The Complete Guide to Acceptable Survey Response Rates

Just 9%.

That’s the acceptable response rate in the worked example further down this post, for a survey on a 1 to 5 scale where you want 95% confidence within half a point.

Most guides would call 9% a failure. It might be, but not necessarily, once you understand why.

Focusing on increasing your survey response rate is important but I’m often asked:

What is the minimum acceptable survey response rate for a customer feedback survey: 1%, 15%, 50%?

This question also comes in the form:

What is the statistically valid response rate?

These are good questions but unfortunately the answer is not a single response rate but a range depending on what you are trying to determine.

In this post we’ll discover that what is important is not so much the response rate, but the total number of responses and the level of error you’re willing to accept.

Then we’ll use that information to calculate the acceptable response rate for your survey.

Note: The approach here works well for standard customer feedback surveys but if you are using Net Promoter Score you need to look at this post: How to calculate Margin of Error and other stats for NPS®

How Do You Calculate Survey Response Rate

Response rate is the number of completed responses divided by the number of people you invited to respond. There are only two variables in that calculation.

Number of invites

This is the number of people you invite to take your survey.

It could be a “census” in which you ask everyone in your audience or it could be a “sample” where you only invite a proportion of people.

Number of responses

This is the number of completed responses you receive for your survey. Note that you might also count partially completed responses if they provide valuable information for your business.

Response Rate

The calculation for response rate is simple: it is the number of responses divided by the number of people you invited to respond. The number is generally reported as a percentage, e.g. a 25% response rate.

In an equation that formula looks like this:

$$\text{Response Rate} = \frac{\text{Number of Responses}}{\text{Number of Invites}}$$

Survey Response Rate Formula

What Is a Good Survey Response Rate

A good survey response rate is one that is above average and, based on some industry data, that would be anything above 25%, subject to having enough total responses.

Industry Response Rate Data

Recent data, SurveyMonkey’s 2025 platform data, is useful because it’s current and split by survey type: opted-in customer email lists average a 49% response rate, while all-staff engagement surveys average around 22%.

SurveyGizmo also published the following chart

Real World External and Internal Survey Response rates

At Genroe our experience, with well crafted customer feedback surveys, is a response rate of between 10% and 30% depending on how engaged the audience is with the company.

But response rates are also generally trending down.

Pew Research’s polling data shows telephone survey response rates falling from 36% in 1997 to 9% by 2016 where they have stayed since.

Our pros and cons of surveys post covers what else is compounding that decline, including AI-generated fake responses.

What Is a Good NPS Response Rate

A good NPS response rate is one that is above around 20%, subject to having enough total responses.

See this post for more details on exactly how many responses you need: How to calculate Margin of Error and other stats for NPS®

You can see from this data published by CustomerGauge, using their extensive CustomerGauge NPS Benchmarks Survey, that most organisations (~51%) have response rates of 20% or higher.

If your response rate is below that figure you might like to review how you might improve your response rate.

NPS Response Rates

Statistically Valid Response Rates

The more important question is what is a statistically valid response rate. It is one that gives you enough responses to keep your margin of error inside the range you’re prepared to act on.

Statistical error can be confusing but here are three common measures: standard error, margin of error and confidence interval.

If you’ve ever tried to understand those terms it’s easy to become confused, so here’s a quick summary of each for clarity.

Standard Error – is a generic term that applies to many types of sample statistics (mean, standard deviation, correlation, etc). When we talk about Averages, the Standard Error is equal to the Standard Deviation of the sample.

Margin of Error – the amount within which you are confident (e.g. 90%, 95%) that the population mean is above or below the sample mean.

Confidence interval – this is the range within which you are confident (e.g. 90%, 95%) the population mean lies

Here is how they are related:

$$\text{Margin of Error} = \text{Standard Error} \times Z$$ $$\text{Confidence Interval} = \text{Mean} \pm \text{Margin of Error}$$

Z is set by how confident you want to be: 1.645 for 90% and 1.96 for 95%.

What Is an Acceptable Response Rate

An acceptable survey response rate is one that allows you to use the information collected in the survey to make decisions in your business. That rate can vary dramatically.

Now that we have a handle on the basic statistical terms behind it, here’s what actually drives that variation.

While you could, for instance, make important business decisions with just 5 responses, and a very low response rate, using the “rule of 5”, most times you will need more responses so you can answer important questions:

How to Measure Anything by Douglas Hubbard

In customer feedback work you will most often want to, for instance:

• Decide if the average score has changed from one period to the next; or
• Decide if two attributes are significantly (in the statistical sense) different

In these cases the acceptable response rate will be the statistically valid survey response rate.

