Setting up an agentic research study takes under five minutes. But knowing what to test, how to frame the inputs, and how to choose the right mode determines whether the output is useful or noise. This guide provides ready-to-use templates for the three agentic research modes, complete with required inputs, audience targeting frameworks, sample size guidance, and example outputs. For the conceptual foundation, see the pillar guide to AI customer interviews and the definition of a customer intelligence hub.
The three modes are Preference Check (which option wins and why), Claim Reaction (is this statement believed), and Message Test (does this framing resonate). Using the wrong mode for a research question produces unreliable findings even when the platform mechanics work perfectly, which is why the mode selection logic in this guide matters as much as the templates themselves.
What inputs does the Preference Check template need?
Use Preference Check when you need to know which of two or more options people prefer and why. Common applications include headline tests, packaging designs, feature priority comparisons, pricing model evaluations, and any A/B comparison where the goal is directional preference plus motivation.
Required Inputs
| Input | Example |
|---|---|
| Options to compare | ”Headline A: ‘Ship faster with AI’ vs. Headline B: ‘Build products people actually want‘“ |
| Target audience | ”Product managers at B2B SaaS companies with 50-500 employees” |
| Context (optional) | “These are homepage headlines for a developer tools company” |
| Sample size | 15 audio interviews |
What you get back
- Preference split with percentages (e.g., “67% prefer Headline B”)
- Top 3-5 themes driving the preference, ranked by prevalence
- Minority objections from those who disagreed, with real verbatim quotes
- Conditions under which preferences might change
- Data quality indicators
Example prompt for your AI agent
“Run a preference check with 15 B2B SaaS product managers. Compare these two homepage headlines: ‘Ship faster with AI’ vs. ‘Build products people actually want.’ Context: these are for a developer tools company homepage. I need to know which one resonates more and why.”
When to scale up
If the preference split is close (55/45 or tighter), consider running a follow-up with 30 participants to increase confidence. If the split is decisive (70/30 or wider), 15 participants provide sufficient evidence for action. The cheap-and-fast cycle ($20 per interview, 24-48 hour turnaround) makes a second round operationally trivial when the first round is ambiguous.
A common Preference Check mistake is testing too many options simultaneously. Comparing two or three options produces clear preference patterns and rich qualitative explanation. Comparing five or six options fragments participant attention and produces shallow reasoning across all of them. Run multiple two-option or three-option Preference Checks rather than one large multi-option comparison when you need to evaluate many alternatives.
What inputs does the Claim Reaction template need?
Use Claim Reaction when you need to know whether people believe a specific statement and what objections they have. Common applications include pricing-page claim tests, product-page hero-statement validation, advertising claim audits, and any context where credibility matters more than preference.
Required Inputs
| Input | Example |
|---|---|
| Claim to test | ”Our platform reduces customer research time by 95%“ |
| Target audience | ”VP-level consumer insights professionals at companies with $100M+ revenue” |
| Context (optional) | “This claim will appear on our website pricing page” |
| Sample size | 20 audio interviews |
What you get back
- Agreement rate with percentage (e.g., “45% find the claim credible”)
- Reasons for belief, ranked by prevalence
- Specific objections from skeptics, with real verbatim quotes
- Suggested modifications that would increase credibility
- Emotional reactions to the claim (trust, skepticism, interest)
Example prompt for your AI agent
“Run a claim reaction study with 20 consumer insights VPs at $100M+ revenue companies. Test this claim: ‘Our platform reduces customer research time by 95%.’ This will appear on our pricing page. I need to know if it’s credible and what objections people have.”
Interpreting results
Claims that test below 50% credibility need revision. Look at the objection themes; they often contain the specific language adjustments that would make the claim believable. A claim that “feels too good to be true” may become credible with added specificity (“reduces time from six weeks to three hours”). Vague claims are often rescuable through concrete substitution; broken claims need to be rebuilt rather than tweaked.
Sample size for Claim Reaction can be modestly larger than Preference Check because objection themes need enough volume to surface reliably. Twenty interviews typically produces three to five distinct objection clusters, which is the right resolution for credible message revision.
What inputs does the Message Test template need?
Use Message Test when you need to know whether a message is clear, what people think it promises, and how it makes them feel. Common applications include tagline tests, brand positioning copy, campaign concepts, and any context where clarity and emotional resonance matter alongside literal comprehension.
Required Inputs
| Input | Example |
|---|---|
| Message to test | ”Customer intelligence that compounds. Every conversation builds on the last.” |
| Target audience | ”Marketing directors at CPG brands” |
| Context (optional) | “This is tagline copy for an AI research platform” |
| Sample size | 15 audio interviews |
What you get back
- Clarity score (percentage who understood the intended meaning)
- What participants think the message promises (in their own words)
- Emotional associations (what the message makes them feel)
- Confusion points (specific words or phrases that cause friction)
- Suggested improvements from participant language
Example prompt for your AI agent
“Run a message test with 15 marketing directors at CPG brands. Test this copy: ‘Customer intelligence that compounds. Every conversation builds on the last.’ Context: this is tagline copy for an AI research platform. I need to know if it’s clear, what people think it means, and how it makes them feel.”
