Thinking for the Audience

Learn who your audience is, the critical factors in decision-making, and analyze a sample dataset.

The audience is the person or group of people listening to our data story. There are two possible types of audiences:

  • Someone we already know, such as our boss or colleagues

  • An unknown audience, such as people attending a conference or an event in general

In both cases, study the characteristics of the audience, for example, by searching for their needs. This includes knowing what they want, what they are looking for, and their objectives. Before defining everything in our story, we need to know our audience.

Identify the audience

Consider the following five key factors:

  • Geographic: This is the target audience’s country, region, city, or neighborhood.

  • Demographic: This is the target audience’s age, gender, income, occupation, education level, and family size.

  • Psychographic: This is the target audience’s personality type, values, interests, and lifestyle.

  • Behavioral factors: This is everything related to a person’s behavior, including, but not limited to, purchase history, web-browsing behavior, and media consumption habits.

  • Knowledge about the topic: This determines whether or not the audience is an expert on the topic we are going to present.

Know the audience’s needs

There are different strategies to know our audience’s needs, including, but not limited to:

  • Survey the audience through a questionnaire.

  • Make a discovery call, which is a conversation where we ask our audience questions about their needs and wants.

  • Use social media: by looking at the comments and posts they write, we can understand their needs.

Call the audience to action

The main objective of our data story is to call the audience to action. When crafting a data story, we have to think about what we want our audience to do after hearing it. Do we want them to change their behavior? Make a decision? Donate to a cause? Whatever it is, we should ensure our story is focused and has a clear call to action.

Use the data story to invite the audience to act.

Key factors in decision-making

There are two main factors in decision-making: logic and emotions.

Logic and emotions
Logic and emotions


Logic helps us identify the flow of events and come to a reasoned conclusion. It also allows us to make the correct decisions. Before making decisions, we should take our time to think about all the possible choices carefully.

If we rush into a decision without thinking it through properly, we may make a choice that we later regret. Using logic and reason enables us to make the best possible decision. Of course, there’s no guarantee using logic will always lead to the best decision.


Usually, we think that we make decisions based only on logic and reason, but in reality, emotions also play an important role in the decision-making process. When we hear a story, our brain automatically starts to assign meaning to it and creates a narrative. This aspect explains why stories are such powerful tools for persuasion. If the story aligns with our beliefs, we are more likely to accept it.

If the story goes against our beliefs, we are more likely to be skeptical and ask for more evidence. This way, our emotions also play a role in decision-making. We tend to make choices that will give us pleasure and avoid choices that will cause us pain.

When we build a data story, balance both logic and emotions to get more chances to engage the audience.


Engaging the audience could also mean manipulating them to lead them to make the decisions we want. We need to avoid this aspect, as well as the manipulation of the data for our purposes. Using emotional appeals to influence the audience’s emotions to sway their decision-making is highly discouraged.

Always be honest!


Let’s assume that we work at a company that sells Christmas trees. Our boss asks us to analyze whether our company must increase the production of fake trees. We find a dataset describing the number of Christmas trees sold in the US, and we decide to use it for our analysis. The dataset is released by Makeover Monday and is available on First, we draw the following chart:

A bad example of a comparison between real and fake trees sold in the US
A bad example of a comparison between real and fake trees sold in the US

The main problem of this graph is that it leaves the reader all the burden of making the decision. The chart is a visual representation of the original dataset, and it does not add any value to the original dataset; it only shows the trendlines.

We build a second chart.

A good example of a comparison between real and fake trees sold in the US
A good example of a comparison between real and fake trees sold in the US

The graph calculates the percentage increase in sales of both real and fake trees. The black line implies that investing in fake trees can be a winning strategy, because the number of sales of this category of trees has increased over the years.

Here is how to calculate the percentage increase: