Explore possible objections before the next cut.
BuyerLens is a synthetic buyer interview engine. Run a structured panel of 6–12 personas against your creative or concept, surface the specific resistance, and decide which objection to remove next.
Use the diagnostics to spot issues. Explore possible objections in simulated interviews.
RoastIQ identifies creative strengths and weaknesses. BuyerLens explores plausible interpretations and objections through simulated interviews. Together they help the team decide which concerns to investigate and what to change.
Synthetic interviews are scenario simulation, not a real consumer panel. The answers depend on the persona, model, questions and supplied creative context. They are hypotheses to review, not independent validation of audience response.
- Real-participant surveyRecruit, screen, field, codeCost and timing depend on the sample and study design.
- Moderated discussionParticipants and a moderatorExplore human responses; scope and format vary.
- Now · BuyerLens6–12-persona synthetic panelDuration varies. Tagged resistance, not a transcript.
- After launchIn-market validationReal signal closes the loop, on a smaller bet.
From one brief to a tagged resistance map, in four passes.
Use a structured question set and persona panel to explore possible objections. Keep core questions consistent within a comparison and distinguish follow-up questions from the original study.
Set the brief
Start from a RoastIQ result. Define the audience and study objective, review the creative context, and select or edit the questions you want to explore.
Build the persona panel
Select 6–12 synthetic personas and inspect their audience fit. The saved study context records the selected profiles. Variation in profiles explores scenarios; it does not make the panel representative of real buyers.
Run the study questions
Review the study questions before running the panel. Use a consistent core question set across personas, and inspect follow-ups separately. A new generated answer can differ even when the question is unchanged.
Tag the resistance
The synthesis organizes recorded answers into themes, tensions and proposed edits. Trace each finding to its source answers and inspect missing or conflicting evidence. Generated themes remain hypotheses.
Five axes. Balanced panels. One resistance map.
Use these dimensions to vary the buying situations explored. Inspect the actual personas selected for the study; diversity of scenarios is not representative sampling.
- Role and life stage: what situation makes the offer relevant?
- Category fluency: what needs explaining?
- Brand familiarity: what does the persona already know?
- Price sensitivity: which trade-offs matter?
- Switching context: what would make an alternative credible?
Read the example counts. Choose what to investigate.
The rules below explain the authored ten-persona illustration above; they are not the product’s scoring algorithm or validated thresholds. Counts refer to example personas with each theme, not real buyers. One persona may contribute to multiple themes. Apply Block first, then Tension, then the low-resistance label. None forecasts conversion.
Trace the finding to the answer.
Look for the question asked, the persona context and the recorded simulated answer. Preserve disagreement and missing evidence.
No audited percentage of agreement with human buyers is published here. Generated answers cannot establish population prevalence, brand recall or campaign lift. See the study-quality guide for practical review questions.
What BuyerLens is, and what it isn't.
Synthetic interviews help explore structured scenarios. Keep their hypotheses distinct from real participant answers and measured campaign outcomes.
BuyerLens does
- Run selected synthetic personas against the creative and study context linked to a RoastIQ result.
- Use a consistent study question set, with focused follow-up questions when needed.
- Organize available answers into themes for further review.
- Present source-linked themes, tensions and recommendations when enough usable interview evidence is available.
- Compare a revised creative while preserving the study context and any remaining objections.
BuyerLens does not
- Run a real consumer panel: each persona is a simulated scenario, not a human participant.
- Measure brand recall, that requires recall-survey methodology.
- Predict sales lift, ROAS, or attributed conversion.
- Establish that a generated edit suggestion will improve a campaign outcome.
- Replace post-launch listening. The map runs before the spend, not instead of it.
SaliencyLab methodology · Updated
How BuyerLens keeps interviews tied to the creative
BuyerLens by SaliencyLab runs AI-simulated buyer interviews from a saved RoastIQ result. It explores plausible objections and interpretations to help you choose the next question or edit. These responses are synthetic hypotheses, not testimony from real customers.
What context carries over from RoastIQ?
The study is anchored to the saved creative and its diagnostic context, with the available brand, audience, platform and benchmark information. New panels retain snapshots of the selected profiles, creative context and question plan. Missing evidence remains a limitation; the advertiser’s brief is kept distinct from what a viewer could see or hear in the ad.
Are the participants viewers or advertising professionals?
The profiles are intended to respond as potential viewers and buyers. Selection checks market, language and audience eligibility, then considers relevant category interests and platform habits. A profile is a reusable simulated scenario, not a newly recruited person or a demographic sample. Inspect the selected profiles and coverage; if eligible profiles are insufficient, revise the audience instead of assuming the panel represents it.
What shapes the generated questions?
The selected study purpose and template, creative evidence, saved context and existing questions inform suggestions. Review and edit the questions before launching. A useful question probes a specific ambiguity without supplying the desired answer: “What would you need to know before trying this?” is more useful than “Does this convincing proof make you want to buy?” A template supplies structure, not evidence of question quality.
How should I read agreement and disagreement?
Inspect the recorded answers behind a theme, including contradictions, missing answers and the number of responses supporting it. Several simulated personas repeating an objection do not establish its prevalence among real buyers. Because BuyerLens receives RoastIQ context, agreement cannot independently validate RoastIQ. Keep supplied audience evidence separate from generated dialogue.
What needs real-world confirmation?
Use a resistance theme to frame an edit or a real interview question. For example, a simulated price objection can motivate testing clearer price and pack-size information; it does not establish actual willingness to pay. Validate audience prevalence, purchase intent and campaign effects with appropriate human research or in-market measurement.
Hear the panel before you re-brief.
Bring the cut and your decision question. We can review the available context, discuss an appropriate study and agree the next step. Any analysis scope and delivery timing are confirmed separately.