4 Colour Secrets for Sure-fire Success in Marketing Communication

Inspired by — Maureen Stone — Colour Expert & Research Manager at the Data Visualization Company ‘Tableau’

We at ThinkWhy intend to explore the world of colours with you, from the technical effects to the emotional affects of using colours. Our aim is that by the end of this piece you will be able to use colours to drive your point home— Be it a written data representation or a graphical.

Tiny details radically alter the way our brain’s perceive as-well-as understand information.

“Lesson here is that designers may think their use of colour is meaningless, but it can have emotional side effects that influence how readers understand the data.”

Colours are there to help us understand the information. They’re a visual-clue for what the data means. We can do many things with colours — e.g.

  • Discriminate between categories
  • Assign value along a scale
  • Assign value along a scale
  • Indicate more-or-less of something though different shades of the same colour
  • Make your colour-coded message/information ‘POP’ out from the background

1. Colour Semantics — One of the key things to keep in mind when choosing colour for data visualization is making sure the colours are “Semantically Resonant,” as Stone puts it, with the data they are representing.

Yes, we understand that this one sounds a bit tricky but it is the most basic of concepts within itself.

…Put simply, it means that anybody involved in marketing needs to pay heed to the relationship between a colour and the thing that the colour is being used for.

Lesson here is that “Everybody has a specific word or a concept they correlate with certain colours.

This rule is called the ‘Colour — Concept’ Association

As part of a research project from 2015, Maureen Stone and fellow Tableau research scientist Vidya Setlur did a research where they discovered that the following colours have very strong connections with the following concepts:

  • Yellow — Taxi / Cab
  • Red — Stop / Caution / Non- Vegetarian
  • Blue/ Green — Go / Safe / Vegetarian / Trees
  • Brown — Chocolate / Mud / Poop ;)

2. Be Discerning — One of the key things to keep in mind when it comes to colours used for data science, we need to discriminate: The colours should be different enough from one another so that they’re easy to tell apart in a visualization.

When choosing colours for data, Stone maps them out using colour space, or a modelling tool that shows the full range of colours. Example given below:

“If the colours are close together in colour space
Green & Yellow are right beside each other, for instance 
they’re also perceptually close.”
“And it’s best NOT to use 2 colours that are 
perceptually close together.”

3. Paint-Chip Effect — Oneshine of the key things to keep in mind when it comes to colours is the size changes the way colours are perceived — When seen in a small size — Colours appear less-colourful!

We know what your thinking…. NO, It’s not the difference in outdoor and indoor light that’s affecting your perception

Put Simply… The larger a colour swatch — the more its intensity! The smaller the colour swatch, the less intense it will appear!

As Maureen Stone says “This is because of the colour’s ‘Chroma’ — It is the measurement of colour by its dimension. It has to be adjusted as per the size!”

4. Colours have Affect — Have you ever seen a car go by on the street and suddenly felt you’re in another time-&-place…? You find yourself immersed in the sensory overload of long-lost memories — This is because all colours evoke emotional connections in our brains to sights, words, sounds and smells, all long-forgotten until stimulated by a particular shade of colour.

As Stone puts it, “Everyone in the design realm knows that colour has affect, & they have examples and rules” based on that knowledge.

One question we had was: Even on a bar chart, does that count?

The answer, according to Stone, is “Yes.”

In conjunction with researchers at Simon Fraser University in British Columbia, Canada, she conducted a study that asked people to colour bar charts so that they conveyed certain feelings, like calm, playfulness, or negativity.

Their research showed certain patterns: People selected more muted colours for a calmer bar chart and brighter colours (high in Chroma) for a playful chart. Meanwhile, they chose dark colours to convey negativity.

The message here is that, “Using this information to colour charts in a way that is consistent with the data, can emphasize the message being conveyed!”

Colouring a chart in a palette that induces calmness may not be as functionally important to data visualization as colour distinctness or semantic correlation. But, considering all of these factors together will help people absorb and understand data more easily, Stone says.

She shows through her research, the fascinating science behind colour theory isn’t dulled by the cold mathematics of big data. Instead, it marries Art & Science in a way that is Ergonomic i.e. Both functional and aesthetically pleasing.

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