What is Data-Driven PR, Part 9: Pitching With Data

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.

How to pitch a data-driven narrative with… data

Just because we’ve used data and the scientific method to create a compelling story doesn’t mean the world will beat a path to our door. The final part of data-driven PR is to use our data, analysis and insights to identify who would be most receptive to our pitching.

Suppose we take our pitch about SEO, about how trust and linking have a relationship. Who should we pitch this story to? Just anyone?

Unsurprisingly, the answer is: let’s examine the data. We’d look at our media lists and examine not just what the reporter, journalist, or publication is about, but what their audience is about as well.

Let’s pretend for a moment that instead of the pitch author, I am a prospective reporter or media outlet. Should this pitch about SEO land on my desk?

Here’s an example of what our Twitter followers care about, visualized as a word cloud using Sysomos MAP, a popular public relations media monitoring tool:

Note that SEO is in there. It’s a topic that my audience – and the journalist’s audience – cares about.

By focusing on the data about what our audiences care about, we increase the likelihood our pitch will be relevant and well-received.

Kicking data-driven PR up a notch

Suppose we had a journalist or reporter we wanted to place a pitch with. Beyond just their social media bio, could we discern their interests? Using advanced analysis with machine learning, we might analyze a reporter’s most recent articles over the past year to see what other topics they write about.

For example, let’s take Marketing Land and Search Engine Land editor Ginny Marvin, and 43 of her most recent columns. Would she be interested in our pitch about SEO? When we do an entity extraction using natural language processing tools, we find:

While Google is a highly salient (prominent) topic in her writing, it’s not Google’s SEO she writes most about. We see she writes much more about Google in the context of AdWords and advertising. If we have nothing relevant about her areas of interest in our pitch, we are unlikely to appeal to her.

This is a glimpse at the future of public relations and communications; over time, machine learning tools will help us be better data-driven PR professionals. Instead of shotgun pitching to anyone who will listen, our tools will help us identify who truly wants to hear from us and what a relevant narrative is. Our tools will help us scale our efforts and improve the way we communicate with the audiences we care about most.

Conclusion

We have reached the end of our series on data-driven PR. From the very beginning, understanding what questions to ask, to the end, in which our machines may help us scale our efforts, data is at the heart of modern PR and communications.