Here is how you calculate the number of responses required for a given level of certainty.

$$n = \left( \frac{Z \, \sigma}{E} \right)^2$$

Source: NIST/SEMATECH e-Handbook of Statistical Methods, § 7.2.2.2 Sample sizes required. The handbook writes it for 95% confidence, where Z is 1.96.

Don’t worry too much about the detailed maths as we have a downloadable tool you can use to do the calculation. What is important, is understanding what drives the number of responses you need.

Here:

• n is the minimum sample size needed
• Z is related to how confident you want to be in the answer, e.g. 90% or 95%
• σ is related to how much inherent variation there is in the population: does almost everyone give you a 5 or 6, or do you get a wide range of scores all the way from 1’s up to 7’s.
• E is the Margin of Error you want to be able to detect

So the acceptable survey response rate depends on those variables:

• If you want to be more confident you have detected a change, you will need a higher response rate
• To detect smaller changes, you will need a higher response rate
• If your population has more variation, you will need a higher response rate

As you can see, the number of responses is very important and that, along with the number of invitees, drives the minimum required response rate.

Calculating an Acceptable Survey Response Rate

To calculate an acceptable response rate: determine the minimum number of responses that keeps you inside the maximum error you’re willing to accept, then calculate the response rate required from your invite list. Standard Error is the idea doing the work here.

Said another way:

How many responses do we need, so we are confident the average is in the range of x +/- y.

We can do this with the equations above, just re-arranged a bit.

Here is a practical example where we want to be 95% certain that the average is in a range of +/- 0.5 on a 1-5 response scale.

For these requirements the minimum number of responses we need to be sure we are within that error range is 9.

If we have 100 invites the acceptable response rate is 9%.

If we have 200 invites the acceptable response rate is 4.5%.

How to Calculate Acceptable Response Rate

Note: This calculator makes some statistical assumptions, e.g. it ignores non-response bias, i.e. it assumes responses are evenly spread among the entire population. See below for more information.

Low Survey Response Rates Are Not Always Bad

If you have a very large population then you may have a very small response rate but a large number of actual responses.

Take for example political polling. These polls try to forecast the winners of elections by surveying a very small proportion of voters.

You can see in the image below that the response rates are very small, e.g. just 0.0011% (1,690/ 146,311,000) but the data is still useful.

Source – Example of Low Response Rates That Are Also Valid Response Rates

Note: strictly speaking this example doesn’t show response rates as not all voters were invited to respond. In this case I am using opinion polls to make the point that it’s not just the percentage of responses that are important but also the total number of responses

Sample Bias and Acceptable Response Rates

Definition of Survey Sample Bias

Survey sample bias refers to systematic errors in the sampling process that result in a sample that does not accurately represent the population being studied.

Survey Sample bias may be introduced in several ways in customer feedback surveys:

Non-Response Bias Examples

Not all customers having the same ability to respond, e.g. asking survey questions in a language the customer doesn’t understand.

Not all customers respond at the same rate, e.g. Typically, for customer surveys, the very happy and very unhappy respond more than the middle group who are neutral.

Selection Bias Examples

All customer not having an equal opportunity to be invited to respond, e.g. contact centre staff intentionally entering incorrect contact details for unhappy customers and thus preventing transactional surveys from being sent.

The Impact of Survey Sample Bias on Acceptable Response Rates

Sampling bias can have a major impact on acceptable response rates because it violates one of the basic assumptions upon which the underlying statistics are based.

That assumption that the people that respond have the same profile as the overall population of customers.

What to Do About Potential Sample Bias

In practice addressing and resolving sample bias is important but doing so is problematic as often there is no consistent and practical way to address the issue.

The only approach is to be aware of the issue and try to mitigate it in the delivery of invites and collection of response as much as possible.

Survey Response Rate FAQs

What is a good survey response rate?

A good survey response rate is one that is above average, which industry data puts at anything above 25%, and that carries enough total responses to support the decision you want to make. The second half matters more than the first. A 40% response rate from 20 invitations is still only 8 responses.

What is an acceptable response rate

What are the benefits of a higher survey response rate?

A higher response rate gives you more confidence that the feedback from a subset of customers represents how the whole customer base feels. Higher is always better, but the acceptable response rate for a given decision is often lower than people expect, so a low rate is not a reason to throw the data away.

How to increase survey response rates

How do I make sure my survey responses represent my customers?

Check that the profile of your responses matches the profile of the whole audience. If your customer base is 23% male and 58% of your responses are from men, the sample is not representative, whatever the response rate says. Random sampling techniques help, as does surveying a rolling share of the base rather than whoever answers.

What is the acceptable response rate for surveys?

An acceptable survey response rate is one that lets you use the results to make a decision in your business, so it varies with the decision. For a rough direction a handful of responses may do. To prove a two point change in a score, you may need most of the list.

Acceptable response rate calculator