Introducing the Predictive Interviewing Model
The business models for traditional media have been breaking down for more than two decades. Many respected publications such as the Chicago Tribune, the Seattle Post-Intelligencer and the Rocky Mountain News have been met with bankruptcy in the wake of a vast shift of audiences to digital platforms where virtual and individualized news ecosystems can be easily created and reformulated. This paper examines how the tectonic financial shifts brought on by digital news ecosystems have in turn, dramatically affected the fundamental behaviors of reporters and reporting. Specifically, technology is replacing human-driven reporting, and as a result the ability of news outlets to develop thoughtful, educated and accurate stories is in decline. Instead, formulaic news packages scripted down to the length of a sound bite have become standard. The effect is the de-skilling of journalists and the slow dissolution of the craft of media relations. The circumstances cannot be reversed; the temptation offered by ever-cheaper technology-based reporting and distribution models is too potent for publishers and general managers to resist. In fact, given Moore’s law, the trend toward robot-based reporting is likely to accelerate. For the media relations discipline to maintain relevance in this era, adaptation is required. Through this paper, a key adaptive tool, the Predictive Interviewing Model (PIM), is introduced. As the rules governing relationships with the media disappear, the PIM offers a mechanism for reducing uncertainty in the most essential aspect of media relations: the media interview.
Traditionally, preparing for media interviews meant studying the beats and narrative tendencies of a reporter. Preparation may have also involved interacting with a reporter prior to a story to ascertain direction and intention, and on behalf of a client, a question-and-answer briefing paper may have been developed to assist a source in responding to questions. In the post-media relations era outlined in this paper, those dimensions of the craft are giving way. Simply put, robot reporters aren’t interested in having a coffee to talk about a story under development. Today, inexperienced reporters with virtually no relationship to a source or PR practitioner, nor back- ground information about the subject of an interview, are in the field to create and disseminate content quickly.
The PIM responds to these conditions in two ways. First, it provides a remarkably prescient tool to anticipate the fixed number of question-types a reporter can ask in an interview. Second, the PIM shows the sequence with which reporters generally ask the question-types. To create the PIM, 505 media interviews broadcast on National Public Radio from 2012 to 2014 were compiled as a data set. Each of the 2,236 questions in the data set was coded using a unique taxonomy, and an SPSS-based statistical analysis was performed to ascertain the ask sequence.
With question-type and sequence probability revealed, a model was diagrammed to help PR practitioners quickly assess media interview opportunities and concentrate on building effective messaging to counter uninformed, misinformed or haphazard journalistic tendencies.
As news outlets migrate inexorably toward staff-free models in favor of algorithm-based solutions, the PIM’s relevance becomes increasingly important to practitioners coping with shifting newsgathering and reporting practices.