How Microsoft made big conversion gains with small color tweaks

Why design and color usage matters in conversion optimization.

How Microsoft made big conversion gains with small color tweaks

Why design and color usage matters in conversion optimization.

Before/after color change with the colors blue, green, and black.
An illustration of the before/after color change with the colors blue, green, and black.
An example of the before and after color change made to Microsoft Bing search results.

Some of the most impactful experiments run on high-traffic websites involve improvements in color.

The right colors can mean the difference between a buy and a bounce. A prime example of this is a case study from Bing. Ron Kohavi, an expert in online experimentation and author of the book “Trustworthy Online Controlled Experiments” shared this experiment in a Harvard Business Review article. The test made subtle changes to the blue, black, and green on the Bing search results (Figure 1).

A side-by-side comparison of base and variant for the Bing color change A/B test
Figure 1: One the left, the control. One the right, the variant.
Comparison of color changes A/B tested by the Microsoft search engine Bing.
Figure 1: The top image shows the control colors. The bottom image shows the variant.

The change increased revenue by $10 million per year. But why did such a “small” change lead to such a big increase? A few reasons are likely when you look closely at Figure 2.

Side-by-side color change comparison.
Figure 2: Side-by-side comparison of the Bing search results color change.

Reason one: Not enough contrast

The first reason is not enough color contrast.

Contrast is how different the foreground color is to the background color in terms of light and darkness. It’s written as a ratio that compares the first number to one. When it’s a 1:1 ratio, that means the contrast is the same. The higher than one it is, the more different it is. Contrast impacts how easy something is to read. When you inspect the contrast ratio of the blue and green on the white background you learn that the blue in base is WCAG 2.0 AAA accessible.

This contrast ratio for the blue in base is high enough for both normal and large text. But, the blue in the variant is even stronger as shown in Figure 3. This extra contrast makes the darker blue easier to read.

Control

The blue has a contrast ratio of 7.09:1.

The blue has a contrast ratio of 7.09:1.

Variant

The blue has a contrast ratio of 11.21:1

The blue has a contrast ratio of 11.21:1

Figure 3: Contrast checkers, like WebAim (above) are a useful tool when you work with color.

When you inspect the green in base you find it’s only WCAG 2.0 AA accessible for large text. The green in the variant is better because it’s WCAG AA accessible for both large and normal text.

Control

The green has a contrast ratio of 4.25:1.

The color contrast ratio was 4.25:1, which failed accessibility tests for all forms of text except large text for AA compliance.

Variant

The green has a contrast ratio of 6.54:1

A higher contrast green that passes for both large and small text—except for small text AAA compliant.

Figure 4: Normal text in the control green is hard for many people to read. The variant green is more legible.

Reason two: Too much contrast

Generally speaking, the stronger the color contrast ratio, the easier text is to read. But, there’s a tipping point. 

When you reach the tipping point, you cross the line into reason number two control likely lost to the variant: too much contrast.

Working with contrast ratios is a delicate balance, but it’s worth the effort. Why? Because 2.2 billion people in the world experience some kind of visual impairment. So, lots of your customers likely experience color in unpredictable ways.

The control design uses pure black (#000) on pure white (#FFF). This has the highest possible contrast ratio of 21:1. This is too much.

Control

The black has a contrast ratio of 21:1.

Webaim's color contrast tool illustrating that pure black on pure white has a contrast ratio of 21:1.

Variant

The black has a contrast ratio of 13:77:1.

Webaim's color contrast tool illustrating that a less dark black on pure white has a contrast ratio of 13.77:1.

Figure 5: Pure black on pure white is the highest possible contrast ratio. It can sometimes be too much.

Contrast sensitivity (aka Irlen Syndrome) makes it hard for people to read pure black on pure white.

This happens when people’s eyes process high-contrast in a way that makes it “vibrate.” This vibration (called chromostereopsis for all you science nerds out there) happens for people with typical vision, too.

Figure 6 illustrates in color the visual effect of pure black on pure white for people that experience contrast sensitivity.

