SurveyMonkey Audience is a feature built into the broader SurveyMonkey platform that allows you to access a diverse pool of survey respondents based on specific targeting criteria. It's designed to make market research faster and more affordable by connecting you directly with real people who fit the demographic and behavioral profiles you specify.

Unlike open surveys – which rely on sharing links across your network or email lists – SurveyMonkey Audience lets you deliver your survey to people outside your existing customer base. It’s especially useful for reaching niche or unfamiliar audiences, testing new markets, or exploring early customer hypotheses.

What makes it unique?

One of the platform's biggest advantages is its ability to automate survey targeting. With just a few clicks, you can choose from filters like age, gender, location, employment status, household income, and more. The platform then recruits responses in real-time, helping projects stay on track even under short timelines.

The process at a glance:

• Define your survey goals – Are you exploring product preferences, brand perception, or customer needs?
• Select targeting criteria – Choose from built-in demographic filters to define your desired audience.
• Check feasibility – The platform estimates how many people meet your criteria and how quickly responses can be collected.
• Launch your survey – Responses begin rolling in from qualified participants selected for you by the platform.

It’s a self-serve tool designed for speed, but it's not a substitute for thoughtful audience planning. Many teams start strong with the right intent but run into challenges like low survey completion rates or misleading results due to over-narrow targeting or poorly designed screeners.

This is where survey feasibility and accurate recruitment become critical. Just because a tool can reach a population doesn’t always mean it should – or that it will do so efficiently. Without insight into audience availability and incidence rates (the percentage of reachable participants who qualify), you risk fielding delays, cost overruns, or skewed data.

SurveyMonkey Audience is powerful, but like any DIY survey platform, its success depends on smart setup. Partnering with experienced consumer insights professionals – like SIVO’s On Demand Talent – can help you make the most of the platform’s reach by avoiding common missteps and keeping your survey aligned with your strategic objectives.

Demographic filters are at the heart of any effective survey targeting strategy. In SurveyMonkey Audience, they're how you direct your survey to specific groups – helping ensure you're speaking to the right people at the right time.

But not every filter adds value equally. In fact, over-filtering can be just as problematic as under-targeting. Understanding how to filter by demographics in SurveyMonkey – and when to rely on survey screeners instead – is essential to getting usable insights without unnecessary incidence loss or added costs.

Common demographic filters available include:

• Age and age ranges
• Gender identity
• Geographic location (country, state, region)
• Household income
• Education level
• Employment status
• Ethnicity

These filters can be layered to align closely with your target market – for example, women ages 35–54 living in the Midwest with a household income of $75K or more. That might be great for a product concept test, but every added layer also reduces pool size and affects survey feasibility.

Best practices for effective filtering:

1. Start broad, then refine in screeners – One of the best practices for survey screeners is to use demographic filters only for the basics (like age or region) and qualify further within the survey using screening questions. This lets you access a broader pool while still filtering out respondents who don't meet your behavioral or attitudinal criteria.

2. Have a feasibility mindset – SurveyMonkey provides real-time estimates of how many people match your filter set. If your desired audience is too narrow or niche, the tool might not estimate enough completions. In these cases, loosening one or two filters or rethinking respondent definitions can improve outcomes.

3. Test before launching – Running a soft launch with a partial sample gives you early indicators of quality and feasibility. If completion rates or screen-out rates are unexpectedly high, you’ll have the chance to adjust before going full scale.

It’s also worth noting that identifying the *right* demographic filters often calls for deep knowledge of your audience. When teams lack internal expertise or face staffing limitations, working with seasoned professionals through a flexible solution like SIVO’s On Demand Talent can prevent costly errors in planning. These experts can help you align targeting with your research objectives and ensure you’re not excluding (or misrepresenting) the audience segments that matter most.

Targeting the right people with online surveys may sound straightforward, but it often requires balancing precision with reach. Thoughtful filter selection – backed by strategy and experience – makes sure your sample reflects your goals, not just your best guess.

Once you've selected your demographic filters and outlined your research goals, the next step is checking survey feasibility. This step ensures that the audience you're targeting is actually reachable within your timeline and budget. Using a DIY survey platform like SurveyMonkey Audience gives you access to thousands of respondents – but that doesn’t mean every audience is equally easy to reach.

What is a feasibility check in SurveyMonkey Audience?

SurveyMonkey Audience offers a built-in tool to estimate available sample size based on your targeting criteria. This lets you preview how many people match your selected demographics and how many completes you can realistically expect. Before launching your survey, it’s good practice to:

• Run a mock test to estimate sample availability
• Adjust demographic or behavioral filters based on incidence rate
• Check estimated cost per completed survey

This early insight helps you avoid launching a survey only to realize too few qualified respondents are available – a common issue in market research recruitment that costs time and resources.

