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Advanced Features

Conditional logic, screening rules, AI follow-ups, and dynamic content

Updated today

Enhance your studies with dynamic logic, intelligent probing, and personalized content.

Conditional Logic

Show or hide questions based on previous answers.

Example

If participant selects "Yes" to "Do you own a car?" → Show follow-up questions about car usage

If participant selects "No" → Skip car-related questions

Setting Up Conditions

  1. Select the question you want to control

  2. Open condition settings

  3. Define the rule:

    • Source question - Which question triggers it

    • Operator - Equals, not equals, is one of, greater than, etc.

    • Value - The answer that triggers the action

  4. Save changes

Available Operators

Operator

Use Case

Equals

Exact match

Not equals

Any option except specified

Is one of

Any of multiple options

Is not one of

None of multiple options

Greater than

Numeric above threshold

Less than

Numeric below threshold

Is empty

Question was skipped

Is not empty

Any answer provided

Common Patterns

Follow-up questions:

Q1: Do you use our app?
  - Yes → Show "How often?"
  - No → Skip to next section

Satisfaction follow-up:

Q1: Rate your satisfaction (1-5)
  - If 1 or 2 → "What disappointed you?"
  - If 4 or 5 → "What do you like most?"

Multi-select branching:

Q1: Which products do you use? (Select all)
  - If "Mobile app" selected → Show mobile-specific questions
  - If "Website" selected → Show website-specific questions

Combining Conditions

Create complex logic with AND/OR groups:

  • All conditions (AND) - Every condition must be true

  • Any condition (OR) - At least one condition must be true

Example: Show a question if participant is age 25-34 AND uses the product daily.

Testing Logic

⚠️ Warning: Always test conditional logic before launching. Test ALL paths, not just the main one.

See Survey Validation for common conditional logic errors.

Screening Rules

Automatically terminate participants who don't qualify.

How It Works

Screening rules determine if participants continue. If they fail, they're redirected to a "thank you" page and marked as "screened out."

Screening vs Conditional Logic

Screening

Conditional Logic

Terminates unqualified participants

Shows/hides questions

Ends survey immediately

Continues survey differently

Used early in survey

Used throughout survey

Common Screening Criteria

Demographics:

  • Age under 18 → Screen out

  • Location outside target markets → Screen out

  • Gender not matching quota → Screen out

Behavioral:

  • Never used product category → Screen out

  • Works in market research → Screen out (avoid professionals)

  • Purchased competitor recently → Screen out or keep (depends on goal)

Best Practices

💡 Tip: Screen early. Don't waste participants' time with 10+ questions before screening.

💡 Tip: Hide the "right" answer. Instead of "We need smartphone owners. Do you own one?" ask "Which devices do you own? (Select all)"

Screening Rate Guidelines

Rate

Indication

0-10%

Very permissive

10-30%

Normal for targeted studies

30-50%

Narrow criteria

50%+

Very restrictive—may be hard to recruit

AI Follow-Up Questions

Follow-up Badge

Automatically generate intelligent probing questions based on responses. Questions with follow-ups are marked with a badge showing the number of follow-ups.

How It Works

  1. Participant answers a question (usually open-ended or video)

  2. AI analyzes the response in real-time

  3. AI generates a relevant follow-up question

  4. Participant elaborates on specific points

Example

Original question: "What do you like most about this product?"

Response: "I love how easy it is to use, especially the quick setup."

AI follow-up: "You mentioned the quick setup. What specifically made it feel quick and easy?"

Configuration Options

Number of follow-ups:

Setting

Use Case

1 follow-up

Light probing, shorter surveys

2 follow-ups

Standard depth

3+ follow-ups

Deep exploration

Custom instructions: Guide the AI on how to probe:

  • "Focus on emotional reactions"

  • "Ask for specific examples"

  • "Explore comparison with competitors"

  • "Probe for unmet needs"

Best Practices

💡 Tip: Enable follow-ups on your most important qualitative questions—not every question.

💡 Tip: Write good initial questions. Open, exploratory questions give the AI more to work with.

What AI Probes For

Topic

Example Follow-Up

Clarification

"What do you mean by 'confusing'?"

Examples

"Can you give me a specific example?"

Reasons

"Why do you feel that way?"

Comparisons

"How does this compare to alternatives?"

Emotions

"How did that make you feel?"

Variable Text

Insert dynamic content into question text based on previous answers.

How It Works

Use #{{question_id}} syntax to reference a previous answer:

Q1: What brand of coffee do you usually buy?
Q2: What do you like most about #{{Q1}}?

If the participant answered "Starbucks" to Q1, they'll see:

"What do you like most about Starbucks?"

Setting Up Variables

  1. Write your question text

  2. Click the variable inserter or type #{{

  3. Select the source question

  4. The question ID is inserted automatically

Supported Variable Sources

Variables can reference:

  • Multiple choice answers - The selected option text

  • Open text responses - The typed text

  • Numeric values - The entered number

Tips

💡 Tip: Variables always reference earlier questions. You cannot reference questions that come after.

⚠️ Warning: Consider what happens if the source question was skipped or had an unexpected answer.

Option Piping

Reuse options from one question in another without duplicating them.

Use Cases

Follow-up on selections:

Q1: Which brands do you know? (Select all)
  - Brand A, Brand B, Brand C, Brand DQ2: [Pipes selected options from Q1]
Which of these is your favorite?
  - (Only shows brands selected in Q1)

Matrix from selection:

Q1: Which features do you use? (Select all)Q2: [Pipes selected options as matrix rows]
Rate your satisfaction with each feature:
  | Feature | Very Dissatisfied | ... | Very Satisfied |

Setting Up Option Piping

  1. Create your source question (must come first)

  2. Create the follow-up question

  3. In "Use options from," select the source question

  4. For matrix questions, select which column to pipe

Column Filtering

When piping from a matrix, you can filter which rows to include:

  • Pipe only rows rated "Satisfied" or above

  • Pipe only rows the participant selected

Randomization

Reduce order bias by randomizing option presentation.

What Can Be Randomized

  • Multiple choice options - Shuffle answer choices

  • Ranking options - Randomize initial order

  • Matrix rows - Shuffle items being rated

Enabling Randomization

  1. Select the question

  2. Enable "Randomize options" in settings

  3. Options will be shuffled for each participant

When to Use

  • Yes: Testing multiple concepts/brands fairly

  • Yes: Long option lists where position might affect selection

  • No: Ordered scales (Strongly disagree → Strongly agree)

  • No: Questions where order provides context

Related Pages

  • Question Types - All question formats and configurations

  • Stimulus Materials - Adding images and videos

  • Survey Validation - Pre-publish validation checks

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