# Message test — https://www.supra.consulting/ After reading your page, 13 of 15 personas could name a reason to pick you over a similar option — and differentiation was the weakest of the four. - **Page tested:** https://www.supra.consulting/ - **Audience tested against:** CEO and CMO's from medium-size SMB to enterprise - mostly in B2C domains. - **Personas:** 15 simulated - **Report:** https://grader.wynter.com/r/supra-root-cause-growth-advisory-dr-frank-buck-s0rEilg > These answers are generated by AI, scored on Wynter's B2B Message > Layers framework using behaviorally-diverse simulated personas. The > methodology is real and the critique is directional. What a simulated > persona cannot have is a live budget, a renewal coming up, or a boss > asking about this quarter. --- ## 01 · The scores Every persona answered all four questions. These are four independent proportions of the same panel, not stages of a funnel. | Layer | Question | Cleared the bar | Strength | Of those who passed | | --- | --- | --- | --- | --- | | 1. Clarity | Do they understand what you do? | 15/15 | 78% | all with reservations | | 2. Relevance | Can they tell what it solves, and who it's for? | 15/15 | 82% | 3 without hesitation, 12 with reservations | | 3. Value | Do they actually want it? | 14/15 | 74% | all with reservations | | 4. Differentiation | Is there a reason to pick you over the alternatives? | 13/15 | 70% | all with reservations | **Brand alignment** (a side metric, not one of the four layers) — 14/15, 74% strength (all with reservations). Does the page read like the company you actually are? **Fix first: Differentiation.** Earliest failing layer, walking the sequence in order — not simply the lowest score. --- ## 02 · What to change, layer by layer Ordered worst-first. Specific edits, not a restatement of the score. ### Differentiation **Show mechanism beside every client result, as Interquell does.** "Interquell: up to 5× profit per product through causal pricing" lands because it names the lever. "Sonos: a whole new product category, launched right" and "T-Mobile USA: 4× revenue at record profit" state outcomes with no mechanism. Add the specific driver each engagement uncovered — the demand plateau, the category code, the barrier removed — so the results read as repeatable method rather than vendor luck. *effort medium · impact high · tested against Proof next to the claim* **Name the product category SUPRA competes in.** The header "Root-Cause Growth Advisory" is an invented label, and nothing else on the page places the offer — research supplier, strategy consultancy, or decision-intelligence software. Add one line near the H1 that says what kind of provider this is and how it differs from the alternative the buyer is weighing, e.g. quantified causal demand research replacing conventional brand tracking and consultant judgement. *effort low · impact high · tested against Lead with the use case* ### Value **Add baselines and sources to the case study numbers.** "$10B+ Generated Value", "4× revenue", "up to 5× profit per product" arrive without a starting point, timeframe or attribution basis, which invites a senior reader to discount all of them. Give each figure its denominator and period — over what baseline, across how many months, measured how — and attribute it, ideally with a named client contact or published case link next to the claim. *effort medium · impact high · tested against Proof next to the claim* **Offer a bounded first step on one real decision.** The page presents an advisory relationship with no visible small entry point, so the reader has no low-risk way in. Add a named scoped engagement — one pricing or brand decision, fixed price, fixed timeline, defined deliverable — and offer a reference call with an existing client insights lead. This is the step buyers say they would actually take before any larger commitment. *effort medium · impact high · tested against Answer the live objection* **Promote the Double Jeopardy Test as the primary CTA.** The free test is the strongest asset here but competes at the top with "Download the Growth Whitepaper" and later with "Discuss your case" and "How Demand Architecture works". Make "Take the Double Jeopardy Test" the single hero action, demote the whitepaper to a text link, and state on the test block what the reader gets back and that it runs on their own decision and data. *effort low · impact high · tested against One clear next action* ### Clarity **Explain the Causal AI method in plain mechanics, not labels.** "Deep Implicit Research + Causal AI" under "How we find the truth" names a method but never shows how it works. Replace the abstraction with three concrete steps a reader can picture: what data goes in (e.g. implicit response tests with n= real category buyers), what the model does with it (quantifies which motives causally drive choice, not correlate with it), and what comes out. The FAQ is too late — the mechanics belong in this section. *effort medium · impact high · tested against Plain language* **State the deliverable: what the client receives and when.** Nothing on the page says what is actually handed over. After "Decision-grade evidence you can take to the board", add a concrete line naming the artefacts and timeframe — e.g. a quantified demand-driver map per segment, ranked price and claim levers, and a board-ready decision recommendation, delivered in X weeks. Readers currently cannot tell whether they are buying a study, software, or a consulting engagement. *effort low · impact high · tested against Concrete over abstract* **Define "implicit measurement" where it first appears.