Using Message Test results
The most valuable output is often what participants think the message promises in their own words. If their interpretation matches your intent, the message works. If there is a gap between intent and interpretation, their language often contains the fix. Participants describe what they want to hear; use their words. This pattern is the same craft principle underlying any consumer-anchored copywriting: the language that lands is the language the audience already uses, not the language the brand wants them to use.
Side-by-side: when to use each mode
| Mode | Question Type | Optimal Sample | Output Focus | Common Mistake |
|---|---|---|---|---|
| Preference Check | Which option wins? | 15 (scale to 30 if close) | Preference split + reasons | Testing too many options at once |
| Claim Reaction | Is this believed? | 20 | Credibility + objection themes | Tweaking unrescuable claims |
| Message Test | Does this resonate? | 15 | Clarity + emotional associations | Treating literal comprehension as the only criterion |
Choosing the wrong mode for a question is the most common agentic-research mistake. Preference Check produces preferences but not credibility scoring; Claim Reaction surfaces objections but not preference splits; Message Test reveals interpretation gaps but is not designed for head-to-head comparison. Selecting the mode that matches the underlying decision determines whether the output is useful.
How should audience targeting be set up?
Getting the right audience is as important as the right question. Three audience-design principles apply across all three modes.
B2B audiences
| Target | Targeting Criteria |
|---|---|
| Enterprise buyers | Job title + company size + industry |
| Product managers | Role + company type + team size |
| C-suite | Title level + company revenue + industry |
| Technical decision-makers | Role + technology stack + company size |
B2C audiences
| Target | Targeting Criteria |
|---|---|
| Category purchasers | Purchase behavior + frequency + brand |
| Demographic segments | Age + location + income + household |
| Behavioral segments | Usage patterns + channel preferences |
| Lapsed customers | Previous purchase + time since last activity |
First-party vs. panel
Use your own customers when: you need feedback from people who know your product, testing retention messaging, understanding churn, or validating features for existing users. The first-party audience is the right choice whenever the research question is about your product specifically rather than the category broadly.
Use the platform panel when: you need feedback from prospects, testing acquisition messaging, evaluating brand perception, or reaching audiences outside your customer base. User Intuition’s 4M+ vetted panel covers B2C and B2B audiences across 50+ languages, with 98% participant satisfaction and continuous quality vetting. Studies start at $200, return results in 24-48 hours, and carry 5/5 ratings on G2 and Capterra.
What sample size should each study use?
Sample size decisions for agentic research differ from traditional qual because the cheap-and-fast cycle makes iterative studies operationally feasible. The right discipline is to start with the smallest sample that answers the question and scale only when the data warrants it.
| Decision Type | Recommended Sample | Cost (Audio, Professional) |
|---|---|---|
| Quick sanity check | 10 | $200 |
| Tactical validation | 15 | $300 |
| Confident directional finding | 20-30 | $400-$600 |
| Segment comparison (2 segments) | 30 (15 per segment) | $600 |
| Quantitative confidence | 50+ | $1,000+ |
| Multi-market (3 markets) | 45 (15 per market) | $900 |
Start with the smallest sample that answers your question. Agentic research makes follow-up studies cheap and fast; it is better to run two focused 15-person studies than one unfocused 30-person study. The compounding intelligence from the customer intelligence hub ensures that smaller sample sizes per study still build into substantial cumulative evidence across the broader research program.
When in doubt, run the 15-person version first and review the output before committing to a larger sample. The platform’s 24-48 hour turnaround makes this iterative approach operationally trivial, and the cost savings on unused larger samples can fund additional focused studies that produce more actionable intelligence overall.
How should agentic research integrate with the broader intelligence hub?
Agentic studies are most valuable when their outputs feed the same customer intelligence hub that captures the team’s continuous-discovery and episodic-research outputs. The consumer ontology guide covers the structural framework that makes findings comparable across study types.
Three integration patterns produce the strongest cumulative value. First, tag every agentic study with the underlying decision it informed, not just the research question it answered. Six months later, the team can query “What messaging decisions did we test in Q3 and which ones produced the strongest claim-reaction credibility scores?” without re-reading individual study deliverables. The evidence trails for auditable customer intelligence guide covers how the underlying architecture preserves this decision-context across time.
Second, link agentic-study verbatims into the hub’s verbatim database so future queries can surface the original consumer language alongside the structured findings. This linkage turns each agentic study from a point-in-time deliverable into a permanent contribution to the team’s consumer-language corpus, which compounds in value as the corpus grows. The conversational querying for customer intelligence guide covers the query patterns that exploit this verbatim density.
