Product & UX

Measures plan-change intent, upgrade/downgrade drivers, and migration friction among active SaaS users. Use with account holders and team leads to identify churn risk, prioritize feature investments, and reduce plan-change barriers.

Collects structured beta tester feedback on feature usability, stability, and adoption intent to support data-driven go/no-go release decisions.

Assesses how clearly customers understand plan differences, invoice details, and usage limits to identify friction points that drive churn and support volume in SaaS products.

Enables cross-functional stakeholders to evaluate roadmap initiatives on Impact, Confidence, and Ease dimensions, producing quantified ICE priority scores alongside qualitative risk and rationale data.

Measures user satisfaction with recommendation relevance, perceived control, and content diversity. Designed for product teams optimizing personalization engines and recommendation UX.

Force real trade-offs with best-worst scaling: respondents pick the most and least valuable feature in rotating sets, then an AI interviewer probes the reasoning behind their top pick. Produces a true preference ranking instead of everything-is-important rating inflation.

Collects structured feedback from pilot participants on instrument clarity, cognitive burden, ambiguity sources, and session flow to guide pre-launch refinement of research questionnaires.

Captures how easily visitors find, use, and hear back from your website's contact channels — form, email, chat, or phone — with an AI follow-up that digs into the specific moment things got confusing or slow.

A short on-site pop-up survey that captures why a visitor came, whether they accomplished their task, and where they hit friction — with an AI follow-up that reconstructs the exact moment things went wrong instead of relying on a vague satisfaction score. Built for UX and product teams running always-on site feedback.

A self-serve usability session: participants share their screen, complete guided tasks on your website or prototype while thinking aloud, and the AI moderator watches, prompts, and probes — then debriefs them. You get recordings, transcripts, and task-level analysis without scheduling a single session.

Measures why active users stay, what value they perceive, and where retention risk exists. Designed for product teams assessing product-market fit, switching costs, and competitive positioning.

Measures consumer reactions to a new product concept across innovation perception, quality assessment, purchase intent, and feature priorities. Designed for pre-launch product testing with target consumers.

A product-discovery instrument for e-commerce operations professionals that identifies operational pain points, prioritizes potential capabilities, and measures adoption readiness and willingness to pay.

An open-ended card sorting study for navigation and information architecture: participants group your content into categories that make sense to them, then an AI interviewer probes the logic behind their groupings — the part a drag-and-drop grid alone never captures.

A post-task usability instrument using the validated Single Ease Question (SEQ) to measure task completion, perceived ease, and satisfaction. Ideal for UX researchers conducting moderated or unmoderated usability studies who need benchmarkable ease-of-use data alongside qualitative insight.

Evaluates how users engage with, perceive, and act on product release notes. Use this survey to identify gaps in clarity, content preferences, and delivery channel effectiveness to improve update communications.

Evaluates how users with color vision differences experience color and contrast in digital interfaces, measuring pain points, satisfaction, design preferences, and customization needs to inform accessible UI/UX design.

Captures early adopter feedback on task completion ease, perceived value, bugs, and improvement priorities to inform MVP iteration decisions for product teams.

Measures engineering teams' risk tolerance, monitoring confidence, and rollback preparedness for feature-flagged deployments. Designed for engineers, SREs, and product managers managing progressive rollouts.

Assesses user dependency, workaround patterns, and transition readiness before sunsetting a product feature. Designed for product teams making data-driven go/no-go retirement decisions.