Analyzing Survey Data

Statistical Significance

It is important to remember that to gather accurate results from your survey you need a large sample size. The larger the sample size, the more likely the result is to represent the population. This also ensures that your survey data is reliable and prevents you from making assumptions. 

Your respondents should be an accurate representation of the target market. For instance, if your target customers fall under the age group of 20 to 35, but you receive most responses from age groups beyond 35, it indicates that your survey data is not statistically significant. 

Remove incomplete responses

Your respondents may skip some questions and leave them blank. Incomplete responses can throw off your survey data. Before you analyze the responses you should remove the incomplete answers. 

Use Qualitative Data to understand the Quantitative Data

Qualitative data helps you understand the ‘why’ behind a customer’s thought or decision-making process. Quantitative data helps understand the trend and preferences of customers.

For instance, you surveyed your customers on why customers were not becoming paid customers even after using the product during a free trial period. 

Asking close-ended questions gives you the quantitative data that 65% of users claim that they are satisfied with the product. The data does not tell you why they are still not converting if they find the product satisfying. To figure this out, you can ask open-ended questions such as “What is stopping you from becoming our paid member?” 

This will help you reason with your quantitative data and help you understand the reason behind their unwillingness to subscribe as a paid customer. 

Sample Size

Always keep in mind that sample size is important. A large sample size ensures that your data is reliable and accurately represents the target population. It reduces bias and margin of error. 

Don’t confuse Correlation with Causation

Marketers often confuse correlation with causation when they are trying to draw conclusions from the survey data. It is the most common mistake you can make by jumping to the conclusion that only because two data are correlated, one is causing the other. 

To avoid making this sort of error look through all the factors that could be responsible for the result. Use qualitative data to understand the survey data. 

For example, you are surveying product satisfaction after updating your product features this year. 

• You compare your current result with your previous result when you launched the product, and you see that 
• Customer satisfaction with your product has increased by 5% in comparison to the previous year.
• You conclude that after updating the product your customer satisfaction has increased. 

However, your conclusion may not be entirely true. The two factors may have a clear correlation; it does not mean that customer satisfaction increased with product development. It could also be the case that your customer service, website/app performance, etc. had a positive influence on customer’s purchase decisions. Other factors can be the reason that caused the increase in customer satisfaction. 

Summarizing,

You need to analyze your survey data to understand how your company is performing and customer experience. Use survey software to work through the large volume of data and get accurate results free of human error.