Public relations, marketing, and communications aren’t known for their futurist perspectives. More often than not, we’re reacting to the latest and greatest, from crisis communications (when the news is bad) to rapid response/newsjacking (when the news is good).
Until now, our ability to keep a finger on the pulse of the news has been good enough. We’ve reacted fast enough to keep crises mostly contained; for every major PR crisis in the news, there are dozens, if not hundreds that are intercepted by talented PR professionals before the spark turns into a conflagration. We’ve jumped on the bandwagon enough to insert our perspectives, our stories, and our brands into trending news stories.
However, relying on just what’s happening now isn’t enough any longer. In the new era of intelligent machines, we marketers and communicators must learn to predict what will be.
A brief recap of predictive analytics
Predictive analytics is the statistical discipline of predicting what’s likely to happen based on existing data. It’s nothing new; we’ve attempted to predict the future since we first became a rational species. The Mayan civilization was known for its uncanny predictive skills, especially in astronomy.
Even as children, we learn basic predictive skills in math class, drawing lines to connect points on a plot.
What’s changed is our ability to predict with extremely large, complex datasets – and how accessible such prediction technology is. With advances in software and computing power, we now make predictions with many more data series, increasing the accuracy and richness of our powers of prediction.
For example, in years past, if we wanted to predict the health of our business, we might assemble revenue, sales numbers, and perhaps a few marketing statistics. Today, we ingest thousands of news articles, millions of social media data points, web analytics, customer service data, CRM data – the list of data sources is nearly endless. Nearly every possible way for us to communicate with our audiences has a digital twin – a digital way to measure the activity, from location-based check-ins to photos posted online to voice conversations we have with company representatives.
All this data is meaningless if we don’t have a way to process it and transform it into predictive models. Today, PR professionals have access to the world’s most advanced predictive, artificially-intelligent algorithms and software thanks to the open source movement and major technology vendors such as Microsoft, Amazon, Facebook, Google, and IBM. With these technologies, turning data into valuable insights is within everyone’s reach.
How do predictive analytics apply to PR?
Let’s look at just a few use-cases of predictive analytics. Suppose we want to understand how an audience is likely to react to a negative corporate announcement. Since very little is completely new in the world, we should be able to find a dataset of a similar circumstance – perhaps even in our competitive set – and build a model of how the audience reacted to that announcement.
For example, if we’re a telecom company raising rates, finding reactions to a competitor’s rate increase is straightforward. Once we have the data, we can model it over time and use machine learning to extract the most common complaints/talking points from the past and develop an action plan for the future.
In another example, suppose a company wants to understand when demand for its product or service will be highest. Using publications, search data, and social media conversations, we construct a model of demand for the product or service looking forward, and give the company a proactive calendar to plan against.
Next: the basics of prediction in PR
In the next post in this series, we’ll examine the fundamentals of predictive analytics for public relations: data. Stay tuned!
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