Clarity
Do they understand what you do?
Strong15 of 15
15 could name what kind of product this is, unprompted.
Saved report, kept for 60 days — expires in 55 days. Re-opening it is free.
01
Your verdict
Relevance
Can they tell what it solves, and who it's for?
Strong13 of 1513 could quickly tell what problem it solves and who it is for.
Value
Fix firstDo they actually want it?
Mixed11 of 1511 would take a meeting to learn more.
Differentiation
Is there a reason to pick you over the alternatives?
Strong12 of 1512 could name a reason to pick you over a similar option.
See what they thought you were
Your page describes: experimentation platform. They said:
- 3×Experimentation / feature flag platformmatches
- 2×Feature Flagging / Experimentation Platformmatches
10 couldn't name one; 5 got it right.
Four separate measures, not stages: all 15 personas answered all four questions. Each square is one persona.
Additional signalBrand alignment13 of 15StrongShow finding ▸
Cut "optimize at the speed of AI" from the hero subhead. Not one of the four layers, and it does not affect the scores above or the order to fix them in.
These are 15 simulated buyers. Want 15 real ones?
Test with humans02
Fix these firstFix these first
Three edits, in the order that matters.
The first is on your weakest layer, the second on the next, the third on the layer the most buyers had a problem with. Each says what to change on the page and why, with one simulated answer behind it.
Add a migration section after the product grid covering timeline, effort, and data import.
Why: A buyer replacing five tools cannot tell what switching costs them in time or retraining. Add a short block naming typical migration timeline, who does the work, and what carries over from existing tools.
4 of 15 raised this
“A documented, apples-to-apples migration case — current stack, swap-in timeline, and a verified before/after cost or headcount number from a company our size — not a pull-quote”
Head of Product, Software · 5000+ employeessimulated Moves ValueAnswer the live objectionAdd a baseline to each case study stat, such as the before figure for Brex's 50%.
Why: "+50% time efficiency gain" has no starting point, so the number cannot be judged. Write what the team did before and after, with the timeframe.
4 of 15 raised this
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of…” Show full quote
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of the page”
Data Leader, SaaS · 501-1000 employeessimulated Moves DifferentiationProof next to the claimName the buyer in the hero subhead, such as product and data teams.
Why: Readers work out who the page is for from logos and testimonials instead of being told. Put the role and team in the subhead under "Measure what ships."
6 of 15 raised this
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs…” Show full quote
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs or marketers”
Data Leader, SaaS · 501-1000 employeessimulated Moves RelevanceName the audience
Keep these · 2
These landed. Keep the wording when you edit around it.
- Keep · Value
The consolidation story — one platform replacing five tools — is the clearest thing on…
“one platform, one source of truth for "did this feature actually move the number."”
Data Leader, SaaS · 501-1000 employeessimulated - Keep · Value
Named case studies with numbers — Brex, Notion — are the proof respondents believed
“The Brex case, "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools," is the kind of consequence that would actually move my budget…” Show full quote
“The Brex case, "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools," is the kind of consequence that would actually move my budget conversation, because it speaks to headcount and velocity, not just features.”
Director of Product, Software · 1001-5000 employeessimulated
03
All recommendations
Value
Mixed11 of 15- Shown aboveAdd a migration section after the product grid covering timeline, effort, and data import.
Moves ValueProof next to the claim
Add pricing range or pricing-model line near the Brex 20% cost-savings claim.
Why: The page claims 20% cost savings from consolidating tools but never says how Statsig is priced, so no one can do the math. State the pricing model and link to the pricing page beside that stat.
4 of 15 raised this
“A documented, apples-to-apples migration case — current stack, swap-in timeline, and a verified before/after cost or headcount number from a company our size — not a pull-quote”
Moves ValueSpecifics beat superlatives
Replace "shockingly affordable" in Feature Management with a concrete price or comparison.
