What is Data-Driven PR, Part 3: Defining Data

We’ve used the expression data-driven PR for quite some time now, but haven’t clearly defined it. What does data-driven PR mean? How do you know whether your public relations efforts are data-driven or not? To be data-driven is to make decisions with data first and foremost. In this series, we’ll examine how to become a more data-driven PR professional.

Defining Data

In the previous step, we developed an open-ended question that was specific, exploratory, and beneficial. In this step, we now must transform our question into the components of a data-driven investigation.

Data-driven investigations begin with two parts:

  • Variables
  • Data Sources

We’ll begin with our question from the previous part:

What do customers like or dislike about the taste of espresso?

Extracting Variables

What variables, what quantifiable measures could we make of this question? If we take apart the question, we see four components.

First, customers clearly identifies the people involved, as well as implies a quantity.

Like or dislike implies the way we will measure, on a scale of some kind.

Espresso identifies an object and taste identifies a dimension.

We have defined data for our question:

  • We will measure our customers.
  • We will ask them on a scale what they like or dislike.
  • We will ask them about the taste of espresso.

Identifying Data Sources

Once we know what variables are contained within our question, we begin the process of determining how to obtain the data. In this case, our question clearly defines the data source: our customers. Thus, we are limited in our sources to surveying and/or focus groups to obtain the information we want.

Broader Example

Let’s look at another example. Suppose our question is broader, such as:

What will be the hot toy among consumers this holiday season?

We deconstruct the question similarly to identify variables:

  • We will measure consumers.
  • We will measure toys by name.
  • We have a defined timeframe: this holiday season. Thus, we should limit our data search to relatively recent times.

With the variables defined, we now ask where to obtain the data. Let’s begin with toys by name. Where might we find a list of popular toys? The largest retailer in the world also shares its data liberally, so let’s go check Amazon’s toy rankings:

amazonmostwished.png

We see a variety of products most wished for. Let’s pick one of the products and use the media monitoring tool of your choice to determine conversation about it. Here’s a look at overall coverage around the Hasbro Pie Face Game:

piefacepopularity.png

Not bad. We do see a trend of increasing conversational volume about the toy. What about the 4M Crystal Growing Experiment?

crystalpopularity.png

This product was under the radar until very recently. When we examine the conversations to find out what people are saying, after removing promotional links, we find no conversation about this gift.

If, however, we examine the conversation around the Pie Face Game, we see substantial evidence consumers actually enjoy it:

piefaceconvos.png

Thus, we have a potential winner for our question, based on data. Our next steps in this very surface investigation of consumer trends would be to repeat this process for all the trending toys, to determine which is trending most sharply and is driven by actual consumers.

Next: Defining Variables

The examples above delve only cursorily into the data. To craft a truly data-driven pitch, we must use a much more rigorous process. In the next post in this series, we’ll expand upon our data and formulate a true hypothesis as part of the data-driven PR plan.

Christopher S. Penn
Vice President, Marketing Technology

Work at SHIFT

Posted on November 21, 2016 in Analytics, Data, Data-Driven PR, Metrics, Public Relations

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