Before AI’s recent introduction into the media pitching workflow, knowing if a reporter would write a story about your brand or product was a complete guessing game. Sure, you would use your professional experience and instinct, and conduct labor-intensive research, reading up on which reporters to pitch, but with no real-time data to guide, support or defend a pitch and therefore no data to back-stop your counsel when setting expectations for your client.
And let’s face it, most clients have outsized expectations because they all think they are more interesting than they really are. PR is the last man standing of the martech world when it comes to adopting technologies that enable us to be more performative (read: land more high impact placements) using real-time data and insights.
For decades now, our industry has been subjugated to using analog earned media “intelligence” tools that are parading as modern platform—skeleton databases in obsolete, legacy platforms that base media targets on rudimentary topical information that’s neither performative nor predictive. The insights developed from these platforms are basic, and oftentimes inaccurate, leading to failed pitches and disappointed clients. Put another way, those old “Bacon’s” books are now just online and just as useful today as they were decades ago.
There’s still hope. Modern brands and communications professionals are starting to adopt data-driven predictive software like PRophet to test the “mediability” of a pitch idea. AI-powered guidance equips PR pros with thorough proof points to show their clients how the media outreach might perform amongst key journalists, and which outlets to avoid to limit risk/exposure of bad sentiment.
PR without proof points
Throughout their career, every PR professional has had a client or executive come to them with a “big idea” they want to communicate to the world. Naturally, the client anticipates massive coverage that dramatically increases brand exposure and, in turn, boosts their company’s business objectives (sales, shareholder value, employee engagement, etc.) There is just one catch: the “big idea” is rarely as interesting as they think.
This tension is not new. The PR industry has undergone a multitude of changes over the past 30 years, but one aspect remains constant: one of the most critical core elements of PR is and always will be to secure positive press coverage.
In the past, consistent positive press coverage was built through relationships between PR people and journalists. Thus, a great deal of a PR professional’s time and effort is focused on building those connections. Nowadays, however, these links have eroded or commodified for many reasons, including:
- A more competitive news environment
- Contracting newsrooms with fewer reporters covering multiple beats
- The increasingly digital nature of interactions between a PR person and a reporter
- Fewer in-house and agency resources to deploy in the resignation economy
Moreover, PR is still primarily based on gut instinct and, for many, a “spray and pray” approach to media relations. It has never really been data-driven or performative like other disciplines.
A recent study by PwC found that 86 percent of executives see AI becoming a “mainstream technology” at their company. I do not know what this number looks like for PR executives, but it’s safe to assume it would be dramatically lower. We have now reached a perfect storm for technology—specifically AI—to make PR more performative. Press releases are dying a slow death, but pitching isn’t dead. Rather, how we do it must change. The future of marketing and, by extension, PR lies at the intersection of strategy, creativity, and intelligent data to help us better target and deliver our message, so it resonates with our target audiences. So, in the same way meteorologists use supercomputers to forecast the weather and movie studios leverage AI technologies to predict the commercial potential for scripts, why can’t a similar approach be adopted for media outreach?
It can. And it has. Revolutionary software platforms, like the one I founded and built in 2020, deploy AI, machine learning and natural language processing techniques to test pitches against a dataset of global, verified journalists from high-authority outlets, all in a virtual, safe and secure environment. Tools like PRophet will surface new and unexpected media opportunities by matching your pitch against millions of actual stories in real-time—predicting both media interest and sentiment.
For example, say a client has a particular reporter that they would like to cover their idea or story. Oftentimes, due to a lack of data-driven resources, a public relations professional might have to guess the odds that the reporter would be interested in the client’s news, which could lead to false promises that damage client relationships. However, public relations professionals can instead use AI-driven software to predict the percentage chances that a reporter would cover their story—all in a matter of seconds. It also allows PR professionals to tweak their pitches to better cater to particular journalists, ensuring that reporters receive an angle that’s particularly relevant to them.
Data as a relationship-building resource
Public relations is a relationship business. In order to be successful, one must maintain stellar relationships with both clients, whose expectations are typically high, and journalists, who receive hundreds of irrelevant pitches a week. These two relationships are mutually dependent on each other, as successful media relations cannot exist without positive journalist relationships, and a pitch to a journalist is incomplete without news or ideas from a client. Using data to supplement a strategic, human approach, PR professionals can ensure optimal relationships on all fronts. It’s where art and science meet in the middle.