Third, schedule recurring agentic studies for high-stakes recurring messaging contexts. Quarterly claim-reaction tests on key product-page claims, monthly preference checks on top-of-funnel ad creative, and seasonal message tests on positioning copy together produce a continuous stream of structured marketing intelligence that supplements the team’s broader research program. The cumulative value of these recurring studies dramatically exceeds the value of running them as one-time exercises because the comparative data across waves surfaces shifts in audience perception that single-point studies cannot detect.
The integration pattern moves agentic research from a tactical messaging tool to a strategic intelligence layer. Teams that exploit this integration consistently outperform teams that treat agentic studies as isolated quick-turn projects, even when the per-study quality is comparable.
How User Intuition runs the Preference Check, Claim Reaction, and Message Test templates
The three templates in this guide are study designs; User Intuition is the system that executes them. Each template’s inputs — the stimulus pair, the claim set, the message variants — get loaded as the opening structure for an AI-moderated interview, and the moderator then probes every participant’s reaction with adaptive follow-up rather than recording a single forced-choice answer. That is what gives a Preference Check its qualitative texture: the platform does not just capture which option won, it captures the reasoning behind each individual choice.
The differentiator for agentic study quality is the speed-and-targeting combination that lets these templates run as a routine practice instead of a special project. Audience targeting is set against a recruited panel, fieldwork returns inside a tight window, and the synthesis arrives structured — which is precisely what makes the embedded-trigger operating model in this guide feasible: a claim reaction can actually fire on every pricing-page change because the study is cheap and fast enough to be a default quality gate. Run repeatedly, the same template surfaces wave-over-wave perception shifts a one-off study cannot.
Teams operationalizing these templates can route every study into a customer intelligence hub so claim-reaction scores stay comparable across quarters, or book a demo to watch a Message Test build depth past the single-variant verdict.
What common pitfalls reduce agentic research quality?
Three pitfalls recur often enough to warrant explicit attention when teams begin using agentic templates.
The first pitfall is treating Preference Check as if it were a poll. The 67% preference split from a 15-person interview study is a directional indicator with rich qualitative explanation, not a statistically projectable percentage to the broader audience. Some teams report Preference Check splits with the same precision they would report a 500-person survey result, which overstates the certainty and risks credibility when the actual rollout result differs. The right framing for stakeholders is “strong directional preference for B with consistent qualitative reasoning around clarity and benefit framing” rather than “67% of customers prefer B.”
The second pitfall is iterating on a broken Claim Reaction without revisiting the underlying claim structure. When a claim tests at 30% credibility, the objection themes sometimes reveal that no amount of tweaking will rescue the underlying assertion because the assertion itself is implausible to the audience. Teams that iterate four or five times on increasingly minor variations of a fundamentally implausible claim waste cycles. The discipline is to read the objection themes critically: if they cluster around the assertion itself rather than around the language, the claim needs to be rebuilt, not tweaked.
The third pitfall is using Message Test to validate copy that is actually a positioning question. Message Test evaluates whether a specific message is understood and feels right. It does not evaluate whether the underlying positioning the message is trying to express is the right positioning. Teams that conflate the two end up testing many variations of a fundamentally misdirected message and never confront the deeper question of whether the positioning strategy itself is correct. The right sequence is to validate positioning through brand strategy work first, then use Message Test to evaluate the specific copy that expresses the positioning.
How should leaders embed agentic research in the team operating model?
Agentic research is most valuable when it becomes a default rather than an exception. Three operational practices help teams reach this default state within a quarter or two of adoption.
First, build a study-template library mapped to the team’s most common research questions. Marketing teams use claim reactions on every pricing page change, preference checks on every hero copy revision, and message tests on every campaign concept. Product teams use claim reactions on feature value statements, preference checks on UX copy decisions, and message tests on positioning shifts. Building the templates once and reusing them dramatically reduces setup cost on each subsequent study, which is what shifts agentic research from “occasional special project” to “routine operating practice.”
Second, integrate agentic-study triggers into existing workflows. Pricing-page reviews automatically include a claim reaction on any modified credibility statements. Campaign concept reviews automatically include a preference check on alternative creative directions. Product-page hero copy reviews automatically include a message test before launch. The integration moves agentic research from an explicit project request to an embedded quality gate, which dramatically increases the volume of validated decisions without proportionally increasing the team’s research-operations overhead.
Third, share agentic-study outputs broadly within the organization rather than confining them to the requesting team that initially commissioned the work. Sales teams benefit substantially from knowing which value claims the customer base finds most credible because those claims become anchor points for cold outreach, demo positioning, and objection-handling conversations. Customer-success teams benefit from knowing which messaging resonates with which segments because those resonances inform expansion-conversation framing and retention-call language. Product teams benefit from knowing which competitive comparisons surface most often in audience conversation because those comparisons reveal positioning gaps that product strategy can address. Broad sharing turns each agentic study into an organizational-intelligence contribution rather than a single-team deliverable, which compounds the cumulative value of every agentic study run within the organization over multi-quarter horizons and converts the agentic-research investment from a tactical messaging expense into a strategic intelligence infrastructure.