Survey Evaluation Metrics: Make Your Survey More Efficient

Surveys are a powerful tool for gathering information and insights from individuals or groups of people. However, to ensure that your survey is effective, it’s important to evaluate its performance using survey evaluation metrics. These metrics can help you to identify areas for improvement and make your survey more efficient.

In this content, we’ll explain the survey evaluation metrics and how to make your survey more efficient.

Why Survey Evaluation Metrics are Important For Your Business?

Survey evaluation metrics are essential for assessing the effectiveness of surveys and ensuring that the data collected is reliable and accurate. The following are some reasons why survey evaluation metrics are important:

Quality assurance

Quality assurance involves ensuring that the data collected is accurate, reliable, and free from errors. This is important because inaccurate or unreliable data can lead to incorrect conclusions and poor decision-making. Quality assurance can be achieved through various means, such as pre-testing survey questions, ensuring that survey instructions are clear and easy to follow, and using appropriate data collection methods.

Measurement validity

Measurement validity refers to the extent to which a survey accurately measures what it is intended to measure. It is important to establish measurement validity because if a survey question does not accurately measure the construct being studied, the resulting data may be inaccurate and misleading. Validity can be assessed through various means, such as content validity, criterion validity, and construct validity.

Sample representativeness

Sample representativeness refers to the extent to which the survey sample is representative of the population being studied. It is important to assess sample representativeness because if the survey sample is not representative of the population, the resulting data may not be generalizable to the larger population. Sample representativeness can be assessed through various means, such as assessing the demographic characteristics of the sample and comparing them to the population of interest, and assessing the response rate of the survey.

Data analysis

Data analysis involves examining the survey data to draw meaningful conclusions and insights. It is important to conduct data analysis because it allows researchers to identify patterns, relationships, and trends in the data. We can conduct data analysis through various means, such as descriptive statistics, inferential statistics, and data visualization. Proper data analysis can help researchers to draw accurate conclusions and make informed decisions based on the survey data.

What Are The Survey Evaluation Metrics?

Response Rate Survey Metrics

Response rate is a measure of the percentage of people who respond to a survey out of the total number of people who were invited or sampled to participate in the survey. It is an important indicator of the quality of a survey, as a low response rate may indicate that the survey results may not be representative of the target population.

The formula for calculating the response rate of a survey is:

Response Rate = (Number of Completed Surveys / Number of Invitations or Sampled Population) x 100

For example, let’s say that a company sends out a survey to a randomly selected sample of 1,000 customers, and receives 350 completed surveys in return. To calculate the response rate, we would use the formula above:

Response Rate = (350 / 1000) x 100 = 35%

This means that the response rate for this survey is 35%, indicating that 35% of the customers who were invited to participate in the survey actually completed it.

Another example could be an online survey sent to a company’s email list of 10,000 subscribers, with 2,000 subscribers completing the survey. The response rate would be:

Response Rate = (2,000 / 10,000) x 100 = 20%

This means that 20% of the email subscribers who received the survey actually completed it.

It’s worth noting that response rates can vary widely depending on factors such as the target population, survey topic, and mode of survey administration. In general, response rates above 60% are considered very good, while rates below 30% may raise concerns about the representativeness of the survey results.

Completion Rate

Completion rate is one of the most important survey evaluation metrics that refers to the percentage of respondents who have completed the entire survey compared to the total number of individuals who were invited or started the survey.

For example, if there are 100 invitation but only 80 people completed the entire survey, then the completion rate would be 80%.

To calculate the completion rate of a survey, follow these steps:

Determine the number of people who were invited to take the survey.

Subtract the number of people who started the survey but did not complete it from the total number of invitations.

Divide the number of completed surveys by the adjusted total number of invitations.

Multiply the result by 100 to get the percentage.

For instance, let’s say that you invited 500 people to take your survey, and 400 people started the survey, but only 350 people completed the entire survey. Then, you can calculate the completion rate as follows:

Adjusted total invitations = 500 – (400 – 350) = 450

Completion rate = (350 / 450) x 100% = 77.8%

Therefore, the completion rate of your survey would be 77.8%.

Average Time to Complete

The average time to complete a survey refers to the average amount of time it takes for respondents to finish the survey. It’s an important metric that can help researchers evaluate how long their surveys take and whether respondents find them engaging or too time-consuming.

To calculate the average time to complete a survey, follow these steps:

Determine the total time taken by all respondents to complete the survey.

Divide the total time by the number of respondents who completed the survey.

For example, suppose you send out a survey to 200 respondents, and it takes them the following times to complete:

Respondent1: 2 minutes

Respondent2: 5 minutes

Respondent3: 10 minutes

Respondent4: 3 minutes

Respondent5: 6 minutes

To calculate the average time to complete the survey, you would first determine the total time taken by all respondents, which would be:

2 + 5 + 10 + 3 + 6 = 26 minutes

Then, you would divide the total time taken by the number of respondents who completed the survey, which in this case is 5:

26 minutes ÷ 5 respondents = 5.2 minutes

Therefore, the average time taken to complete the survey is 5.2 minutes.

