The reason organisations visualise data is so people can better understand the data and make better decisions. Unfortunately, in many organisations, how that data is visualised isn’t nearly as clear as it could be.
Let’s look at some examples that some companies may think are exemplary, but in fact leave a lot of critical insight hidden.
The promoter scorecard
If you are in a marketing organisation, chances are you or your colleagues will need to analyse customer satisfaction data. Figure 1 below presents a typical customer satisfaction scorecard, showing the percentage of promoters broken down by segment and region.
Figure 1 – Typical scorecard showing people who think very highly of a product or service.
“What’s wrong with this?” you may ask. “I can see which regions are doing well and which are doing poorly. What am I missing?”
It turns out you are missing a lot.
Traffic light colours and colourblindness
First, if you are among the one-in-12 men (and a smaller fraction of women) who suffer from colour vision deficiency (colourblindness) you won’t be able to tell the difference between the dark green at the top of a segment and the dark red at the bottom of the segment. Want to get a sense of just how hard it is for that 8% of the population? Compare the regular image on the left with a colourblind simulation on the right.
Figure 2 – Original image on the left and Deuteranopia simulation on the right. Yikes!
It gets worse
Let’s look back at Figure 1. Assuming you do not have a problem with red and green, Region A in the East is really bad and so is Region H in the West. But wait a minute – Region A is at 26% and Region H is at 40.3%. Oh… the colour is based on ranking, not on magnitude.
Indeed, the inability to show magnitude has doomed this visualisation to failure, as the viewer is going to have to work very hard to answer these questions:
- How much larger is the percentage of promoters for Region C than Region A?
- What about Region F compared to Region H?
- How big is the drop from the previous quarter for Region N compared with the gain for Region C?
These are very reasonable questions, but they are hard to answer, let alone understand, when you just have numbers, colour and arrows pointing up and down.
Indeed, this gets to the heart of why this scorecard is deficient. You must be able to ‘know’ how big and small the numbers are relative to each other. With a good chart that taps in to your ability to perceive, you can ‘tell’ how big and small the numbers are relative to each other.
A better alternative for understanding marketing data
Glad you asked! Consider the dashboard shown in Figure 3, below.
Figure 3 – Customer Satisfaction dashboard
Look how easy it is to understand the magnitude of each region by comparing the length of the bars (humans are uncannily good at comparing bar length). Colour is only used to indicate which regions were worse than the previous quarter (orange) and which were better (grey). The chart in the centre allows you to easily gauge just how much better and worse, and the sparklines on the right – the grey lines with little red dots – allow you to see the selected quarter within the context of many quarters, with the red dot indicating when an increase or decrease is statistically significant.
And it’s so much simpler to make cross-segment comparisons. For example, it’s now easy to see that the worst performer in the West (Region H) would be the third best if it were in the East.
Good visualisation can change people’s behaviour
A good visualisation can do more than just help people see and understand their data. It can change behaviour.
Imagine you are charged with managing a marketing agency and you are hyperventilating over billable hours. It’s mid-year and you try to marshal the troops by showing them actual billable hours compared with the planned billable hours. Here’s a snippet from your PowerPoint presentation.
Figure 4 – Text table showing actual vs potential billable hours
You’re not going to win over a lot of hearts and minds with an image like this one.
Contrast this with a snippet from the Agency Utilisation dashboard featured in The Big Book of Dashboards.
Figure 5 – Area graph showing the difference between actual and potential
Now we can see what’s going on – just look at the improvement! At the end of June, the gap between actual and potential was cavernous, but look at how the gap narrows between July and November, with a slight drop off at the end of the year. This type of chart makes it so easy for people to see a problem and do something about it.
Is the way you show data in your organisation generating ‘ah-ha!’ reactions or just apathetic stares? If it’s the latter, then you should look into building visualisations that provide insight and impact.
Steve Wexler is co-author of The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios (Wiley, 2017).
Have an opinion on this article? Please join in the discussion: the GMA is a community of data driven marketers and YOUR opinion counts.
Read also:
TOFU, MOFU and BOFU: marketing funnel acronyms that lead to a successful campaign