Marketing & Growth

Tests how a specific ad lands — attention, message clarity, believability, likability, and purchase intent — plus which creative elements actually stick. An AI follow-up interview digs into the 'why' behind the intent score so you're not left guessing from numbers alone. Built for brand and creative teams pre- or post-launch.

Measures how often and how favorably customers encounter your brand when they ask AI tools like ChatGPT or Gemini for recommendations, versus competitors. An AI follow-up reconstructs the actual prompts, answers, and buying decisions behind a recent AI-assisted search.

Measures the core drivers of brand strength — unaided and aided awareness, familiarity, consideration, attribute perceptions, and likelihood to recommend — benchmarked against named competitors. An AI follow-up interview probes why respondents rate the brand as they do, surfacing the perceptions behind the numbers for marketing and brand teams running health trackers.

Measures how safe, respected, and free from harassment or spam your followers feel when engaging with your brand's social media community — comments, replies, and DMs. Built for social media and community managers who need more than sentiment scores; the AI follow-up interview reconstructs a specific safety experience so you know exactly what to fix in moderation.

Evaluates how recipients actually experience your marketing or product emails — layout, readability, mobile rendering, and visual hierarchy — rather than just open and click rates. An AI follow-up interview digs into what made a specific recent email easy or hard to act on, surfacing design friction that analytics alone won't show.

Measures marketing professionals' awareness, trust, perceived accuracy, and adoption barriers related to data clean rooms for measurement. Ideal for ad tech vendors, industry bodies, or research teams benchmarking clean room sentiment.

Measures perceived ad intrusiveness, relevance, tolerance thresholds, preferred ad controls, and willingness to pay for ad-free experiences among internet users to inform UX and monetization strategy.

A 90-day diagnostic survey for ecommerce teams to evaluate campaign goal-setting, attribution methods, tool effectiveness, and cross-functional alignment — surfacing gaps in ROAS, CAC measurement, and reporting workflows.

Assesses an organization's preparedness for third-party cookie deprecation and signal loss across paid media channels, covering current measurement maturity, privacy-preserving toolkit adoption, investment priorities, and organizational barriers. Designed for marketing, analytics, and data science leaders.

Measures whether a celebrity or influencer endorsement actually transfers credibility to a brand — covering perceived authenticity, trustworthiness, and purchase influence — with an AI follow-up that unpacks why the pairing does or doesn't feel believable to this specific respondent. Built for brand, marketing, and partnerships teams testing a specific spokesperson or creator campaign.

Measures which digital marketing channels customers actually notice, trust, and act on — from social ads to email to influencer content — and how they'd prioritize messaging and budget. An AI follow-up reconstructs the specific ad moment that shaped their perception, surfacing the 'why' behind trust or annoyance that closed questions miss. Built for marketing teams evaluating channel mix, creative, and spend.

Measures marketer trust in multi-touch attribution outputs and identifies perceived channel biases. Designed for marketing, analytics, and media professionals who work with attribution data to inform budget and optimization decisions.

Measures whether a specific influencer or creator campaign actually changed awareness, trust, and purchase behavior — not just impressions. Built for brand and growth marketers evaluating a campaign, with an AI follow-up that reconstructs the real moment a customer decided (or chose not) to act after seeing the content.

Screens content writer applicants on experience, content types, self-rated skills, working style, and rate expectations. An AI follow-up interview reconstructs how a candidate actually approached a real writing sample or assignment, surfacing genuine process over rehearsed self-praise. Built for hiring managers and recruiters filling writing roles.

Measures whether subscribers actually open, skim, or read your newsletter, what content keeps them subscribed, their ideal send frequency, and what pushes them toward unsubscribing. The AI follow-up digs into the real moment a reader almost unsubscribed, not just the stated reason.

Structured stakeholder or customer feedback on a design, mockup, or creative asset: first impressions, a click on what draws attention, ratings against the brief, and file upload for marked-up versions. The AI interviewer converts 'I just don't like it' into usable direction.

Assesses email marketers' authentication setup, list hygiene, monitoring habits, and inbox-placement confidence to identify deliverability gaps and training needs.

Measures how subscribers actually read and value your newsletter — open habits, section-by-section ratings, and content priorities — with an AI follow-up that digs into the specific reasons behind promoter or detractor scores instead of generic satisfaction talk. Built for newsletter editors, content marketers, and community teams refining a recurring send.

Captures how listeners actually consume your podcast — ideal episode length, topic preferences, ad tolerance, and what would push them to subscribe or share it — with an AI follow-up that digs into the real reason behind their loyalty score instead of a generic satisfaction number.

Where do people click first when given a task on your design? First-click success is one of the strongest predictors of task completion. Upload your screenshot, set the task, and let the AI interviewer ask what they expected to happen — turning a click map into a usability diagnosis.