** "We measure the subconscious motives, barriers and value drivers behind real choices" reads as marketing gloss because the measurement itself is invisible. Say in plain words how subconscious drivers are captured — reaction-time based choice tasks, real purchase behaviour, whatever the actual instrument is — so the claim "Evidence, not opinion" has something under it. *effort medium · impact medium · tested against Concrete over abstract* ### Relevance **Add an automotive decision example to the industry blocks.** The page invokes premium auto buyers and shows the Audi logo, but every worked example belongs to pet food, FMCG or telco. Give automotive the same treatment the Pet Food block gets: name the decision at stake — trim and options pricing, EV positioning, dealer versus DTC — and the demand driver the work uncovered, so an auto reader sees their own case rather than inferring it. *effort medium · impact medium · tested against Name the audience* ### Brand alignment (side metric) **Replace loosely defined tiers with fixed scope and price.** The tiered engagement framing reads as an open-ended, scope-creeping consulting relationship, which undercuts the evidence-driven, decision-grade positioning the rest of the copy claims. State for each tier what is in scope, what is delivered, and either the price or the price mechanism, so the commercial model matches the promise of "Evidence, not opinion." *effort medium · impact medium · tested against Answer the live objection* --- ## 03 · What is working ### The free Double Jeopardy Test is the offer respondents named as their entry point Three respondents singled out the Double Jeopardy Test as a concrete, low-risk way to evaluate the methodology on their own data before committing, and one made it an explicit precondition for any engagement. It was cited both as a value driver and as a differentiator. This is the strongest specific asset on the page. > The Double Jeopardy Test is the specific thing that would tip me toward them over a generic strategy shop — "we run the decision past three leading AIs... then pressure-test it against our proprietary knowledge graph" is a concrete, low-risk way to see their methodology on my own decision before I commit real budget, and it's free. > > — CEO, Automotive, 201-500 > I'd take the free Double Jeopardy Test on one live pricing or launch decision before I'd sign anything at €50k+, because the case studies are compelling but I have no idea yet if my category/data would produce something as clean as their T-Mobile or Sonos examples. > > — CMO, Consumer Electronics, 501-1000 ### The Interquell demand-plateau example works because it shows a mechanism, not just an… Three respondents pointed to the specific demand-plateau pricing mechanism and its accompanying number as tangible enough to test against their own SKUs, and said it outweighed generic vendor pitches. The T-Mobile revenue claim was similarly named as a credible differentiator. Where the copy shows how a result was produced, respondents believed it. > T-Mobile "4x revenue at record profits" is specific and checkable > > — Chief Executive Officer, Luxury Goods, 1001-5000 > The Interquell pricing line — "products sit on a demand plateau that carries a far higher optimal price," leading to "up to 5x profits per product" — is the one thing here that's specific enough to act on > > — Chief Executive Officer, Consumer Packaged Goods, 1001-5000 > The specific thing that would pull me toward them is the Interquell pricing example: "some products sit on a demand plateau that carries a far higher optimal price" leading to "up to 5x profits per product" > > — CMO, Telecommunications, 501-1000 --- ## 04 · What the personas said ### Proprietary terminology is asserted rather than explained, and respondents call it… Six respondents said the copy names its method without showing mechanics, model, data or step-by-step logic, and described the language as abstract jargon or marketing gloss requiring inference. Two specifically could not tell what deliverable was being sold. This was the most consistently negative reaction on the page. > Phrases like "we see the causal logic behind every recommendation" and "decision-grade evidence" sound reassuring but never show the actual model or data behind them — that's marketing language standing in for method > > — CEO, Automotive, 201-500 > The phrases themselves are abstract nouns doing all the work — 'Demand Architecture,' 'Deep Implicit Research,' 'Causal AI' — none of which tell me if I'm buying a study, software, or a consultant's time. > > — Chief Executive Officer, Consumer Packaged Goods, 1001-5000 > I'd still want to see the actual mechanics of their "knowledge graph" and how implicit measurement is captured before I trust the label, because "Causal AI" is doing a lot of unexplained work here. > > — CMO, Telecommunications, 501-1000 > they're used as if they're established category terms, but they're proprietary-sounding jargon with no plain definition, so I had to infer from context > > — Chief Marketing Officer, Consumer Packaged Goods, 5000+ > The mechanism is