At SHIFT, our approach is to apply equal parts art and science to build integrated programs that help brands connect with the people that matter most. But what does the ‘science’ part of communications entail? What does it look like in action?
First and foremost, it means to be data-driven in our planning and execution; to make informed decisions based on data and research. In this series, we examine how to become a more data-driven communications professional.
Developing a question
The first step in the data-driven PR process is to understand your challenge by breaking down your objective and gaining situational awareness. The way to do that is by creating a question you want to create a ‘solution’ for. What constitutes a great question to ask for data-driven PR? A great question has three characteristics:
Open vs. closed questions
Great questions are open questions, open-ended in nature. For example, suppose we run a coffee shop. A poor question would be: Do customers like or dislike espresso?
This is a very closed-ended question. We will learn relatively little from a question like this, certainly nothing we can pitch well with.
An open-ended question would be:What do customers like or dislike about espresso?
This question opens up many more opportunities for us to learn, to explore, to investigate. Since data-driven PR is about making decisions using data, we want to ask questions which maximize our use of data – and that means open-ended questions.
Questioning for specificity
Great questions are specific. The more specific we are in our questioning, the better the data and insight we are able to collect.
In the question above, we asked what customers like or dislike about espresso? Could we be more specific in our line of questioning? Certainly! After all, customers might like or dislike the cost of espresso, the flavor, the temperature, the roast – many different aspects of the espresso topic.
We might want to specify that we’re asking customers about how espresso tastes, in which case we should narrow our question around taste: What do customers like or dislike about the taste of espresso?
This is an improved, specific question.
Questioning for maximum benefit
Great questions serve more than one function. PR professionals typically ask questions and collect data for the purposes of pitching, of crafting a story that media outlets will cover. However, if we align our pitching process with the needs of the business, our work can provide multiple benefits.
For example, if our company is developing a new product line around cold espresso, our specific question above could serve both a public relations and a market research function.
Ask questions which create benefits for more than just the public relations and communications function, but help inform the who, what, when and where of our external communications and media pitches.
While we’ve discussed what makes a great question, let’s tackle the cardinal sin of many public relations and communications programs: incuriosity.
An incurious question is one in which we presume the answer, and we collect data to reinforce that answer. We select only the things we want to hear, or only the talking points we want to pitch. Incuriosity is one of the prime enemies of data-driven public relations.
If you’ve already got a conclusion in mind, a pitch angle you’ve settled on, or a pre-ordained answer for the question you’re developing, you are incurious. You are not doing data-driven public relations, because you have already made a decision without data.
For example, in our question above, suppose we worded it this way instead?
What do customers like about the taste of espresso?
This question presumes that customers like the taste of espresso. This is an incurious question, and is therefore invalid in data-driven communications.
The simplest way to determine whether you’ve built an incurious question is to ask yourself whether you’re already leaning towards an answer or not. If you are, stop and determine what a truly curious, open question would be.
Next: defining variables
Now that we’ve got an understanding of how to ask great questions, in the next post we’ll tackle defining the variables in our data collection process.
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