Will new technologies and automated-computers be able to produce effective measurement tools for PR campaigns?
By Carole Bersillon, Intern
Personality has mysteries that one cannot explain. I wrote in a former blog post about Twitter, Facebook and Big data that I had never really liked math and analytics at school. It turned out that starting my professional life, I like them a lot. Especially when dealing with analyzing PR campaigns’ impact, coverage or producing metrics for measurement tools and ROI. That blog post could be titled: Big data and I, the self-contradiction of a communication and art graduate. But this is another story…
Steve Lohr made the point in the article The Age of Big Data that we entered a new era of content production and analysis. The activities of quantifying and analyzing blog postings, comments on social media, speeches, press releases, or news articles have a bright future, especially with new computer-automated tools.
I must admit I am fascinated by the way a colossal amount of digital information can be analyzed by computers and how these computers turn quantitative information into qualitative information. This is part of the debate we had here at Fusion during one of our educational sessions dealing with the measurement of PR activity.
How can we turn long-term actions and the day-to-day maintenance of good relationships with reporters into quantitative and figured metrics to show our clients we work hard for them?
Beyond coverage reports, how can we show our clients the actions we recommend and take, even if they are “invisible”, produce profitable results on their business?
It has been showed in science that two teams of researchers working on the same topic could end up with different results, depending on the methods and analyzing process they chose. Is it the same for PR and communications, and more generally the analysis of big data? Does it all depend on the metrics you pre- determine and so the results? Is there any sort of universal communication/PR effectiveness measurement tool that could be agreed on and lead to objective and unquestionable results? Will supercomputers and new technologies be able to bring this tool via standardization and automated process?
The age of big data is here for sure and it opened a wide field of questions, research and new interests. Steve Lohr concluded his article with these words, explaining at the same time my self-contradiction about math and analysis:
“Despite the caveats, there seems to be no turning back. Data is in the driver’s seat. It’s there, it’s useful and it’s valuable, even hip. There is this idea that numbers and statistics are interesting and fun. It’s cool now.” I could not agree more…