Types of sampling for market research: A practical guide to choosing the right method

Good research starts with a good sample.

The group you choose to survey shapes everything that follows: how accurate your results are, how quickly you can field the study, and whether you’re hearing from the people who actually matter to your business. Sampling connects a small group of responses to the bigger audience you want to understand. Get it right, and you’ll save time, budget, and guesswork.

This guide walks you through the essentials of sampling design for marketing research: what it is, why it matters, and how to choose the right method for your goal. You’ll see a simple decision path, clear explanations, and real examples you can apply right away. Along the way, you’ll find tools that make planning easier, including the SurveyMonkey sample size calculator, margin of error calculator, and options to reach targeted audiences.

Start here to build a sampling plan you can stand behind and insights you can trust.

Sampling utilizes data from a small group, such as a simple random sample, and allows marketers to draw conclusions about a much larger target population.

Before you choose a method, align on three basics:

Why sample at all? A full census is rarely practical. It’s too slow, expensive, and hard to control for quality. Sampling lets you move quickly and still make reliable decisions. Say a regional coffee chain wants to vet a new roast. Instead of surveying every loyalty member, they can sample key segments by region or age, field in a week, and launch with confidence.

The various types of sampling methods will generally fall into one of two categories. The first category is random sampling while the second category is representative sampling.

A random sample is a sample of randomly selected individuals, designed to represent the population as a whole. Simple random samples can help companies and other organizations draw broad conclusions about people in general.

If a company is trying to sell a product that essentially everyone might use, such as toothpaste, a simple random sample can help them draw broad conclusions. What flavors of toothpaste do people typically prefer? When do people typically brush their teeth? What type of toothbrush do most people use? These are questions that can be effectively answered by asking a wide range of people for their opinion, rather than limiting the survey to a deliberately narrow group.

In contrast, researchers using representative sampling don’t want a random sample of all people. Instead, they want a random sample of people who are representative of a specific group. For example, if a company is selling a product that only some people use, such as skiing equipment, they’d want a sample of individuals who actually use that particular product.

Representative samples can be broken down in myriad different ways. In the example above, “people who ski” could be a distinctive group that helps filter the broader population. In other instances, you might consider breaking the population down by age, demographics, location, income, hobbies, profession, or other traits. As long as you can find enough survey takers to generate statistically significant conclusions, you will have a considerable amount of flexibility when creating a representative group.

There are two major types of sampling methods: probability and non-probability sampling. Before you choose a specific technique, it helps to understand how each one affects the accuracy and reliability of your results. 

Think of it this way: probability methods give you measurable confidence in how representative your results are; non-probability methods give you practical reach when representativeness is hard or unnecessary. Most real-world studies balance both.

Probability sampling is a type of sampling in which every single member of a group has a non-zero probability of being selected for the survey. Probability sampling can still exist within a filtered group (such as American adults), as long as every representative of this subgroup has a chance of being selected.

Here are the types of probability sampling methods.

What it is: Simple random sampling is a sampling method in which every person in your frame has an equal, known chance of selection. It’s like assigning each person a number and using a random number generator to pick the sample.

When it shines: Broad-appeal products or services where you want clean inference to the whole frame with minimal assumptions.

Cautions: Operational overhead. SRS requires a clean, deduplicated frame and consistent handling of nonresponses. 

Quick scenario: To evaluate employee satisfaction fairly, a company assigns a unique number to all 500 staff members and uses a random number generator to select 50 individuals for an anonymous survey.

What it is: Systematic sampling is a sampling method that starts from a random point in your list and then selects every nth person after that (for example, start at #37 and select every 200th name).

When it shines: When your list is large, organized, and free of hidden patterns, like customer IDs or employee rosters, and you need a quick, evenly spaced sample without the extra steps of full randomization.

Example: You export 200,000 loyalty members and invite every 200th ID to gather 1,000 invitations.

Caution: Avoid hidden patterns. If your list is sorted by a variable related to your outcome (like purchase frequency), it can unintentionally bias the results. Randomize the frame or stratify first to reduce risk.

What it is: Stratified random sampling is a sampling method that divides the frame into meaningful strata (like region, plan tier, or company size), then randomly samples within each group.

Why it’s powerful: Ensures representation in each stratum and lowers variance. You can often reach the same margin of error with fewer completions. Use our margin of error calculator to see how the error changes as n grows (hold confidence at 95% to compare apples to apples).

Example: A ski brand wants insight into helmet preferences across the West, Midwest, and Northeast markets. Create regional strata, set proportional targets, and draw at random within each stratum.

Cautions: Strata must be clearly defined and mutually exclusive to avoid overlap. Misclassifying respondents or using uneven strata sizes can skew results. Keep sampling random within each group and document how strata were set.

What it is: Cluster sampling is a sampling method in which you randomly select groups called clusters, such as stores, schools, or cities, and then survey people within those groups.

When it helps: Multi-market tests where travel or recruitment cost is high. 

Example: A retailer pilots a new display in 30 randomly chosen stores and surveys shoppers before and after.