Note that this calculation assumes that all respondents who started the survey completed it. If some respondents did not complete the survey, you may want to exclude their incomplete responses from the calculation or calculate the average time to complete only for those who finished the survey.

Net Promoter Score (NPS)

Net Promoter Score (NPS) is a customer loyalty metric that measures the likelihood of customers recommending a company, product, or service to others. It’s a simple and widely-used metric that ranges from -100 to 100 and is based on a single question: “How likely are you to recommend our product/service/company to a friend or colleague?”

The NPS score is based on a scale of 0 to 10, with 0 being “not at all likely” and 10 being “extremely likely.” Respondents are classified into three categories based on their responses:

Promoters: We consider that Customers who give a score of 9 or 10 are “promoters” because they are highly likely to recommend the company, product, or service to others.

Passives: We consider that customers who give a score of 7or 8are “passives” because they are satisfy but not necessarily loyal, and may easily switch to a competitor.

Detractors: We consider that customers who give a score of 0 or 6 are “detractors” because they are unlikely to recommend the company, product, or service to others and may even spread negative word-of-mouth.

To calculate the NPS, follow these steps:

Calculate the percentage of respondents who are promoters by dividing the number of respondents who gave a score of 9 or 10 by the total number of respondents and multiplying by 100.

Calculate the percentage of respondents who are detractors by dividing the number of respondents who gave a score of 0 to 6 by the total number of respondents and multiplying by 100.

Subtract the percentage of detractors from the percentage of promoters to get the NPS score.

An Example For Nps

For example, let’s say that 100 respondents took a survey and gave the following scores:

Promoters (9-10): 60 respondents

Passives (7-8): 20 respondents

Detractors (0-6): 20 respondents

To calculate the NPS, you would first calculate the percentage of promoters and detractors:

Percentage of promoters = (60 / 100) x 100% = 60%

Percentage of detractors = (20 / 100) x 100% = 20%

Then, you would subtract the percentage of detractors from the percentage of promoters:

NPS score = 60% – 20% = 40

Therefore we can say that the NPS score for this survey is 40 and thats a good score. Generally, we can say that an NPS score of 0 to 30 is good, 31 to 70 is excellent, and 71 to 100 is exceptional.

Satisfaction Score

Satisfaction score is a metric that measures the overall satisfaction of customers with a product, service, or experience. It’s typically based on a scale of 1 to 5 or 1 to 10 and we calculate by asking customers to rate their level of satisfaction with specific aspects of the product, service, or experience.

To calculate a satisfaction score, follow these steps:

Decide on a satisfaction scale. This could be a scale of 1 to 5, 1 to 10, or any other scale that makes sense for your survey.

Determine the question or questions that you will use to calculate the satisfaction score. For example, you could ask customers to rate their satisfaction with the product, service, or experience overall, or you could ask them to rate their satisfaction with specific aspects of the product, service, or experience, such as customer service or ease of use.

Collect responses from customers and calculate the average score.

An Example

Let’s say that you send out a survey to customers asking them to rate their satisfaction with your product on a scale of 1 to 10, with 10 being extremely satisfied and 1 being extremely dissatisfied. You receive the following responses:

Customer-A: 9

Customer-B: 7

Customer-C: 8

Customer-D: 10

Customer-E: 6

To calculate the satisfaction score, you would first add up all of the ratings and then divide by the number of respondents:

Total satisfaction score = 9 + 7 + 8 + 10 + 6 = 40

Number of respondents = 5

Average satisfaction score = 40 / 5 = 8

Therefore, the average satisfaction score for your product is 8 out of 10.

Note that, we can calculate satisfaction score based on any scale that you choose, and you can also calculate scores for specific aspects of your product or service by asking targeted questions on your survey.

Open-Ended Responses

Open-ended response metrics in surveys typically refer to the process of analyzing and categorizing the qualitative data obtained from respondents’ answers to open-ended questions. We can use this metrics to identify themes, patterns, and trends in the data that may not have been captured by closed-ended questions or quantitative metrics.

The process of analyzing open-ended responses typically involves a few steps:

Transcribing: Transcribing the responses into text format so they can be more easily for analyzing.

Coding: Developing a set of codes or categories to classify the responses based on their content or theme. This may involve assigning codes based on keywords or concepts that appear in the responses.

Categorizing: Grouping the responses into broader categories or themes based on the codes assigned in the previous step.

Analyzing: Analyzing the results to identify patterns, trends, and insights that can inform decision-making or further research.

Open-ended response metrics can provide valuable insights into respondents’ attitudes, opinions, and experiences that may not be captured by closed-ended questions or quantitative metrics. However, analyzing open-ended responses can be more time-consuming and subjective than analyzing quantitative data, and the results may be more difficult to generalize to larger populations.