Obtaining customer feedback is difficult. You need strong survey questions that effectively derive customer insights. Not to mention a distribution system that shares the survey with the right customers at the right time. However, survey data doesn't just sort and analyze itself. You need a team dedicated to sifting through survey results and highlighting key trends and behaviors for your marketing, sales, and customer service teams. In this post, we'll discuss not only how to analyze survey results, but also how to present your findings to the rest of your organization.
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Ordinal scales are used to depict the order of values. For this scale, there's a quantitative value because one rank is higher than another. An example of an ordinal scale is, "Rank the reasons for using your laptop." You can analyze both mode and median from this type of scale, and ordinal scales can be analyzed through cross-tabulation analysis.
Interval scales depict both the order and difference between values. These scales have quantitative value because data intervals remain equivalent along the scale, but there's no true zero point. An example of an interval scale is in an IQ test. You can analyze mode, median, and mean from this type of scale and analyze the data through ANOVA, t-tests, and correlation analyses. ANOVA tests the significance of survey results, while t-tests and correlation analyses determine if datasets are related.
Ratio scales depict the order and difference between values, but unlike interval scales, they do have a true zero point. With ratio scales, there's quantitative value because the absence of an attribute can still provide information. For example, a ratio scale could be, "Select the average amount of money you spend online shopping." You can analyze mode, median, and mean with this type of scale and ratio scales can be analyzed through t-tests, ANOVA, and correlation analyses as well.
Once you understand how survey questions are analyzed, you should take note of the overarching survey question(s) that you're trying to solve. Perhaps, it's "How do respondents rate our brand?"
Then, look at survey questions that answer this research question, such as "How likely are you to recommend our brand to others?" Segmenting your survey questions will isolate data that are relevant to your goals.
Additionally, it's important to ask both close-ended and open-ended questions.
A close-ended survey question gives a limited set of answers. Respondents can't explain their answer and they can only choose from pre-determined options. These questions could be yes or no, multiple-choice, checkboxes, dropdown, or a scale question. Asking a variety of questions is important to get the best data.
An open-ended survey question will ask the respondent to explain their opinion. For example, in an NPS survey, you'll ask how likely a customer is to recommend your brand. After that, you might consider asking customers to explain their choice. This could be something like "Why or why wouldn't you recommend our product to your friends/family?"
Quantitative data is valuable because it uses statistics to draw conclusions. While qualitative data can bring more interesting insights about a topic, this information is subjective, making it harder to analyze. Quantitative data, however, comes from close-ended questions which can be converted into a numeric value. Once data is quantified, it's much easier to compare results and identify trends in customer behavior.
It's best to start with quantitative data when performing a survey analysis. That's because quantitative data can help you better understand your qualitative data. For example, if 60% of customers say they're unhappy with your product, you can focus your attention on negative reviews about user experience. This can help you identify roadblocks in the customer journey and correct any pain points that are causing churn.
If you analyze all of your responses in one group, it isn't entirely effective for gaining accurate information. Respondents who aren't your ideal customers can overrun your data and skew survey results. Instead, if segment responses using cross-tabulation, you can analyze how your target audience responded to your questions.
Cross-tabulation records the relationships between variables. It compares two sets of data within one chart. This reveals specific insights based on your participants' responses to different questions. For example, you may be curious about customer advocacy among your customers based in Boston, MA. You can use cross-tabulation to see how many respondents said they were from Boston and said they would recommend your brand.
By pulling multiple variables into one chart, we can narrow down survey results to a specific group of responses. That way, you know your data is only considering your target audience.
Below is an example of a cross-tabulation chart. It records respondents' favorite baseball teams and what city they reside in.
As we mentioned in the last section, not all data is as reliable as you may hope. Everything is relative, and it's important to be sure that your respondents accurately represent your target audience.
For instance, your data states that 33% of respondents would recommend your brand to others. 75% of them were over 40 years old, yet your target audience is 18 to 29 years old. In this case, this data isn't statistically significant as the people who took your survey don't represent your ideal consumer.
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