Using Charts Effectively to Report on Survey Ratings
The best way to visualize survey data depends on both the type of data and the story the data tell.
In a prior post I discussed visualizing the composition of an audience. In this post I turn my attention to visualizing ratings.
Rating questions are ubiquitous in market research surveys. They are used to measure customer satisfaction, evaluate product concepts, assess brand performance, and more. They are so common that market research practitioners can chart the results with our eyes closed. But if we close our eyes, we might miss the story in the data.
With eyes wide open, let’s look at the ways our ratings charts impact how our readers interpret the findings.
- Colors are loaded with meaning. Color choices that reinforce the findings can make a chart easier to understand.
- The more data we present the higher the reader’s cognitive load. Reducing the data charted to only that necessary to support the story helps the reader focus on what’s important.
Colors Have Meaning
Let’s consider the following typical 5-point scale:
- Extremely unlikely
- Very unlikely
- Neither likely nor unlikely
- Very likely
- Extremely likely
The chart colors and the words comprising the scale should reinforce one another. Generally, cool colors for positive scale points and warm colors for negatives align with human expectations. Given these exceptions, stoplight colors – green, yellow, red – are an obvious choice. However, they are not universally accessible due to red-green color blindness. Furthermore, yellow is associated with caution, so may have a more negative connotation than is appropriate for a neutral midpoint. Apart from natural choices, brand colors can also play a role in color choice. If a brand’s logo is red, for example, using red for negatives would not be a viable choice.
At Big Village we use greens for positives, blue for neutrals, and browns for negatives when working within our own color palette, as shown below. When working with client palettes we develop an appropriate cool-to-warm color set. This approach can easily be extended from 5-point scales to 7-points or more with additional tones of the same basic colors.
Not all rating scales are balanced between positives and negatives. When a scale is all positive (e.g., good, better, best), all negative (e.g., mild, moderate, severe), or skewed (as in the example below) the colors used should be adjusted accordingly.
Choose Scale Points to Display Thoughtfully.
One way we help our readers digest our research findings is through data reduction. Part of our job as market researchers is to determine which findings are important and focus our reports on those findings. For scale questions, this means being deliberate in deciding which scale points to include in our charts.
A common practice is to chart only the positive scale points, as in the example below.
In this example, the chart implies the results are the same for both attributes thus charting the full scale provides a more complete picture.
Perhaps the full scale also shows no difference:
But it might show a large difference:
Researchers should consider the shape of the data, as in the examples above, when deciding which scale points to show.
However, that is not the only consideration. The content of the question and the business implications also matter.
- Perhaps the question has to do with the customer experience, for example asking: “How likely are you to buy from this brand again?” If the brand believes it can sway those that are “very unlikely” to change their minds, but not those “extremely unlikely” then displaying all five scale points is meaningful.
- Perhaps the question has to do with a concept test, for example asking: “How likely would you be to purchase this product?” If the brand is focused on targeting the portion of the market that is likely to buy, it is less important to know the distribution between those very and extremely unlikely to buy. This makes Example 1 the better choice since it focuses on the important data, removing the extraneous.
Taken further, the example below might also be sufficient in this case, where the positive scale points are combined into a “top 2 box” net rating.
Reducing the amount of data that is charted reduces the reader’s cognitive load and helps them focus on the most important findings. However, sometimes complexity is necessary to illustrate key results. We need to be thoughtful to include enough data to illustrate our story, but no more.
Select the Best Chart Type.
In addition to being thoughtful about what to display in your charts, it is important to determine the chart type that can best do justice to interpretation of the data. In the above ratings examples, I used stacked bar charts; there are more options to choose from. Let’s focus on options to consider if the ratings of an attribute can be reduced to a single number. This might be, for example, a mean rating or top-2-box rating.
Pictogram charts use stars or other simple shapes to illustrate the data. They can be engaging and stand out from a sea of bar charts.
However, the choice of shape must be thoughtful. For example, stars are viewed as positive, making them most appropriate for rating scales that range from less positive to more positive, rather than from negative to positive. Circles tend to be neutral shapes, so can be used for various types of scales.
Mean or top-2-box ratings can also be charted as dots or bars, as in the examples below. This can be helpful when comparing ratings for multiple series, such as different brands. It makes comparing the results across brands easy.
Focus on important attributes.
In addition to reducing the number of scale points to chart, reducing the number of attributes to include in a chart can also help focus the reader on what’s most important. This might mean displaying the results only for the top-rated product concepts or the customer experiences with the most room for improvement. The idea behind data reduction is to make choices that enable focus without losing sight of context.
When charting the results of scale questions be deliberate in your choices of colors, which scale points to show, which chart type to use, and which attributes to include. Don’t just use the defaults or copy what you did last time. Being thoughtful in designing data visualizations can help hone your story so it is easy for your reader to understand the story with focused insights.
Written by Ashley James, Vice President of B2B Program Management at Big Village Insights.