Three brightly color squares that have bright colored text on them. It creates a vibrating visual effect called chromostereopsis.
Figure 6: This vibration effect is called chromostereopsis. It’s similar to what people with Irlen Syndrome experience with too high of a contrast ratio.

Knocking the pure black in base down to a dark gray in variant lowers the contrast enough to not trigger a chromostereopsis-like vibration.

Reason three: Backward visual weight

Finally, the last reason the experiment worked is that in base the colors have a backward visual weight.

Visual weight is how much something pulls the eye’s attention. Big, bold, dense, saturated, and high-contrast things draw the eye. Small, light, airy, muted, and low-contrast things don’t. In the control, the black has the heaviest visual weight. This draws the eye in and encourages it to be read. But search results are for scanning—not for reading.

Ideally, people’s eyes would bounce from one headline to another and only enter the black text if the headline and link don’t answer the question. The order in which the text gets read in the control is: first the small text, second the title, and third the link. In the variant, the order is: first the title, second the link, and third the body content shown in Figure 7.

Control

The visual weight order is black, blue, green.

The visual weight of the colors is backwards compared to how it should be read.

Variant

The visual weight order is blue, green, dark grey.

The order the content the variant gets read in is headline, link, body content.

Figure 7: How colors work together and their visual weight impacts the order in which information gets read.

Good color use isn’t just about individual colors. It’s also about how colors compare to and work with one another. That is proven by the subtle, yet impactful, color change in the variant.

It ain't easy

Though the change is subtle and probably seems simple, color experimentation is hard.

The average designer cannot pull off color change successes like these. Case studies like this one from Bing and Google’s “50 Shades of Blue” experiment give a false impression. It seems like all you have to do is change some colors to make frustrated customers happy. Then, it rains happy users and lots of money.

But, a LOT happens visually, psychologically, and technically when people glance at a screen. That makes color both an art and a science.

Designers think they're in control of the color. In reality screens, operating systems, devices, etc. all treat color differently in ways purposeful and accidental. Experimenting with color is about finding the right place on the spectrum to safely serve the largest possible segment of your audience.

If you enjoyed this article, check out my new book, "Design for Impact: Your Guide to Designing Effective Product Experiments."

Design for Impact: Your Guide to Designing Effective Product Experiments book by Erin Weigel on a bookshelf. Propped up against the book is a die cut sticker that says, "Make things better—not just different"
Get a free sticker when you order a paperback copy of "Design for Impact."

Design for Impact

Your Guide to Designing Effective Product Experiments

Design for Impact features the Conversion Design process to help you and your team drive business growth. In it, Erin Weigel gives you practical tips and tools to design better experiments at scale. She does this with self-deprecating humor that will leave you smiling—if not laughing aloud.

The Good Experimental Design toolkit in the appendix presents everything you learn into step-by-step process for you to use each day.

Bonus! Get a FREE sticker when you order through this webshop.

 39,99 incl. VATAdd to cart

*This webshop serves EU customers only.

Check out these resources

Control 13 people with 5 confounds. Treatment 7 people with 5 confounds. Warning! This is an SRM.

What you need to know about sample ratio mismatches (SRMs)

Randomization within experimentation is important. It’s how we isolate the change we aim to learn about. When randomization goes wrong, you can get an SRM.

Go to resource
The Good Experimental Design toolkit templates

The Good Experimental Design toolkit

The Good Experimental Design toolkit templates and checklist level-up your experimental design. As Ronald Fisher learned, experiment data is only as good as the design you put into it.

Go to resource
SRM calculator UI

Lukas Vermeer’s manual sample ratio mismatch (SRM) checker

Randomization is the hidden power behind A/B testing. When randomized properly, the confounds in your data are completely removed. This allows you trust any cause/effect relationship you might observe.

Go to resource
Screenshot of Speero's A/B tesing tool comparison website

A/B testing tool comparison

Speero’s A/B testing tool comparison website Helping you find the right experimentation tool quickly and easily Speero’s A/B testing too comparison website includes a comprehensive list of options. If you’re […]

Go to resource
Scroll to Top