Why: "Shockingly affordable" is a claim with nothing behind it and reads as sales talk next to real numbers elsewhere. Give a per-event or free-tier figure instead.
4 of 15 raised this
“A documented, apples-to-apples migration case — current stack, swap-in timeline, and a verified before/after cost or headcount number from a company our size — not a pull-quote”
Differentiation
Strong12 of 15Moves DifferentiationProof next to the claim
Add source and measurement period under the 1+ Trillion and 2.5 Billion scale stats.
Why: Readers discount "1+ Trillion events processed per day" because nothing says how it is measured or over what window. Add a one-line footnote naming the measurement period and source.
4 of 15 raised this
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of…” Show full quote
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of the page”
Moves DifferentiationGive a reason to choose you
Add a line under "5+ products in a single platform" naming the tools it replaces.
Why: The consolidation story is the strongest thing on the page but stays generic. Name the categories of separate tools a team drops when they consolidate, and what that removes from their stack.
4 of 15 raised this
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of…” Show full quote
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of the page”
Relevance
Strong13 of 15Moves RelevanceName the audience
Add a budget-owner line near the case studies covering vendor consolidation cost.
Why: Everything speaks to practitioners running tests, with nothing for the person signing the contract. Add a sentence stating what consolidating separate experimentation, analytics, and replay contracts does to total spend.
6 of 15 raised this
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs…” Show full quote
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs or marketers”
Clarity
Strong15 of 15Moves ClarityPlain language
Define "experimentation velocity" where it first appears in the Ancestry stat.
Why: "30x experimentation velocity" and "velocity from 70 to 600+" assume the reader knows the unit. Say experiments run per quarter or per year in plain words.
2 of 15 raised this
“"Measure what ships. Ship what matters." reads like a tagline built for rhythm, not clarity, and "test how each release... lands with customers" is soft verbs doing the…” Show full quote
“"Measure what ships. Ship what matters." reads like a tagline built for rhythm, not clarity, and "test how each release... lands with customers" is soft verbs doing the work a noun should do. I had to get past that to the product tile labels before I had actual nouns — flags, experiments, analytics”
Brand alignment
Strong13 of 15Moves Brand alignmentConcrete over abstract
Cut "optimize at the speed of AI" from the hero subhead.
Why: The phrase carries no meaning and could sit on any vendor's homepage. Replace it with the concrete job: test every release against real customer metrics before it ships wide.
04
Buyer evidence
Biggest risks
A deliberately adversarial read of the same answers. Each claim was checked back against what the personas said and dropped if nothing supported it.
- high
The page's own proof points contradict each other — the same customer numbers are both the strongest evidence and the biggest credibility problem.
Six respondents flagged unsourced statistics including the Brex numbers as lacking baselines, while four cited those same named case studies as the credible basis for a budget conversation. The proof is doing damage and work simultaneously.
- high
The page is unsellable internally: it convinces the person who cannot sign and ignores the person who can.
Four respondents said the messaging addresses technical users and omits cost justification for consolidating vendors, and four more demanded migration, retraining and pricing detail before judging the value. Nothing on the page survives a budget review.
- high
The consolidation story is a liability, not an asset, because it raises the switching question the page then refuses to answer.
Eight respondents read 'one platform replacing five tools' as the core value, but four demanded a documented migration case before they could judge whether the promised time savings survive the cost of switching. The headline claim generates its own…
- medium
Readers are doing the positioning work the page should do, and they are doing it from the footnotes.
Six respondents inferred the target buyer from logos, testimonials and job titles because the page never names who it is for, and two said the hero tagline only makes sense after the product tiles explain it. Comprehension arrives late, if at all.
- medium
With no competitor named and no methodology shown, every differentiation claim rests on a single customer quote.
One respondent noted the page never positions against LaunchDarkly or Eppo, and six discounted the scale metrics for lacking sources or baselines. Both differentiation routes are closed at once.
- low
Vocabulary the page treats as self-evident is not — 'experiment' and 'velocity' are asked to carry the value proposition without definition.
