Are the lines between communications and marketing blurred or disappearing?

Communicators are working more closely with marketing than ever, even sharing budgets. CMOs are looking to hire data scientists to help them turn data into insights. Like it or not, it’s a data-driven world out there – are communicators prepared to thrive in this environment?


PRWeek recently hosted the CMOs of Dropbox, Pinterest, Hilton, and Microsoft on a panel at the Cannes Lions International Festival of Creativity to talk about “taking data and turning it into insights.”

The panelists noted, “the lines have blurred between marketing and communications, marketing and information technology, and who exactly is doing the marketing.”  At Microsoft, communications and marketing report to the same boss and work off a single budget. And at Hilton, none of the brands have their own PR budgets.

As these lines blur or disappear, are communicators really equipped to handle a landscape that requires so much data not only be ingested, but also relayed smartly back to executives?

First and foremost – embrace the power of data. Being data-informed is going to get you a seat at the table – or let you keep your seat. While you don’t need to become a data scientist, you do need to ask the right questions and adjust your analytical approach to get the answers.

Generic counts of keyword mentions and likes or a simple list of “nice” articles is not insightful.  And counting articles that mention your brand, where a significant number of those mentions are stock quotes or auto-published press releases on spam sites, is simply misleading. Any communicator who brings such numbers forward risks losing credibility.

But imagine what you could do with the answers to these questions:

  • Is our message pulling through?
  • Are we over-allocating resources to push an already successful message?
  • How successful was the campaign/event? Who did we reach?
  • How is my coverage impacting my reputation?
  • Which influencers are driving the industry conversation?
  • Which authors and outlets aren’t writing about us enough?
  • How effective are our spokespeople at driving positive conversations?
  • How are similar competitive products/services perceived differently?
  • Who is writing about competitors but not me?
  • Where is there white space for message expansion (where no one is talking – but should)?

Confidence in the answers is critical – you must trust the data is being analyzed properly. How are you gathering and analyzing your traditional and social media data today? Will it stand up to scrutiny?

While great strides are being made in media measurement, there are still shortcomings in a technology-only analytics approach:

  1. Analyzing only relevant information. Too many erroneous or irrelevant hits slipping through will dilute relevant content so much that metrics become misleading or even random. Important issues can be hidden within large datasets that are time-consuming to review, so you won’t know you have a problem until it is too late.
  2. Understanding true sentiment. Automated sentiment analysis is widely considered to be 60% to 80% correct. But that is on overall sentiment, not against the individual topics, subtopics, and competitors within the content. And forget drawing out sarcasm.
  3. Getting to the stories behind your stories. Most automated solutions attempt to measure topics with keywords. But how do you measure that your product is innovative or that your company is socially responsible if those words don’t appear?

Given the current limitations of technology-only solutions, many communicators are choosing to partner with a media intelligence provider that can interpret the data to deliver the answers held within. Some of these innovative providers use a hybrid approach that combines smart technology with expert human analysis. The added layer of accuracy and insights that human analysis brings directly address these shortcomings. And the insights provided become invaluable as you navigate sentiment, reputation, influence, and competitive analysis in a more data-informed environment. You don’t have to be a data scientist to get the big answers out of your big data.

Written by Eric Koefoot for PR Week.