The success of PR campaigns hinge on the degree to which their target audiences are fully understood. Demographic data is useful, but it really only scratches the surface of the human-level detail that must be known and “baked into” a campaign to ensure that the right people find it sufficiently compelling.
Before PR professionals put creative pen to paper, we want to know our public more personally. To understand which features, facts, and characteristics define and differentiate them. This includes getting under the skin of what they care about, how they think, their favored media, browsing habits, hobbies, and past-times. That’s as well as the personal challenges they face, and who they lean on.
Technology recently supercharged our ability to gather and analyze relevant data to this end, helping us to build out three-dimensional audience personas by accurately identifying preferences, tendencies and characteristics. Now our goal is to understand what audiences like, and also what people like them like.
Built into this process of analysis are assumptions about correlative tastes, as well as the assumption that past preferences forecast future behaviors. These suppositions are not entirely without merit, but with massive volumes of data available to us, as well as the ability to analyze it, we can find ourselves relying on tools that are built to find the strongest commonalities across massive samples. This process provides us with trends, but they are usually the most obvious of trends.
We learn that urban millennials love music, food, and trying new restaurants. They are focused on their careers. Suburban dads enjoy watching major sports. Stay-at-home moms care about the environment and education. We use these findings to develop stale, static stereotypes. Without carefully digging deeper into the data, we risk reinforcing one-dimensional “types” and, in turn, create and deploy targeted materials that reinforce these fairly crude perceptions.
It’s important to acknowledge that by way of data-driven, targeted communications we are submitting additional evidence of these stereotypes into the ether. We present messages suggesting that members of this audience like certain things, act certain ways, use specific media. Then, in pulling data again for another client, lo-and-behold we find more evidence that these stereotypes rarely deviate. Given the force of the media and the potential role it plays in shaping our world, it is quite possible that data-driven “types” not only reflect our world, but also in some ways determine it. Like a closed feedback loop.
It is the responsibility of media organizations and those involved in public relations campaigns to avoid the stifling effect of narrowly casting clients’ audiences in ways that reduce them to a simplified data point. Providing nuanced, actionable information that transcends broad-brush stereotypes both better prepares the client to understand who they are speaking to and positions them as more in-tune with their audiences. After all, audiences are savvy and sensitive to whether brands really “get them,” a trend that isn’t going anywhere as consumers increasingly demand more personalized content from brands and businesses.
So how do we go about creating more understanding and less feedback? There is room for discussion, certainly. That said, some basic principles and practices can begin to address this challenge:
Triangulation: Relying on multiple data sources with varying methodologies provides different views of the same audience. Understanding the inherent implications and limitations of each method and data source is critical to accurately interpreting and incorporating multiple data sources.
Mixed-Methods: Stronger yet is analyses that use quantitative data as directional to find qualitative data that provides description. Rigorous insights and strategy are developed by understanding the trends of the groups and nuances of group members. It is in the individual responses that we can learn how individuals talk about an issue, how it relates to other issues in their lives, how it nests in their reality.
Dynamic Data: Less glamorous than the one-off data pulls, there is substantial value in using analyses that examine audience data over time. Through this lens a conscientious investigator can identify shifts in priorities, conversations and realities. Creating a feedback loop, where data is consistently refreshed and changes over time are noted ensures that audiences are allowed to grow and change. This ensures clients stay in touch with who customers are, not just who they were. It also provides the opportunity to correct and refine assumptions, correcting any errors or misconceptions.
It is no coincidence that these recommendations align with the Barcelona Principles. Value is developed from the thoughtful consideration of data, not just the data itself. At the core, this is what separates actionable data from the rest. All of these research practices will ensure data remain dynamic and actionable; however, there is one critical piece needed to harvest the strongest results: A keen (human) eye. Without a skilled eye digging deeper to find the truly valuable, unique insights, identifying meaningful connections, insights into the data are likely to remain static.
AI is an incredible resource for organizing and stratifying data, but when it comes to defining human audiences, we have a responsibility to draw on multiple sources and our superior semantic intelligence, rather than relying upon data readouts. In short, we take advantage of all of the audience insights options available to us — but without sacrificing the human element that makes our insights, well, human.