AI is the Future of Communications Measurement

An image of a girl and a robot, illustrating the concept of AI.

Hear that crunching sound? It’s me, eating my words.

In 2018, in this publication, I declared myself an AI skeptic through and through. At the 2019 Summit on the Future of Communications Measurement, I again called BS. I said that AI would never be useful to PR folks until it could detect a crisis on the way, tell what kind of a crisis it was, determine the optimum response, and predict how soon things would get back to normal.

A year later Gaugarin Oliver developed an AI crisis prediction and management tool that could do all those things. And at this year’s Summit on the Future of Communications Measurement, artificial intelligence was the answer to almost every question.

Thus I now freely acknowledge that AI and the predictive analytics it produces are making the future of measurement better and a lot more interesting.

Here are some of the AI highlights from this year’s Summit:

AI, crises, and optimum messages

Angela Dwyer, director of insights at FullIntel, described how they regularly use their years of human-coded data to train their AI machine. They can then identify trending topics, and inform clients on which might turn into a crisis and how best to respond. They also use it to identify:

  • Optimum messages,
  • Issues that are most likely to help a client’s goals, and
  • Opportunities for amplification of the impact of desirable coverage.

But at least my predictions were right in one respect: Dwyer emphasized the importance of combining human intelligence with machine learning to ensure accurate results.

AI, reputation, and revenue

Rob Key, founder and CEO of Converseon, explained the difference between artificial intelligence and decision intelligence. Decision intelligence combines AI and very sophisticated modeling to predict which specific aspect of reputation, e.g., environment, is most likely to increase additional revenue.

Converseon’s models take over where traditional reputation measurement, AI, and natural language processing leave off. By analyzing social conversations, rather than just looking for words in a bunch of unstructured data, Converseon has developed multiple models for multiple industries. Instead of measuring reputation as a whole, the models can pick out the individual elements of a reputation (e.g., environmental performance or governance) that will increase revenue.

Converseon can also look at the data that is not relevant to revenue and identify blind spots or other issues where your brand might be weak or need fixing. Or might become the source of bad news that could bubble up to bite you.

What sets Converseon’s system apart is that ties different elements of reputation directly back to revenue, and then tells you which ones are more likely to increase revenue. The data comes from publicly available financial data and sales data making it an even more intriguing product for competitive analysis. And it can get even more granular, going beyond the simple concept of “diversity” to reveal whether the discussion is about  gender equality vs. employee wellness. Best of all, it shows the potential revenue increase from promoting one concept over the other. 

Rob predicts that this ability to analyze and affect reputation will not just earn Comms a seat at the table, but put it in charge of the table.

Predictive AI

Mark Stouse, chairman and CEO of ProofAI, jumped on the AI bandwagon early on. ProofAI uses it to show how different forms of communications are impacting or will impact revenue and show the most return. What’s particularly savvy about his system is that it helps set expectations around when to expect that return. ∞

Photo up top by Andy Kelly on Unsplash.

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