Two respondents found the hero unclear on first read and said these terms are used without precise definitions, requiring the product tiles to retroactively explain them. The hero is spending attention it has not earned.
Value
Switching cost is the unanswered objection — migration and retraining are never addressed
4 of 15
“A documented, apples-to-apples migration case — current stack, swap-in timeline, and a verified before/after cost or headcount number from a company our size — not a pull-quote”
Head of Product, Software · 5000+ employeessimulated See all 3 comments
“I'd go in wanting to see the actual migration path and pricing at my scale, because "generous free tier" and case-study logos from OpenAI-scale companies don't tell me…” Show full quote
“I'd go in wanting to see the actual migration path and pricing at my scale, because "generous free tier" and case-study logos from OpenAI-scale companies don't tell me what this costs or how painful the cutover is for a 201-500 person company”
Product Manager, SaaS · 201-500 employeessimulated “The Brex case study claiming "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools" is the kind of number that would matter to…” Show full quote
“The Brex case study claiming "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools" is the kind of number that would matter to me, but it's unsourced and context-free”
Head of Product, Software · 501-1000 employeessimulated The consolidation story — one platform replacing five tools — is the clearest thing on…
6 of 15 · what worked
“one platform, one source of truth for "did this feature actually move the number."”
Data Leader, SaaS · 501-1000 employeessimulated See all 8 comments
“kill two or three vendor contracts — flagging, experimentation, and analytics — and get my engineers and data team working off one data model instead of reconciling numbers…” Show full quote
“kill two or three vendor contracts — flagging, experimentation, and analytics — and get my engineers and data team working off one data model instead of reconciling numbers across LaunchDarkly, Optimizely, and Amplitude”
Product Manager, SaaS · 201-500 employeessimulated “A/B testing infrastructure bundled with product analytics and session replay”
Senior Product Manager, Technology · 501-1000 employeessimulated “the hero line "Measure what ships. Ship what matters." plus the subhead about testing "how each release - from features to AI-powered experiences - lands with customers" told…” Show full quote
“the hero line "Measure what ships. Ship what matters." plus the subhead about testing "how each release - from features to AI-powered experiences - lands with customers" told me within a few seconds this is about shipping features and measuring their impact, and the product grid right below (Experimentation, Feature Management, Product Analytics, Session Replay) confirmed it.”
Director of Product, Software · 1001-5000 employeessimulated “If it worked as promised, I'd kill 3-4 contracts — flagging, experimentation, analytics, maybe session replay — and get my data scientists back the time they currently spend…” Show full quote
“If it worked as promised, I'd kill 3-4 contracts — flagging, experimentation, analytics, maybe session replay — and get my data scientists back the time they currently spend stitching results together across tools. That's real money and real velocity if the Brex "50% reduction in time spent by data scientists" and Notion "30x experimentation velocity" numbers hold up under my own math”
Data Leader, SaaS · 5000+ employeessimulated “the product list (flags, experimentation, analytics, session replay) was concrete. What got vague was the quantified payoff sitting next to it: "consolidate your stack," "optimize at the speed…” Show full quote
“the product list (flags, experimentation, analytics, session replay) was concrete. What got vague was the quantified payoff sitting next to it: "consolidate your stack," "optimize at the speed of AI," and "industry's most generous free tier" are all puffery with no number attached”
Data Leader, SaaS · 5000+ employeessimulated “the hero line "Measure what ships. Ship what matters" plus "Test how each release — from features to AI-powered experiences — lands with customers" told me the problem…” Show full quote
“the hero line "Measure what ships. Ship what matters" plus "Test how each release — from features to AI-powered experiences — lands with customers" told me the problem (you ship stuff, you don't know if it worked) within the first screen.”
