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Tapping into the pulse of marketing with data visualization


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Chances are you’ve heard the phrase “a picture is worth a thousand words.” What you may not know is that depending on the context, this can be somewhat of a misleading statement.

Hear us out. The human brain is hardwired to ingest images 60,000 times faster than text, accounting for 90% of the information we process every day being visual. These numbers make a convincing case as to why a picture deserves a little more credit than just a thousand words.

But we didn’t dig up a century-old proverb to nitpick on its statistical shortcomings. Instead, we wanted to highlight how the sentiment behind the phrase has never been more apropos for marketers who are left to stay afloat in an expanding sea of raw data every passing day. 

Refining raw data with visualization 

Clive Humby was onto something when he proposed data as the new oil to his fellow C-suite executives at the 2006 Association of National Advertisers (ANA) Master of Marketing summit. A decade and a half later, his prediction came to fruition as data completely superseded introspection and guesswork as a bottom line for marketing success.

What makes Humby’s foresight truly impressive, however, isn’t the eventual rise of data as king in advertising. It’s more so the fact that data, just like crude, is practically useless in its rawest form. 

To elaborate, oil goes through a refining process before hitting the pumps. The same goes for raw data. It needs contextualizing and must be broken down first into something more structured and ultimately actionable. 

This is where visualization comes into the picture. Once the datasets have been cleaned and standardized, visualization steps in as the last critical step of the refining process to remodel them into intelligible graphics that put actionable insights on full display. 

Harnessing the power of data visualization 

Take a second to absorb the contrast between left and right. The difference should be rather stark unless you happen to be a secret mathematical mastermind.  

Seriously, to the eyes of an average Joe, the table on the left appears as a random concoction of numbers that tell nothing substantive. On the other hand, the scatter plots make plain the positive correlation binding the variables together right from the get-go. 

That’s the power of visualization. It harnesses the ability to unlock hidden patterns, making it possible to connect the dots between disparate data points at once. For marketers who must repeatedly ask loaded questions such as which acquisition funnels lead to conversion, which time of the day are prospects most active and the like, visualization can help cut through the pile of raw data standing in the way of getting those questions answered.

And the best part? Visualization knows no boundaries. Whether it’s your team, board members or external stakeholders, presenting the data through graphics primes even the most boring of datasets to be readily processed and utilized regardless of who’s on the receiving end.   

Choosing the right graphics for data visualization

As wonderful as data visualization is, figuring out which type of visual aid would best represent the dataset can get tricky. And going with a suboptimal choice is hardly an option when doing so carries the risk of confusion or, worse, misinterpretation.   

Thanks to Dr. Andrew Abela, who put forward a comprehensive diagram on picking the right chart for different data types, choosing a visual can be boiled down into four basic criteria:

  • Comparison. Drawing a comparison between datasets over a specified period to pinpoint highs and lows. 
    • E.g., website traffic breakdown by source. 
  • Relationship. Establishing a correlation to see whether given variables positively or negatively influence one another.   
    • E.g., regional influence on sales growth.
  • Distribution. Gauging the range of a dataset to better understand how variables interact while checking for outliers.
    • E.g., fluctuation in average monthly lead conversion rate across a fiscal year. 
  • Composition. Charting out how individual parts make up a whole to create hierarchies within a given dataset. 
    • E.g., breakdown of marketing expenditures by strategic priorities. 

With these criteria in mind, use the following overview as further guidelines to single out the visual aid that’d best serve your needs: 

Column chart

A column chart refers to a graphical display in which vertical bars – the height of each proportionate to the category it represents – run across the chart horizontally.  

Nine times out of ten, a column chart will do the trick if you’re looking for a side-by-side comparison of 10 or fewer items. 

Column chart example

Line chart

What if you have more than ten datasets to be stacked against another? A line chart is your best bet. 

Unlike the column chart, a line chart runs a line through a series of dots. 

While it’s best known for highlighting the ups and downs across various data points, a line chart can also effectively compare the trends between different metrics by plotting multiple lines in a single chart.   

Line chart example

Scatter plot

A scatter plot is all about mapping out the correlation between two datasets. Also known as the cause-and-effect diagram, a scatter plot can help you see whether a set variable influences the other and which direction (positive or negative) the correlation is running towards. 

Scatter plot example

Pie chart 

A pie chart is used to deal with categorical variables to see how the total amount is split amongst them. It provides a general sense of the part-to-whole relationship that comes in handy when you want to find out the most and least effective channels for driving visitors to your website. 

Pie chart example

Word cloud

Perhaps the newest addition to the data visualization stack, a word cloud refers to a cluster of words displayed in different colors and sizes. It’s a nifty tool to visualize how the audience thinks about a given topic and discover the best and worst keywords when it comes to traffic generation. 

Word cloud example

Making headway with data visualization

All said and done, visualization is the present and future of marketing analytics. The good news is, with all you’ve seen and read so far, you’re ready to get the most mileage out of visualization.  

But if there’s anything I hope you’ve learned from this piece, it’s that images speak much louder than words. It’s time to take your marketing data visual.

Sophie Eom is cofounder and CEO of


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