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Two Crucial Rules to Improve Data Visualization and Reporting

The role of the marketing analyst is becoming critical to the success of a marketing organization. Often times, analysts are tasked with going deep into data that no one else is familiar with to uncover insights that can improve marketing strategy. After hours of collecting, cleaning, and analyzing, it’s easy for analysts to overlook the most important part of their role: communicating what the data means to decision makers.


At this critical point, an analyst may choose to visualize the data in the default PowerPoint or Excel templates and call it a day. While this will technically complete their reporting task, it often causes a communication failure. If decision makers aren’t able to easily understand the insights, their implications, and how to act accordingly, then it’s unlikely that the analyst’s visualization will be as valuable as it can be.


Fortunately, analysts can improve their visualizations by remembering two simple rules: Less is more and Don’t make me think.


How a Typical Visualization Falls Short


Here is an example of a typical chart being used to analyze the performance of geographically tailored blog posts for a company operating across the Eastern Seaboard.


While this isn’t the worst graph, it’s far from the best. There are many data visualization crimes being committed here but the two big ones are that:


1) There are too many unnecessary and distracting chart elements.


2) There are no insights called out.


The analyst is providing the information but is making the audience decipher what they’re being shown and then hunt for an interpretation.


Improving visualizations through the twin rules of “Less is more” and “Don’t make me think”


1) Less is More


To make the chart less distracting, I removed the chart lines, border, legend, and entire Y axis from the original chart. The chart lines and border added no information, making them unnecessary. I decided to use the title as the legend, increasing its functionality and decreasing the number of objects on the chart. This will also be the first thing that someone viewing the visualization reads so they’ll know what they’re seeing before they scan the data. The data labels include percent signs on every bar, so including the Y axis would have been a repetition of information. Therefore, I removed it. Eliminating repetitive and unnecessary visualization elements will help the communication flow.




Scroll back up to the original graph, then back down here and pay attention to how quickly you are able to interpret each one. It’s undeniable that this graph, with the same data, is more user friendly.


2) Don’t make me think


To solve the second crime, I’ve highlighted and called out what I think is the most interesting part of the visualization. The data labels for New York and Washington are not the focal point of my communication, so I’ve excluded them. All the data is still present for context and discussion, but I’ve slightly faded the color to focus the audience’s attention on the most important part of the visualization. I’ve also changed the order of the cities to prioritize the highest percentage of leads in descending order, anticipating what my audience wants to see. Finally, I’ve included a call to action, engaging the audience to consider making a strategic change in response to the data.






Targeted content for Philadelphia and Boston is over performing, making up 20% of leads with only 5% of posts and 6% of page views.

Consider reallocating resources from Baltimore which is underperforming in both views and leads.

Some analysts may respond “But all the data is important! I can’t bring it down to one message”. If this is the case, I’d consider creating two visualizations, so possible story points won’t be excluded or ignored. It will be more work, but it will show that the analyst has been thorough.


Why it matters


An analyst’s most important job is translating data to actionable insights for decision makers. Remembering the principles of “Less is more” and “Don’t make me think” will make this translation quicker. With fewer visual elements present, the audience will be able to decipher what’s occurring faster. Words that spell out what is interesting about the visualization leave no room for interpretation about what the analyst is implying. Taking the time to implement these two principles will make it more likely that decision makers will understand the insight being communicated and, more importantly, act on it.


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