Factors that can affect feasibility

Several criteria may impact whether your ideal survey audience is feasible to recruit. These include:

1. Very specific demographics: The more niche your criteria (e.g. Gen Z brand managers who also own electric vehicles), the harder it may be to fulfill your sample needs.

2. Length and complexity of survey: Longer or confusing surveys can result in drop-offs or failed completions, especially if incentives don’t match the effort.

3. Timing and volume: Surveys with quick turnaround times or very high quotas are harder to meet unless your criteria are broad.

How to address feasibility issues

If feasibility looks low in SurveyMonkey Audience, consider relaxing certain filters. For example, instead of targeting only U.S. consumers aged 25–35 who drink oat milk daily, you could expand to 25–40 or reduce the frequency to weekly users. Even small changes in audience planning can significantly improve feasibility and reduce costs per complete.

Another solution is pre-launch rebalancing – designing two or more sample groups that help you meet your core research objectives while staying within reach of available respondents.

Feasibility checking helps ensure your research isn't just smartly designed, but also highly practical. Without this step, even the most carefully crafted study risks underperforming.

Even if your audience targeting is spot on, your survey won’t succeed without well-written screeners. Screeners are the qualifying questions at the beginning of a survey used to identify whether someone fits your required respondent profile. Done right, they sharpen your insights. Done wrong, they can lead to high incidence loss – when too few people qualify – or worse, inaccurate data from respondents who don’t belong.

What is incidence loss?

Incidence loss refers to dropping too many respondents during the screener phase. Not only does it slow down your study, but it increases costs and extends fielding time. High incidence loss usually means your screener is unnecessarily strict or confusing.

Best practices for effective screener design

Here are some tips to write screeners that protect your data integrity while avoiding unnecessary drop-off:

• Be clear and direct: Use simple, jargon-free language that a wide audience can understand.
• Avoid double-barreled questions: Don’t combine criteria like “Do you shop at Walmart and Target weekly?” – split them into separate items.
• Include non-leading response options: Don’t give away the ‘right’ answer. Keep options balanced so only truly qualified respondents are selected.
• Use behavioral indicators over opinions: Instead of asking “Are you a health-conscious eater?”, ask about specific behaviors (e.g. “How many times did you purchase plant-based products in the past month?”).

Example scenario (fictional): You're researching pet food preferences among dog owners. A poor screener might ask: “Do you care deeply about pet nutrition?” – which is likely to get a ‘yes’ from almost everyone. A better screener asks: “Which of the following types of pets do you currently own?” and follows up with “How often do you research pet food ingredients before purchase?”

Testing your screeners

Before launching, always test screeners with a small internal group or do a soft launch to detect confusing wording or qualifying problems early. Tracking early incidence rates can help you adjust quickly to improve performance.

Solid screener questions ensure you're targeting the right people with online surveys – so your data reflects real consumer behaviors and preferences, not just hopeful assumptions.

DIY survey platforms like SurveyMonkey Audience have made it easier and faster than ever to gather feedback – but ease of use doesn’t guarantee quality results. Behind every meaningful insight is thoughtful design, rigorous planning, and experienced execution. That’s where expert support makes the difference.

Why DIY doesn't mean going it alone

Today’s businesses face growing pressure to move faster and do more with fewer resources. DIY tools are a smart way to scale research, but their value depends on how well they’re used. Without deep audience planning, sharp screeners, or survey expertise, these platforms can produce data that looks good on screen – but lacks depth or direction.

That’s where solutions like SIVO's On Demand Talent come in. These are experienced insights professionals who can step in quickly to:

• Guide demographic targeting based on business goals
• Design surveys that collect answers, not guesswork
• Assess feasibility and minimize costly cycle delays
• Interpret and activate results across your org – not just deliver charts

Unlike freelancers or general consultants, our On Demand Talent professionals are hand-selected for their market research skillsets and leadership thinking. Whether you’re a lean team, testing new tools, or need extra bandwidth temporarily, these experts can step in and make sure your survey recruitment delivers strategic impact – not just surface metrics.

Building internal capability through external support

What sets On Demand Talent apart isn’t just the quality of execution – it’s the knowledge transfer. Our experts don’t just fill a gap. They help your team deepen its understanding of platforms like SurveyMonkey Audience so you can maximize the value of your digital research toolkit over time.

As AI and online survey tools reshape the research landscape, having flexible, embedded talent to guide experimentation and apply best practices makes all the difference between running surveys – and running smart surveys that drive business growth.

Need to pivot quickly or upskill your team? On Demand Talent offers fast access to fractional support, with zero long-term hiring commitments. It's what modern research agility looks like.