- How do I appeal to the largest number of consumers? (TURF analysis)
- How do I prioritise marketing messages or product attributes? (Max Diff)
- How do I find out what people value in my (new) product / service? (Conjoint)
- How do I identify what drives a desired behaviour or outcome? (Key driver analysis)
- How do I know what to prioritise to meet strategic goals? (Gap analysis)
- How do I build consumer loyalty? (Consumer journey mapping)
- How do I use behavioural science to improve my research? (Cognitive biases)
- How do I live without you? (LeAnn Rimes)
- How do I know how many people will buy my product at a given price? (Van Westendorp’s price sensitivity meter)
- How do I assess the impact of my advertising? (Ad effectiveness)
- How do I turn data into clear findings (Data visualisation)
- How do I tap into the unconscious perceptions that influence decision-making? (Implicit response testing)
- How do I reduce a large amount of data into something more meaningful? (Factor analysis)
- How do I group people together based on shared characteristics? (Segmentation)
- How do I forecast market share at a given price point? (Brand price trade off)
- How do I account for cultural differences when surveying across markets? (ANOVA)
- How do I judge brand performance relative to competitors (Correspondence analysis / brand mapping)
The importance of good data visualisation
Every piece of good research should deliver findings that grab attention and are remembered. But for most people, this won’t happen if these findings are shown to them as raw data, or even in nicely aggregated and formatted Excel tables – this is why we visualise data in graphs and graphics.
The above statement is completely obvious, but how to visualise the data in the most impactful way is sometimes less obvious – I’m sure you’ve had pleasure of scrolling through 100 pages of densely populated bar charts at least once!
Data visualisation is an art, it enables us to tell stories with data – and stories stick. Not only is it more enjoyable to look at pretty visualisations, but it makes the data more believable thanks to the cognitive ease bias (which we’ve helpfully covered in another post!).
We like to think the reports and presentations we deliver to clients successfully tell impactful and memorable stories, but we wanted to share a few tricks of the trade in case you need some inspiration for your own data visualisations.
1. Simplify the data
It’s tempting to want to show off all of the data you’ve collected. Afterall, you’ve spent time and money covering all bases and don’t want anything to go to waste. But you have the data tables for posterity. When visualising data, it’s important to first decide the point you’re trying to make, and only include data relevant to that point. To simplify things further, you can group data together into net scores (e.g. all spontaneous mentions of brands in the same category) or use a rolling average to smooth a line chart.
2. Layer Information
The best visualisations can be instinctively understood at a glance, revealing more information as the reader spends more time looking. This means you need to pay special attention to first impressions. A clear and accurate title paired with the right visual should be able to convey the key finding. For example, in a line chart labelled ‘Prompted brand awareness’, you instantly understand an upwards trending line to indicate positive results over time – great, as this is the point you want to land. The reader can then go on to look at the details (if they so wish), e.g. over what time period was this increase? What was the starting point? By what proportion did awareness grow? And so on.
3. Declutter the design
So you’ve got some simple data, and you’ve designed a visualisation that can be understood at a glance – perfect. Stop there. Don’t be tempted to fill the slide with more information, and go easy on the labels and design elements. You’ll quickly undo all of the good work if you label every point on a line chart or use a rainbow of colours set against a half-faded image. Unless you’re writing a report that’s designed to be read, you’ll want to keep the commentary in the notes section to be voiced over during your presentation.
4. Create mnemonics
A mnemonic is a tool that helps us remember information. They can come in the form of a song, rhyme, acronym, image, phrase, or sentence, such as ‘Never Eat Shredded Wheat’, or ‘Beer before wine, you’ll be fine; wine before beer, you’ll feel queer’. A catchy headline or chart title can work wonders for making your data memorable, and if chosen correctly so can an accompanying image. You won’t want to take this approach with every finding though, that would feel gimmicky and quickly lose its impact – pick your ‘hero stats’ and treat them to a mnemonic.
5. Add emphasis
Often, there’s one data point in your visualisation that you want your audience to pay particular attention to. When this is the case, it’s important to visually emphasise or signpost this data point to make sure it’s the first thing the eye is drawn to. Simple graphic devices such as boxes, arrows, contrasting colours and even negative space can transform a page of data into a beautiful, clear visualisation.