Product Manager, SaaS · 1001-5000 employeessimulated “It was fast — the header "Measure what ships. Ship what matters." plus the subhead about testing releases "so you can protect trust and optimize at the speed…” Show full quote
“It was fast — the header "Measure what ships. Ship what matters." plus the subhead about testing releases "so you can protect trust and optimize at the speed of AI" told me the problem in one breath: you ship features blind and need to know what actually lands with customers.”
Data Leader, SaaS · 5000+ employeessimulated Named case studies with numbers — Brex, Notion — are the proof respondents believed
3 of 15 · what worked
“The Brex case, "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools," is the kind of consequence that would actually move my budget…” Show full quote
“The Brex case, "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools," is the kind of consequence that would actually move my budget conversation, because it speaks to headcount and velocity, not just features.”
Director of Product, Software · 1001-5000 employeessimulated See all 4 comments
“The named customers (OpenAI, Notion, Brex, Ancestry) and specific before/after numbers like "70 to 600+ annual experiments" for Ancestry are what actually stuck with me, since that's a…” Show full quote
“The named customers (OpenAI, Notion, Brex, Ancestry) and specific before/after numbers like "70 to 600+ annual experiments" for Ancestry are what actually stuck with me, since that's a credible, checkable claim rather than marketing fluff.”
Director of Product, Software · 1001-5000 employeessimulated “Brex's "50% reduction in time spent by data scientists" plus "20% cost savings from consolidating analytics and experimentation tools," and Notion's "30x experimentation velocity" with "600+ features released…” Show full quote
“Brex's "50% reduction in time spent by data scientists" plus "20% cost savings from consolidating analytics and experimentation tools," and Notion's "30x experimentation velocity" with "600+ features released with Statsig flags." Those are specific, attributable, and map directly onto what I'd have to justify to my own CFO”
Product Manager, SaaS · 201-500 employeessimulated “If it worked as promised, I'd kill 3-4 contracts — flagging, experimentation, analytics, maybe session replay — and get my data scientists back the time they currently spend…” Show full quote
“If it worked as promised, I'd kill 3-4 contracts — flagging, experimentation, analytics, maybe session replay — and get my data scientists back the time they currently spend stitching results together across tools. That's real money and real velocity if the Brex "50% reduction in time spent by data scientists" and Notion "30x experimentation velocity" numbers hold up under my own math”
Data Leader, SaaS · 5000+ employeessimulated
Differentiation
The big scale metrics are discounted because no methodology or source is given
4 of 15
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of…” Show full quote
“"2.5 Billion unique monthly experiment subjects" and "99.99% infra uptime" with zero source or methodology are the kind of unsupported numbers that make me discount the rest of the page”
Data Leader, SaaS · 501-1000 employeessimulated See all 5 comments
“The "50% time efficiency" and "1+ trillion events processed" numbers have no methodology behind them, so I'd discount those”
Head of Product, Software · 5000+ employeessimulated “"1+ Trillion events processed per day," "2.5 Billion unique monthly experiment subjects," "<1ms post-init evaluation latency" — none of these say over what period, what counts as an…” Show full quote
“"1+ Trillion events processed per day," "2.5 Billion unique monthly experiment subjects," "<1ms post-init evaluation latency" — none of these say over what period, what counts as an "event," or how latency was measured”
Head of Product, Software · 5000+ employeessimulated “the product list (flags, experimentation, analytics, session replay) was concrete. What got vague was the quantified payoff sitting next to it: "consolidate your stack," "optimize at the speed…” Show full quote
“the product list (flags, experimentation, analytics, session replay) was concrete. What got vague was the quantified payoff sitting next to it: "consolidate your stack," "optimize at the speed of AI," and "industry's most generous free tier" are all puffery with no number attached”
Data Leader, SaaS · 5000+ employeessimulated “The Brex case study claiming "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools" is the kind of number that would matter to…” Show full quote
