User interview screener template for B2B SaaS: 5 questions

A useful user interview screener answers one question: can this person speak from recent, relevant experience about the decision you need to make?

For B2B SaaS research, do not ask customers to re-enter facts you already hold. Use product or account data for the coarse filter, then use a short form for what the data cannot tell you: the person’s role, recent experience, current workflow, and ability to describe a concrete event.

The five-question template below is a practical starting point, not a scientifically proven optimum. If your data already answers one question, delete it. If the study needs an essential criterion that the template misses, replace a question rather than making the form longer by default.

Copy this five-question screener

Start with an honest introduction:

We’re speaking with people who handle [broad activity] to understand how that work happens today. This form takes about two minutes. We’ll use your answers to select a small number of interview participants. Taking part is voluntary. [Explain the interview format, timing, incentive, data use, and link to your privacy information.]

Then adapt these questions to the study:

Do not score question 5 on polished writing. A short or misspelled answer may still come from exactly the user you need to understand. Use it to check relevance and prepare a follow-up. Only treat communication format as an inclusion criterion when the research method genuinely requires it, and offer an accessible alternative where possible.

Step 1: define the decision before the participant

A screener is a filter. You cannot design the filter until you know what evidence the decision requires.

Write these four lines before writing any questions:

• Decision: What will this research inform?
• Target experience: What must someone have done, attempted, bought, abandoned, or supported?
• Must-have criteria: Which facts are essential for a useful interview?
• Variation targets: Which different situations should appear across the interview set?

For example:

• Decision: Improve first-team setup.
• Target experience: Started setting up an account in the past 60 days.
• Must-have criterion: Personally handled or closely observed setup.
• Variation targets: Completed setup quickly, got stuck but returned, and stopped before inviting a teammate.

This is more useful than “operations managers at companies with 50–200 employees.” Company size may matter, but only if it changes the experience or decision you are studying.

Nielsen Norman Group recommends defining inclusion and exclusion criteria around the research goal and focusing on attributes that affect the research question. The UK Government Service Manual similarly recommends starting with the types of people and experiences the study needs, using existing service data where it helps.

Step 2: use product data before the form

If you are interviewing your own customers, your product and CRM may already tell you:

• signup, activation, renewal, or cancellation date;
• plan and account size;
• role or permissions, when those are reliably recorded;
• use of a workflow related to the research question;
• support contact or a recorded failure event.

Use only the fields needed for the study. Do not turn a research screener into a quiet exercise in profiling customers.

Before using account or behavioural data for recruitment, confirm that the use fits your privacy notice, permissions, and internal access rules. During recruitment, explain what participant data you will collect and how you will use it. Government Digital Service guidance recommends collecting the minimum needed, limiting access, and deleting recruitment data when it is no longer required. Sensitive screener answers need particular care.

Product data is also fallible. A “buyer” field may be stale. A feature event may have been triggered by a teammate. Treat the data as a filter and interview prompt, not as a perfect account of what happened.

Step 3: select for relevance and variation

Qualitative interviews are not a small opinion poll. They help you understand experiences, mechanisms, language, workarounds, and exceptions. They do not tell you what percentage of the whole customer base behaves a certain way.

Teresa Torres recommends interviewing for variation rather than statistical representativeness: speak with active and disengaged users, first-time and experienced users, or customers in other situations relevant to the outcome. That does not mean “talk to anyone.” Participants must still be actual or plausible users with relevant experience.

Build a simple selection grid before invitations go out:

The rows above are hypothetical. The point is to make selection visible. When all selected rows say “enthusiastic admin at an active account,” you can see the gap before the interviews begin.

One person per company is a useful default when you want variation across accounts, but it is not a law. A multi-role B2B study may deliberately include an admin, end user, and buyer from the same customer. If you do that, label the shared account and do not treat their accounts as three independent contexts.

Step 4: write questions that are hard to game

Good screener questions ask about observable history without advertising the qualifying answer.

Ask about past behaviour, not future intent

Avoid:

Would you use an automated reporting feature?

Ask:

When did you last prepare this report, and what did you use?

NN/g warns that people are unreliable at predicting future behaviour and recommends screening on relevant past behaviour instead. The same discipline should continue in the interview: Product Talk recommends collecting specific stories about past behaviour rather than relying on general claims.

Avoid yes-or-no gates

“Do you use dashboards?” reveals that “yes” probably qualifies. A list of plausible workflows makes the desired answer less obvious and gives you more useful context. Include “Other” and “None of these” when they are honest possibilities.

Keep answer ranges mutually exclusive

Do not offer “1–5” and “5–10.” Do not use vague labels such as often unless the study is about the participant’s own interpretation of frequency. Use dates or non-overlapping ranges.

Disclose the study without revealing the desired answer

You should not tell applicants, “We need people who failed at onboarding after importing data.” That makes the route into the study obvious. But hiding the sponsor, data use, recording, or broad purpose is not a neutral screener; it prevents informed participation.