Cautions: Clusters tend to be internally similar. Plan for larger sample sizes or more clusters to offset the design effect and preserve precision.

Multistage sampling

What it is: Multistage sampling is a sampling method that selects samples in multiple steps: first choosing clusters, then sampling individuals within them. It combines elements of cluster and simple random sampling to make large-scale studies more manageable.

When it helps: Ideal for large or geographically dispersed populations where listing every individual is impractical. Common in national or multi-regional surveys that require efficient, scalable fieldwork.

Example: A national research team first selects a random set of school districts (stage one), then randomly samples schools within those districts (stage two), and finally surveys teachers within each selected school.

Cautions: Each stage introduces potential sampling error. Keep randomization consistent at every level, and document how selections were made to maintain transparency and replicability.

While probability sampling can be used to draw conclusions from random (though sometimes slightly modified) groups, non-probability sampling uses groups that are a bit more deliberately structured. 

Non-probability sampling can help reduce random biases and, in many instances, ensure that key portions of a broader population are included within the sampled population. 

There are five primary types of non-probability sampling methods.

What it is: Quota sampling is a sampling method in which the researcher sets demographic or behavioral targets—like 30% ages 18–34, 40% ages 35–54, and 30% ages 55+—and recruits participants until each target group is filled.

When it helps: Quick brand trackers or concept tests where you need directional reads that mirror your market’s top-line demographics.

Example: A streaming service sets age and region quotas to test a new ad campaign, ensuring each group is proportionally represented.

Cautions: Monitor incidence rates, use attention checks, and apply weighting to match targets. Document your quotas and note any limits on generalizability.

What it is: Convenience sampling is a sampling method that recruits from easy-to-reach sources, like intercepts, website pop-ups, email lists, or social followers.

When it helps: Early-stage pilots, in-product feedback, or quick temperature checks before committing to a larger market research study.

Example: A café chain posts a survey link on its loyalty app to get fast feedback on new menu items.

Cautions: High risk of selection bias—your most reachable customers may differ from the rest. Screen carefully, randomize invitation timing, and, when possible, weight results to external benchmarks.

What it is: Snowball sampling is a sampling method in which qualified respondents refer others who also meet your criteria.

When it helps: Reaching hidden, sensitive, or niche groups—like early adopters in a micro-community or professionals in rare roles.

Example: A researcher studying biotech founders asks current participants to invite peers from their network.

Cautions: Network homophily (people recruiting those like themselves) can skew results. Limit referrals, validate eligibility, and pair with qualitative methods for context. Always handle consent and privacy thoughtfully.

What it is: Purposive sampling involves is a sampling method in which researchers intentionally recruit participants with specific expertise or characteristics because their input is uniquely valuable.

When it helps: Expert interviews, B2B concept tests, or other studies where specialized knowledge matters more than representativeness.

Example: Interviewing senior procurement leaders in aerospace to evaluate a new vendor-management workflow for mid-market firms.

Cautions: Define inclusion criteria up front, confirm expertise (for example, role tenure or decision authority), and avoid over-generalizing findings beyond the defined group.

What it is: Opt-in sampling is a method that relies on volunteers who proactively join research panels or sign up for surveys, often incentivized by rewards.

When it helps: It is a highly efficient way to quickly reach large groups or specific niches.

Example: A news website posts a poll on its homepage asking readers to share their opinions on a new tax law, allowing anyone who visits the site to voluntarily participate.

Cautions: Because participants self-select, the results may be biased toward certain demographics or "professional respondents."

When it comes to survey precision, three numbers do most of the work:

As your sample size grows, your margin of error shrinks, but not in a straight line. Doubling your responses doesn’t cut the margin of error in half.

Example: At a 95% confidence level, if 30% of respondents say they’d “definitely buy,”

Bigger samples give tighter estimates, but after a point, you’re spending more for only slightly more precision. The key is to find a sample size that’s big enough to trust and small enough to field efficiently.

Use our sample size calculator to back into n for your target MOE, then verify with the margin of error calculator.

Survey sampling with a market research panel, like SurveyMonkey Audience or our integrated global panel, makes it easy to reach verified, high-quality respondents fast. Panels give you control over who you ask, the questions you pose, and how results are segmented across demographics, roles, or regions.

You can build studies around countless dimensions: age, geography, industry, job title, company size, and more. These panels power insights across everything from market sizing and product testing to brand tracking and consumer behavior. By using a trusted panel, you get reliable data from real people and a clearer view of your broader market.

Each sampling method has trade-offs. A simple random sample minimizes bias and supports broad conclusions, but it can be slow or costly. Convenience or quota sampling moves faster, but you’ll need to watch for overrepresented groups. The right method depends on your goals, your audience, and your timeline.

Start by clarifying what you want to learn and who you need to hear from. Then factor in time, budget, and accessibility. With planning and the right tools, you can choose a sampling method that fits your project and your precision needs.

To make it easier, use our sample size calculator, margin of error calculator, and survey templates to design a study you can stand behind. When you’re ready to reach verified respondents, tap into SurveyMonkey Audience, a network of more than 80 million people ready to share feedback that helps you make confident, data-driven decisions.