“The Brex case study claiming "+50% time efficiency gain" and "20% cost savings from consolidating analytics and experimentation tools" is the kind of number that would matter to me, but it's unsourced and context-free”
Head of Product, Software · 501-1000 employeessimulated No direct comparison against named competitors is offered
1 of 15
“the page doesn't do any direct comparison — the SoundCloud quote even namechecks "Optimizely, LaunchDarkly, Split, and Eppo" and just says they picked Statsig for "comprehensive end-to-end integration"…” Show full quote
“the page doesn't do any direct comparison — the SoundCloud quote even namechecks "Optimizely, LaunchDarkly, Split, and Eppo" and just says they picked Statsig for "comprehensive end-to-end integration" without saying why those others fell short”
Senior Product Manager, Technology · 501-1000 employeessimulated
Relevance
The page speaks to practitioners and skips what a budget owner needs to approve
2 of 15
“A line that names my actual budget owner problem — something like 'consolidate your experimentation spend and cut vendor count without losing analyst headcount' — plus a sourced…” Show full quote
“A line that names my actual budget owner problem — something like 'consolidate your experimentation spend and cut vendor count without losing analyst headcount' — plus a sourced TCO comparison against running Optimizely+LaunchDarkly+Amplitude separately”
Head of Product, Software · 5000+ employeessimulated See all 3 comments
“The tone is written for a technical IC or a VP of Eng/Data who gets excited by "npx @statsig/wizard@latest" and SDK lists for fourteen languages, not for someone…” Show full quote
“The tone is written for a technical IC or a VP of Eng/Data who gets excited by "npx @statsig/wizard@latest" and SDK lists for fourteen languages, not for someone in my seat who needs a business case.”
Head of Product, Software · 5000+ employeessimulated “I'd go in wanting to see the actual migration path and pricing at my scale, because "generous free tier" and case-study logos from OpenAI-scale companies don't tell me…” Show full quote
“I'd go in wanting to see the actual migration path and pricing at my scale, because "generous free tier" and case-study logos from OpenAI-scale companies don't tell me what this costs or how painful the cutover is for a 201-500 person company”
Product Manager, SaaS · 201-500 employeessimulated The target buyer is never stated — respondents had to infer it from logos and job titles
6 of 15
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs…” Show full quote
“The customer logos (OpenAI, Brex, Notion) and lines like "accelerate the speed at which we release new features" reinforce that it's for scaled product orgs, not solo devs or marketers”
Data Leader, SaaS · 501-1000 employeessimulated See all 5 comments
“the "who it's for" is inferred, not stated — there's no "built for Heads of Product at mid-size SaaS companies" line, I had to piece it together from…” Show full quote
“the "who it's for" is inferred, not stated — there's no "built for Heads of Product at mid-size SaaS companies" line, I had to piece it together from the logos (OpenAI, Notion, Brex) and testimonials”
Head of Product, Software · 5000+ employeessimulated “logos (OpenAI, Brex, Notion) and testimonials from Heads of Data, Eng Managers, and PMs make it obvious who this is pitched at”
Senior Product Manager, Technology · 501-1000 employeessimulated “The intended reader isn't stated in so many words like "for engineering and data teams," but it's obvious from the logos (OpenAI, Brex, Notion) and the language around…” Show full quote
“The intended reader isn't stated in so many words like "for engineering and data teams," but it's obvious from the logos (OpenAI, Brex, Notion) and the language around "data teams," "engineers," and "product teams" throughout the testimonials — so I had to infer the audience rather than read it in a sentence”
Director of Product, Software · 1001-5000 employeessimulated “the hero line "Measure what ships. Ship what matters" plus "Test how each release — from features to AI-powered experiences — lands with customers" told me the problem…” Show full quote
“the hero line "Measure what ships. Ship what matters" plus "Test how each release — from features to AI-powered experiences — lands with customers" told me the problem (you ship stuff, you don't know if it worked) within the first screen.”
