia (an outspoken environmental advocate) or the slew of tech companies that recently vocally opposed the FCC's overturn of net neutrality. In both cases, the causes were very much in line with the companies' values and the values of their customers, which makes these types of announcements far more successful. Campaigns that make more of a leap (like Pepsi's ill-fated protest ad) don't go as smoothly. Businesses that are new to the political space, have a diverse customer base, or that don't have clear insight into the viewpoints of their audience will start seeking clear, data-backed insights about what their customers believe in and which issues are most fraught. Here too, the nature of marketing will shift from speculation based on snippets to overseeing algorithms that process millions of inputs for a balanced perspective.

Almost across the board, modern work has grown more technical in recent years. Everyone from farm workers to salespeople have needed to learn new software and machine skills to stay relevant, and marketing has changed significantly. Many marketers have already become experts in automation tools and SEO, but 2017's growing norm of corporations getting involved in public issues has created a new necessity: a science-based understanding of changing perceptions and deeply rooted opinions. Because the stakes have never been higher.

##

About the Author

Doug Randall has more than 20 years working as a strategy consultant, entrepreneur, and technology executive. He is founder and CEO of Protagonist, the Narrative Analytics company. Randall and his team created a program that combines natural language processing and machine learning with human expertise to solve complex business problems.