still a bit of a black box to me — "causal AI" and "implicit research" get repeated a lot without a clear step-by-step of the actual method > > — CMO, Automotive, 501-1000 ### Absence of automotive proof makes the page feel off-target for that buyer Three respondents flagged that the case studies contain no automotive examples despite the category framing around premium auto buyers, which reduced perceived relevance. One extended this to doubt about bench depth for a sustained automotive engagement. Logos alone did not close the gap. > What would rule them out, or at least stall me, is the industry mix: pet food, telecom, insurance, FMCG — zero automotive logos or case studies. > > — CEO, Automotive, 201-500 > I'd need a line that names my actual situation — a mid-size automotive OEM or supplier under margin pressure trying to move beyond incremental optimization — rather than just consumer brand logos like Sonos or L'Oréal; right now the industry list skips automotive entirely > > — CMO, Automotive, 501-1000 > a page this personality-driven around one founder makes me wonder about bench depth on a live automotive engagement. > > — CEO, Automotive, 201-500 ### Case study numbers are unsourced and lack baselines, so respondents discount the… Four respondents said the case study figures arrive without baseline context, sourcing, or the causal models behind them, which limited credibility of the promised P&L impact. One framed unsourced numbers as actively damaging with a time-pressed senior buyer. The same case studies that generated belief when they showed mechanism generated doubt when they showed only outcomes. > the Interquell "up to 5× profit per product" and T-Mobile "4× revenue at record profits" numbers are the kind of thing that would matter if I could see the base case behind them. > > — CEO, Consumer Electronics, 201-500 > a page that respects my time would footnote "4× revenue" with a timeframe and baseline, not just drop it as a headline > > — Chief Executive Officer, Automotive, 1001-5000 > the named case studies — T-Mobile's "4x revenue," Sonos, Interquell's "up to 5x profits" — never show the mechanism, just the outcome > > — Chief Executive Officer, Telecommunications, 1001-5000 ### Product-category positioning stays vague, and the pricing tiers read as scope creep Two respondents said that despite clear problem framing and client logos, they could not place the offering in a product category. Another read the loosely defined pricing tiers as a signal of an open-ended, scope-creeping engagement. The page's comparison set — McKinsey, Simon-Kucher, Kantar — was noted as assumed rather than established. > the pricing tiers ("From €5k/month," "From €50k") are so vague I can't tell if this is a real engagement or a McKinsey-style anchor-low-then-scope-creep game > > — Chief Marketing Officer, Consumer Electronics, 5000+ > I'd need my category named specifically — CPG, retail, whatever — with a number attached, not just logos like P&G and Unilever sitting in a scroll bar > > — Chief Executive Officer, Consumer Packaged Goods, 1001-5000 > the FAQ positioning against McKinsey/Simon-Kucher and Kantar/Ipsos is clearly aimed at someone who already knows those names and is comparison-shopping > > — Chief Executive Officer, Automotive, 1001-5000 ### The problem and buyer are legible fast, but the mechanism behind Causal AI is not Nine respondents said the problem statement, target audience and high-stakes decision framing came through quickly from the header, logos and case studies. Several of the same respondents drew a line between that clarity and the product itself: what Causal AI and implicit measurement actually do is left to inference or buried in the FAQ. Clarity of audience is not in question; clarity of method is. > Consulting shop selling causal-AI market research for pricing and brand decisions. Fancy insights firm. > > — Chief Executive Officer, Luxury Goods, 1001-5000 > tells you what it does within seconds, and the client logos plus named cases (T-Mobile, Sonos, Allianz) tell you who it's for: CMOs and growth/insights leads at big consumer brands > > — Chief Marketing Officer, Consumer Electronics, 5000+ > the hero line "Consensus feels safe. That's what makes it dangerous" plus "SUPRA finds the root cause of growth and turns it into decisions in brand, innovation, communication, and pricing" tells me the problem > > — CMO, Telecommunications, 501-1000 > "Causal AI" and "Deep Implicit Research" are named but not explained until the FAQ, so my first pass gives me the who and the what but not really the how > > — Chief Executive Officer, Telecommunications, 1001-5000 > What's still missing is a crisp one-line definition of "Causal AI" itself; I had to infer what it actually does from the case studies rather than any stated methodology > > — Chief Marketing Officer, Luxury Goods, 5000+ > The "who" is inferred rather than stated outright, but the client logos (T-Mobile, Sonos, Allianz, P&G) and case studies make it obvious this is aimed at CMOs/leadership teams making high-stakes brand, pricing or innovation calls > > — CMO, Consumer Electronics, 501-1000 ### Nobody would buy the full engagement first; they want a bounded pilot on one real decision Four respondents described the same purchase path: a small, scoped test — entry price tier, one pricing or brand decision, a reference call with a