Product Manager, SaaS · 1001-5000 employeessimulated
Clarity
The hero tagline and key terms don't land until the product tiles explain them
2 of 15
“"Measure what ships. Ship what matters." reads like a tagline built for rhythm, not clarity, and "test how each release... lands with customers" is soft verbs doing the…” Show full quote
“"Measure what ships. Ship what matters." reads like a tagline built for rhythm, not clarity, and "test how each release... lands with customers" is soft verbs doing the work a noun should do. I had to get past that to the product tile labels before I had actual nouns — flags, experiments, analytics”
Product Manager, SaaS · 201-500 employeessimulated See all 2 comments
“Mostly it was terms like "experiment," "velocity," and "AI-powered experiences" doing a lot of unexplained work — they're used as if everyone agrees on the definition, but an…” Show full quote
“Mostly it was terms like "experiment," "velocity," and "AI-powered experiences" doing a lot of unexplained work — they're used as if everyone agrees on the definition, but an experiment counted in a dashboard could mean a rigorous A/B test or a trivial flag toggle, and the page never draws that line.”
Director of Product, Software · 1001-5000 employeessimulated
05
How this works
Who we simulated (15 personas)
15 AI-simulated personas matched to your target market. Each answered independently, without seeing your goal, the scoring criteria, or each other’s answers. Attribution is role, industry and company size only.
Data LeaderSaaS · 501-1000 employeesUS
Growth LeadTechnology · 1001-5000 employeesUS
Head of ProductSoftware · 5000+ employeesUS
Product ManagerSaaS · 201-500 employeesUS
Senior Product ManagerTechnology · 501-1000 employeesUS
Director of ProductSoftware · 1001-5000 employeesUS
Data LeaderSaaS · 5000+ employeesUS
Growth LeadTechnology · 201-500 employeesUS
Head of ProductSoftware · 501-1000 employeesUS
Product ManagerSaaS · 1001-5000 employeesUS
Senior Product ManagerTechnology · 5000+ employeesUS
Director of ProductSoftware · 201-500 employeesUS
Data LeaderSaaS · 501-1000 employeesUS
Growth LeadTechnology · 1001-5000 employeesUS
Head of ProductSoftware · 5000+ employeesUS
Methodology
Every answer on this page was written by an AI model role-playing a buyer profile, scored on Wynter’s B2B Message Layers framework. The personas were sampled in code across role, industry, company size and behavioral traits; the model wrote only the answers. Scores arrive through fixed verdict categories and the counts are computed in our own code, so no number here was written by a model.
Score details: the count and the strength
The count is how many personas cleared the bar on each question. A yes can be unhesitating or come with reservations; the scorecard counts both as a yes, and this is the only place the difference is shown. Per layer:
- Clarity: 15 of 15, 5 without hesitation, 10 with reservations
- Relevance: 13 of 15, 1 without hesitation, 12 with reservations
- Value: 11 of 15, all with reservations
- Differentiation: 12 of 15, all with reservations
These answers are AI-simulated and directional. Validate anything you’re betting on with real buyers, your ICPs.
Your next 3 moves
- 1.Add a migration section after the product grid covering timeline, effort, and data import.
- 2.Add a baseline to each case study stat, such as the before figure for Brex's 50%.
- 3.Name the buyer in the hero subhead, such as product and data teams.
See what real buyers say.
A detailed, section-by-section message test report from verified B2B professionals who are actually in-market for what you sell.
Trusted by
RetentionThis report is kept for 60 days, until 27 Dec 2026, then deleted along with the personas, their answers and everything derived from them. The link stays live for that whole period so it can be shared or revisited, and stops working afterwards.
The email address it was requested from is kept beyond that, because it subscribes you to the newsletter — that was the price of the report. You can unsubscribe in one click from any issue, which stops the email without affecting a report still inside its 60 days. The public report page never shows the requester’s address.
The email address it was requested from is kept beyond that, because it subscribes you to the newsletter — that was the price of the report. You can unsubscribe in one click from any issue, which stops the email without affecting a report still inside its 60 days. The public report page never shows the requester’s address.