Product · Beta
Ambient Qual — interviews at the moment of decision
Ambient Qual drops Sofi into your site with a single script tag. When a shopper tries to leave checkout, lands from a paid ad, or finishes a purchase, Sofi can interview them right there—no calendar invite, no panel recruit. You hear the reasoning at the exact moment it happens, not reconstructed weeks later.
What does in-flow qualitative actually mean?
Traditional qualitative programmes are project-shaped: scope, recruit, schedule, interview, analyse. That rhythm is powerful for big questions, but it leaves quiet weeks while shopper behaviour keeps moving.
Ambient Qual means interviews fire from what shoppers do on your site, so learning continues between those larger projects. Growth and merchandising teams hear language and reasoning in context; research teams still own methodology, consent, and study quality.
How does implementation work?
- Create a project in Fieldwork and connect it to one or more live studies.
- Define resolver rules that map traits or events to the right study.
- Drop the loader script onto your site with a publishable key and a trigger (
exit-intent,time, orcustom). - Optionally set
window.FieldworkTraitsto pass the shopper id and traits. - Transcripts, themes, and topic coverage land in your Fieldwork workspace; webhooks can forward outcomes to your data stack.
How do resolver rules route participants to studies?
Resolver rules map incoming signals—event name, cart value band, lifecycle stage—to the study that should run. When multiple rules match, priority ordering determines which study fires first.
Frequency caps and cooldown windows protect the shopper experience. We default to a seven-day cooldown per visitor so nobody gets interviewed twice in a week.
What is Ambient Qual not meant to replace?
It does not replace moderated sessions when you need deep exploration, sensitive facilitation, or relationship-heavy contexts. It complements analytics by adding why, not by substituting for behavioural measurement.
It is strongest for cart abandonment, post-purchase depth, ad-landing expectation checks, and adoption mysteries—places where timing and context matter as much as the questions you ask.
What does an in-flow interview look like in practice?
A DTC cookware brand wants to understand checkout abandonment: what stopped the shopper, whether shipping framing was the issue, and what would have made the order worth finishing. The loader fires an interview when exit-intent hits the cart page.
The brief
"Understand what shoppers were trying to accomplish at checkout, what stopped them from completing, and what framing would have tipped them into the order."
Example conversation · not live data
In-flow prompt
Looks like this cart is not quite ready—got 90 seconds to tell us what is making you hesitate?
What were you hoping to pull off with this order—and what stopped you short of finishing?
I wanted the full set, but shipping bumped it past what I planned to spend tonight.
What would shipping need to look like for you to finish this order right now?
Sofi · following the thread
Transcripts and topic coverage land in the workspace automatically; webhooks can notify your data stack the same hour.
Frequently asked questions
What does "ambient" mean in Ambient Qual?
Ambient means interviews surface in the flow of normal site use, triggered by behaviour rather than scheduled by a researcher. A shopper who just abandoned checkout sees a short invitation to talk through what stopped them. Someone bouncing from a paid landing page can be asked what they expected. The research programme keeps running between major study cycles without coordinating each session.
Do we need to replace our existing analytics tools?
No. Ambient Qual adds qualitative depth—the language and reasons behind behaviour—alongside what analytics already measures. It tells you why shoppers did or did not convert, not only that they did or did not. Most teams run it next to GA4, Mixpanel, Amplitude, PostHog, or a warehouse pipeline.
Is this for growth teams or research teams?
Both. Growth and merchandising teams get in-context signal that helps interpret funnels and campaign performance. Researchers keep control of study design, consent, topic coverage standards, and how interviews are routed. Execution scales without turning experiments into an unmanaged mess of one-off conversations.
How is participant consent handled for in-flow interviews?
Every participant sees a consent prompt before the interview begins, with a link to your privacy policy. Fieldwork records consent for each session—including timestamp, study, and locale—so you have an audit trail per conversation rather than a one-time banner click that no longer matches what was actually fielded.
What does integration look like?
Today: a single script tag with a publishable key and trigger configuration (exit-intent, time-on-page, or a custom event). The script iframes a hosted interview surface, so nothing in your DOM changes. An npm package and React Native support are on the roadmap.
How do resolver rules route participants to studies?
You define resolver rules that map traits or events to specific studies—for example, a shopper who triggered exit-intent on checkout routes to a cart-abandonment study. Priority ordering decides which study runs when multiple rules match. Frequency caps and cooldown periods reduce the risk of over-interviewing the same visitor.
Related: Fieldwork Interviews, ResearchOps, and pricing.