Customer surveys are a great way to gain valuable information for your company. They dig deeper into the likes and dislikes of your customers concerning your products, services, and brand as a whole, going above and beyond your sales revenue and goals.
The first thing to do is to get data from client satisfaction surveys. The next step is to learn how to analyze, summarize, and display this survey data so that you can draw conclusions and make adjustments that will help your organization grow and expand. In this comprehensive guide, you will learn how to analyze survey data:
Recognise The Four Stages of Measurement.
The four levels of measurement should be familiarized before any data analysis. Based on these thresholds, the appropriate metrics for survey questions and the type of statistical analysis to employ are established. There are four types of measurement scales: nominal, ordinal, interval, and ratio.
- Nominal Scale: Classifying information with no numerical value, like labels, is the job of nominal scales.
- Ordinal Scale: Value rankings can be shown on an ordinal scale. Because one position on this scale is superior to all others, it may be numerically measured.
- Interval Scale: Values are ranked, and the gap between them is shown via an interval scale. There is no actual 0 point on these scales, but the continuous nature of the scale makes them useful for quantitative purposes.
- Ratio Scale: When comparing two numbers, a ratio scale shows the relative position of the two numbers and the magnitude of the difference between them. Since even the lack of a feature can give information, ratio scales have quantitative value.
Decide on the Survey question(s) to use.
Take note of the larger survey question(s) you are attempting to answer once you fully grasp how survey questions are analyzed. The question could be, “How do respondents rate our brand?”
Take a look at survey inquiries like “How likely are you to recommend our brand to others?” that gets at the heart of this research inquiry. Your survey responses will be more useful if you divide them into sub-questions.
Alternating between asking yes/no questions and more free-form inquiries is also crucial.
These creative advanced cool excel charts and graphs can make your dashboard stand out of the crowd and deliver more information than a regular excel charts.
A survey question with only a few possible responses. Participants are limited to a single answer choice and cannot elaborate. Any question type might be used, including yes/no, multiple choice, checkboxes, dropdown menus, and even a scale. The best information can be gleaned from answering a wide range of questions.
The respondent can elaborate on their thoughts in an open-ended survey question. For instance, in an NPS survey, you might inquire about the respondent’s propensity to advocate for your company. The next step could be to inquire about the rationale for the customer’s selection. Questions like “Why or why not would you recommend our product to friends/family?” are examples of this type of inquiry.
Focus on Numerical Information
The use of statistics to derive conclusions makes quantitative data particularly useful. While qualitative data is sometimes more illuminating than quantitative data, its subjectivity makes it more challenging to analysis. However, quantitative data is gleaned from questions with definite answers that can be translated directly into numbers. When outcomes and customer behavior are measured, comparisons and patterns may be easily identified.
When doing a survey analysis, quantitative data should be the starting point. The reason being quantitative statistics can shed light on the meaning of qualitative information.
Use Cross-tabulation To Gain Insights into Your Demographic
Looking at everyone’s answers together will give you a better picture of the whole picture. Unqualified respondents can overwhelm your data and distort your survey’s findings. Instead, you can examine how your intended audience replied to your questions by segmenting replies using cross-tabulation.
Recognize the Data’s Relevance in Statistical Terms
As we discussed, you can only sometimes trust online information. Context is everything, so make sure your survey takers represent your intended audience.
A more representative cross-section of the population can be gleaned from survey replies by employing the statistical technique of random sampling. The larger the sample size of your survey, the more reliable your findings will be.
Consider Causation versus Correlation
Understanding the reliability of your survey results is another crucial part of any study. Take the hypothetical example of discovering a link between selling ice cream and the number of automobile thefts in Boston. Sales of ice cream and allegations of auto theft rose over a month. These variables probably have no relationship, but the data may imply otherwise.
Despite their correlation, there is no necessary causal relationship between the two variables. The two “dependent” variables in this scenario are influenced by a third, “independent” variable. In this scenario, the issue is heat. More people buy ice cream when the temperature rises. And because more individuals are going outside, more criminal activity is possible.
Even if this is an extreme case, you should only decide based on sufficient evidence. Gather as much information as possible before making assumptions about what motivates customers.
Compare New Data with Past Data
Data from the present is useful for keeping you abreast of developments, but comparing this information to the past is also important.
If this is your first time analyzing data, use this as a starting point for future studies. You can use this history as a benchmark against which to measure future performance and observe trends from quarters to years. You can monitor information on particular groups to determine if they benefit from your efforts.
While data collection is essential, it may be easier to draw conclusions supporting your company goals with a systematic approach to analysis.
Successful companies have always made it a priority to hear from their customers. Having your online surveys shared is only half the battle. Understanding how to analysis survey data is crucial for acting on customer input and making appropriate adjustments.
Methods for conducting surveys, analyzing the results, and concluding are discussed in this blog. You must compare your survey data to historical findings and standard industry metrics for the best outcomes.