Tell people who is conducting the research, the broad topic, what participation involves, how their information will be used, and whether there is an incentive. Keep the exact hypothesis and qualifying logic out of the form.

Pilot the form

Send the screener to two or three colleagues who were not involved in writing it. Ask them what they think qualifies, which words are ambiguous, and whether the logic can produce impossible combinations. NN/g recommends piloting screeners because a small test catches confusing wording and skip-logic errors before recruitment starts.

For more examples of neutral wording, use Maren’s guide to avoiding leading questions.

Step 5: audit self-selection

The screener can filter respondents. It cannot correct for everyone who ignored the invitation.

People who volunteer can differ from the population you hoped to study. The Catalog of Bias describes differences in participant characteristics and attitudes as a source of selection bias. In customer research, that can leave you hearing mostly from advocates, critics, or people with unusually flexible schedules.

Keep three counts by the segments that matter:

1. invited;
2. responded;
3. selected and completed.

If disengaged trial users are 40% of the invitation list and none complete the screener, that absence does not tell you why they disengaged. It tells you the recruitment route did not reach them. Try a different invitation, channel, format, time commitment, or incentive. Do not replace them with easier participants and keep the same label on the study.

The UK Government Service Manual also notes that recruitment choices—channel, timing, location, and activity—include some people and exclude others. Use more than one approach when the decision needs perspectives your first route misses.

B2B details that change the template

Separate user, buyer, and administrator roles

In B2B SaaS, “customer” can mean the person who bought the contract, configured the workspace, uses the product daily, or approves renewal. Those people can give accurate but different accounts. Name the role you need, or recruit several roles and analyse them separately.

Do not make accessibility a disqualifier

Ask about access or communication needs separately from the qualification logic. A form that only works with fine motor control, a scheduled video call, or fluent written English can remove relevant users before the research begins. Offer another response format when practical and record which groups the method could not reach.

Verify identity in proportion to risk

For research with your own customers, a matching account, work email, and relevant product event may be enough. A public, highly paid study may need more validation. No single LinkedIn profile, technical answer, or writing style proves identity. Use several signals, minimise the personal data collected, and manually review unusual cases.

Set the incentive before you read the answer

Compensate time and inconvenience, not a favourable opinion. State the incentive and eligibility rules before the interview. Follow the participant’s employer gift policy where relevant. There is no universal B2B rate: seniority, scarcity, location, duration, and recruitment channel all change the appropriate amount.

Worked example: stalled trial accounts

Suppose the decision is whether to change a team-invitation step. This example is fictional.

Product data identifies 36 recent trial accounts:

• 12 invited teammates in the first week;
• 15 returned several times but invited nobody;
• 9 stopped after the first session.

You want recent experience from all three groups. Your form asks:

1. What was your role in setting up the workspace?
2. When did you last work on the setup?
3. How does your team currently share this work?
4. How many people were expected to use the workspace?
5. Tell us briefly about the last step you completed or attempted.

The screener does not ask, “Was inviting teammates confusing?” That is the hypothesis, not a neutral qualifying question.

Select people across the three behaviour groups and roles. Start the interview from the event they described: “You said you stopped after connecting the data source. Walk me through what happened next.” A grand tour question can then map the wider setup process before you zoom into the invitation step.

If you need the broader recruitment workflow, see how to recruit participants for user interviews. For a study tied specifically to activation, the trial-conversion interview template shows how to move from selection to the discussion guide.

Where Maren fits

Maren helps after you have defined and selected the audience. You set the objective and review the discussion guide, then share an interview link or invite selected participants by email. Maren sends reminders, runs adaptive text interviews, retains transcripts, produces per-interview summaries, and synthesises findings across the project.

Maren does not provide a participant panel or decide which customers qualify for the study. A public share link is useful when broad access is intentional; email invitations give you tighter control over who receives a participant-specific route. You still own the recruitment criteria, privacy basis, selection grid, and interpretation of who did not respond.

That boundary is useful. Use the screener to make sure the right experiences enter the study. Use Maren to ask follow-up questions consistently and turn those conversations into evidence you can inspect.

A final screener checklist

Before sending the form, check that:

• every must-have criterion maps to the research decision;
• product data answers the questions it can answer reliably;
• each form question has a clear selection use;
• answer options are neutral, non-overlapping, and include an honest exit;
• the qualifying answer is not obvious;
• the sponsor, broad purpose, logistics, incentive, and data use are clear;
• access needs are supported rather than screened out;
• selected participants vary on the situations that matter;
• shared accounts and roles are labelled;
• invitation, response, selection, and completion gaps will be recorded;
• the form has been piloted.

A short screener cannot make a qualitative sample statistically representative. It can do something more practical: give every interview a clear reason to be in the study and make the limits of the evidence visible.