Qualt

AI-facilitated qual at quant scale

Deep, probing conversations with every respondent, plus the structured data to size what you hear.

Talk to us about Qualt See how it works
Sample interview from a project with 1,200 participants. Illustrative example
You mentioned you almost switched banks last year. What made you consider it?
Honestly the app. Paying someone back took way too many steps.
Which steps felt like too many? Walk me through the last time.
How likely are you to switch in the next 12 months? Sample rating question: the respondent selected 4 on a 5-point scale.

Open-ended probing and structured questions in the same conversation.

What Qualt is

An AI interviewer holds an adaptive conversation with each respondent, following up on vague answers and exploring what matters to them. Structured questions capture data you can count and cut.

Interviews run in parallel, so a study can reach hundreds or thousands of people in days. Sibilance researchers design the guide, oversee quality, and interpret the results.

Questions this answers

  • Why do customers choose, switch, or leave, in their own words?
  • How widespread is each theme, and who holds it?
  • What language should our messaging use?
  • How do people react to a new concept, and why?
  • What should go into the segmentation or conjoint that follows?

How we run it

One team, end to end. The Sibilance researchers who design your guide also run the fieldwork, review the coding, and present the findings, then carry them straight into any segmentation, MaxDiff, or conjoint that follows. No hand-offs between vendors.

  1. Design the guide Discussion topics and embedded quant questions, together.
  2. Configure and pilot Probing rules, tone, and guardrails, tested before launch.
  3. Field at scale With fraud, attention, and response-quality checks.
  4. Code the themes AI-assisted coding, with researchers reviewing the codeframe.
  5. Size and segment Count each theme and cut it by the structured data.
  6. Report Numbers and verbatims side by side, with recommendations.

What you get

  • A theme framework with prevalence
  • Differences by segment
  • A searchable verbatim library
  • Quantitative results from embedded questions
  • Inputs for segmentation, MaxDiff, or conjoint

Our techniques

Adaptive AI moderation
Follow-up questions tailored to each answer.
Embedded quant
Scales, rankings, and choice questions inside the conversation.
Concept and stimulus reactions
Show concepts, then probe reactions in depth.
AI-assisted thematic coding with human review
Consistent coding at scale, checked by researchers.
Theme sizing and segment cuts
How many people hold each view, and who they are.
Why customers consider switching Illustrative data
Theme prevalence Share of respondents mentioning each theme: app friction 42 percent, fees 35 percent, rewards 27 percent, customer service 22 percent, branch access 12 percent, security worries 9 percent. App friction42% Fees35% Rewards27% Customer service22% Branch access12% Security worries9% Theme prevalence Share of respondents mentioning each theme: app friction 42 percent, fees 35 percent, rewards 27 percent, customer service 22 percent, branch access 12 percent, security worries 9 percent. App friction42% Fees35% Rewards27% Customer service22% Branch access12% Security worries9%

Text summary of the chart: app friction is mentioned by 42% of respondents, fees by 35%, rewards by 27%, customer service by 22%, branch access by 12%, and security worries by 9%. Figures are invented for illustration.

“Paying someone back took way too many steps.” App friction
“I only noticed the monthly fee when I checked my statement.” Fees

How a Qualt study fits

An illustrated example of a typical engagement. This is an invented scenario, not a client project.

Challenge

A retail bank sees current-account switching rising among under-35s and its existing tracker says only that “app experience” scores poorly.

Approach

AI-facilitated interviews with 1,200 recent switchers and stayers across four markets, with embedded switching-intent and product-holding questions.

Result

Six themes sized and cut by segment, the language customers actually use for each, and a prioritized attribute list that fed the conjoint that followed.

6 themes sized and cut by segment, in this illustrative example

Illustrative example. Not based on a specific client engagement.

Questions we hear

Not the right fit?

For in-person observation, shop-alongs, or live co-creation, we run traditional qual.

Works well with

Segmentation & personas Richer inputs, and personas with a real voice.
Conjoint Attributes grounded in customer language.
Does this replace human researchers?

No. Researchers design, oversee, and interpret every study. The AI conducts the interviews and speeds up coding.

How big can a Qualt study be?

From around 100 to several thousand respondents.

How do you ensure quality?

Attention and fraud checks, response-quality scoring, and researcher review of probing and coding.

How is respondent data protected?

Interviews are collected under our standard data handling and privacy practices. Respondents are told at the start that they are speaking with an AI interviewer and how their responses will be used. Transcripts are stored securely, access is limited to the research team, and findings are reported in aggregate. Directly identifying details are removed from the verbatim library before it is shared, and personal data is retained only as long as the study requires.

Which languages do you support?

Most languages. We confirm the specific languages and dialects for your markets during design, and pilot each one before fielding.

Talk to us about Qualt

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