client insights lead — before any larger commitment. Each tied this to needing verifiable, board-defensible proof on their own data. No respondent described committing on the strength of the page alone. > A reference call with the actual T-Mobile or Sonos insights lead who'd walk me through the before/after numbers and confirm SUPRA's model, not the agency's spin, drove the result - that's the one thing that converts curiosity into a paid pilot > > — Chief Marketing Officer, Consumer Electronics, 5000+ > I'd want the Double Jeopardy Test or the smaller "Top 5% Growth Diagnostic" (from €50k, 4 weeks) as a bounded pilot on one real pricing or comms decision, not a full engagement > > — CMO, Telecommunications, 501-1000 > A pricing or brand call where their read directly contradicted what my internal team and agencies were converging on, and where being right on that call was later verifiable in the P&L — one real save like that a year justifies the retainer > > — Chief Marketing Officer, Luxury Goods, 5000+ --- ## 05 · The hardest read An adversarial pass over the findings. Every claim below was checked against the panel's own answers; unsupported ones were dropped. - **The page sells a method nobody can describe back, so it cannot be evaluated, let alone bought.** *(high)* Six respondents said the proprietary method is named without mechanics, model, data or logic, and called it abstract jargon; two could not identify the deliverable. Nine said the problem and buyer land fast but the mechanism behind Causal AI is left to inference or buried in the FAQ. The page's clearest asset — audience framing — is attached to an unexplainable product. - **The case studies are doing the page's persuasion work, and half of them are doing it backwards.** *(high)* Three respondents believed the Interquell demand-plateau and T-Mobile figures because a mechanism was shown, while four discounted the same case study format for arriving without baselines, sourcing or causal models — one calling unsourced numbers actively damaging with a senior buyer. The identical asset produces belief and doubt depending on whether mechanism appears, which means the page's outcome-only figures are subtracting credibility, not adding it. - **The page cannot close anyone. Its own best-case outcome is a free test.** *(high)* Four respondents described an identical purchase path — entry tier, one bounded decision, a reference call — and no respondent described committing on the strength of the page alone. Three named the free Double Jeopardy Test as their entry point, one as a precondition. The strongest asset on the page is the one that generates no revenue. - **The one thing the page proves is that mechanism sells and the page mostly withholds it.** *(high)* Where mechanism appeared — the Interquell demand plateau — three respondents said it outweighed generic vendor pitches and was testable against their own SKUs. Where it did not, six called the method jargon and four discounted the numbers. The evidence for the fix is already on the page and unevenly applied. - **Framing the category around premium automotive buyers and then showing no automotive work reads as a bluff.** *(medium)* Three respondents flagged the absence of automotive examples in the case studies despite the category framing, with one doubting bench depth for a sustained automotive engagement. Logos did not close the gap. The page invites the exact comparison it fails. - **Without a stated category, the pricing tiers become the only thing buyers can interpret — and they interpret them as risk.** *(medium)* Two respondents could not place the offering in a product category despite clear problem framing and logos; another read the loosely defined tiers as an open-ended, scope-creeping engagement. When the page will not say what it is, buyers price the ambiguity themselves and against the seller. --- ## 06 · Who answered | # | Role | Industry | Company size | | --- | --- | --- | --- | | 1 | Chief Executive Officer | Consumer Packaged Goods | 1001-5000 | | 2 | Chief Marketing Officer | Consumer Electronics | 5000+ | | 3 | CEO | Automotive | 201-500 | | 4 | CMO | Telecommunications | 501-1000 | | 5 | Chief Executive Officer | Luxury Goods | 1001-5000 | | 6 | Chief Marketing Officer | Consumer Packaged Goods | 5000+ | | 7 | CEO | Consumer Electronics | 201-500 | | 8 | CMO | Automotive | 501-1000 | | 9 | Chief Executive Officer | Telecommunications | 1001-5000 | | 10 | Chief Marketing Officer | Luxury Goods | 5000+ | | 11 | CEO | Consumer Packaged Goods | 201-500 | | 12 | CMO | Consumer Electronics | 501-1000 | | 13 | Chief Executive Officer | Automotive | 1001-5000 | | 14 | Chief Marketing Officer | Telecommunications | 5000+ | | 15 | CEO | Luxury Goods | 201-500 | --- ## 07 · Before you act on this The methodology is real, and the critique is directional. What a simulated persona cannot have is a live budget, a renewal coming up, or a boss asking about this quarter. **Validate anything you're betting on with real ICPs who are actually in-market.** Being wrong is more expensive than you think. Finding out is cheaper than you'd guess. Wynter runs message testing with verified B2B professionals — trusted by HubSpot, RingCentral, Shopify, Cognism, Paddle, Veeam, Rippling and Miro. This report is kept for 60 days from 2026-08-25, then deleted along with the personas and their answers.