Use cold colors in B2B, warm colors in B2C
B2B social media images attract more positive engagement when they use cold, dark colors because they signal competence. Warm colors trigger emotions so they work better in B2C.
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We know that social media posts with images are more popular and get more engagement.
But which images are most effective for B2C? And which ones for B2B audiences?
One major factor is color, as discovered by this Australian study.
Let’s take a look.
Use cold, dark color images in B2B and warm, colorful images in B2C
Channels: Social media | Image Ads | Marketing communications
For: Both B2C and B2B
Research date: May 2022
If your target audience is B2B, use cold and dark colors in your social media images (e.g. blue, dark green, purple) to signal competence and confidence.
If your target audience is B2C, use warm colors in your social media images (e.g. yellow, bright green, red) to stimulate emotions.
In both cases, use a rich variety of different colors in the image, not only one or two colors.
You will increase positive engagement on your social media posts (e.g. supportive comments).
Pro tip: It’s worth testing this in other contexts as well (e.g. blog images, banner ad, landing page).
This study analyzed the rate of positive comments (e.g. ‘awesome’, ‘interesting’) of 13,356 Instagram posts from 8 B2C (e.g. General Electric) and 8 B2C companies (e.g. Apple).
Positive comments of images increase with:
Colder, darker, less saturated, and more varied colors for B2B
Warmer and more varied colors for B2C
🧠 Why it works
When we engage with B2B content (e.g. this insight you are reading), we tend to do so for more rational reasons. For example, we want to gain information or solve a problem.
And we would rather get this from a highly competent brand that can deliver on what they say.
Cool, dark, colors are associated in our mind with status and competence (e.g. a black tuxedo, black limousine).
So the image colors make the B2B brand seem more competent, and we are more likely to engage with it.
When we engage with B2C posts (e.g. announcement of a new pair of sneakers), we are looking for emotions and self-enhancement.
And the reverse happens. Warm colors are more emotional, so we are more likely to engage with the warm, colorful content.
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This study focused on the impact of comments on Instagram posts. However, based on previous research it’s likely that the effect applies more broadly (e.g. warmer colors increased sales in a B2C online store).
The research did not look at the content of the images, only at the overall colors. What companies show in the images (e.g. the product, people’s faces) is likely to have a stronger impact.
🏢 Companies using this
Companies seem to mostly apply this effect correctly. B2B companies use colder images and B2C companies use warmer colors.
Fun fact: text descriptions in B2B Instagram posts tend to be double the length of those of B2C companies. This does not appear to give any gains.
An example from Morgan Stanley that correctly uses mostly cold colors but could have used more variety.
This example from Walmart uses a rich variety of warm colors (and a cat - which we’ve seen also has a strong impact)
⚡ Steps to implement
If you are B2B, choose colder brand color palettes and colors in images (e.g. dark blue, purple). If you are B2C, use warmer colors.
To measure the warmth of an image, look at the average Hue value of its pixels (part of the image HSV). You can do so with a program like OpenCV.
You can adjust the average Hue of existing images using software like Photoshop or other freely available software online.
🔍 Study type
Market observation (analysis of Instagram posts of 16 US Fortune 500 companies between June 2011 and January 2021)
The role of cool versus warm colors in B2B versus B2C firm-generated content for boosting positive eWOM. Industrial Marketing Management (May 2022).
Jumbum Kwon. University of New South Wales (UNSW) Sydney
Ka Wing Chan. UNSW Sydney
William Gu. UNSW Sydney
Felix Septianto. University of Queensland
Remember: This is a new scientific discovery. In the future it will probably be better understood and could even be proven wrong (that’s how science works). It may also not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.
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