Public working demo
See how Funl turns activity into commercial evidence.
Illustrative sample data only. The screens below mirror capabilities already implemented in the protected Funl product.
Sample · Last 30 days
Journey
Leak detectedQuote to booking
Landing12,480
100%Service viewed8,310
67%Quote started4,210
34%Quote submitted2,860
23%Booked1,658
13%Primary leakage signalQuote submitted → Booked loses 42% of remaining high-intent visitors.
Funl diagnostic
What deserves attention?
01
Mobile paid-search traffic is under-converting.
High-value acquisition source, but booking completion trails desktop.
02
Quote completion is comparatively healthy.
The larger observed loss appears after submission, narrowing the next investigation.
INVESTIGATE NEXTCompare post-quote mobile behaviour with the current booking path.
Attribution
Which sources create value?
SourceRevenueConversionSignal
Paid Search$72,8106.2%Strong
Organic$51,4405.4%Stable
Direct$38,9004.1%Stable
Social$23,2702.0%Leak
Commercial anomalies
Notice unusual movement before guessing why it happened.
Funl compares the latest complete UTC day with the same weekday across prior weeks. It uses robust seasonal evidence to decide whether a move deserves investigation—without turning deviation into a diagnosis.
Same-weekday median 9,880 · -36.8% movement · robust z 4.21
Same-weekday median 4,760 · +3.2% movement · robust z 0.62
Not enough comparable history to score responsibly.
Same-weekday median $12,060 · +3.5% movement · robust z 0.48
Pageview evidence
InvestigateMeasured drop outside the reviewed seasonal guardrails.
6,240latest complete day
9,880same-weekday median
-36.8%relative movement
4.21robust z · not probability
7matching weekdays
What Funl knows
The latest complete day is materially below its comparable weekday baseline and clears the reviewed robust-deviation guardrails.
What Funl does not know from this signal alone: the root cause, whether the movement is causal, how much revenue is recoverable, or what action should be taken.
Recent context
PageviewsLast 14 complete days
9,720
8,210
8,460
8,150
7,310
6,940
9,880
10,020
8,540
8,420
8,170
7,480
7,010
6,240
Illustrative anomaly sample only. V1 scores complete UTC days against same-weekday history, requires enough comparable data, materialises zero-activity days, and favours silence when evidence is sparse.
Funnels
Define the customer journey, then inspect every step.
Funl supports saved 2–8 step page/event funnels, bounded conversion windows and optional event-property filters. This sample shows the commercial result, not raw identities.
1Landing12,480 visitors
100%
2Service viewed8,310 visitors
67%
Observed3Quote started4,210 visitors
34%
Observed4Quote submitted2,860 visitors
23%
32% step loss5Booked1,658 visitors
13%
42% step lossEvent steps can also be narrowed using the visual property-filter editor with equals, not-equals, contains and does-not-contain matching.
Journeys
See the paths customers actually take.
Visit-scoped journey analysis groups common page/event sequences without exposing raw pseudonymous session IDs.
01
/services→/plumbing→quote_started→quote_submitted
1,84002
/→/pricing→/contact→lead_submitted
1,32603
/services→/electrical→/reviews→quote_started
918Opportunities
Turn leakage into a ranked investigation queue.
Funl ranks observed funnel losses by dropped pseudonymous visitors and drop-off rate. It deliberately does not invent root cause or “recoverable revenue”.
Quote submitted → Booked
42% drop1,202 observed visitors lost between these steps
Observed evidence · investigate, do not assume causePaid Search mobile booking
31% below desktopHigh-value source with weaker completion after quote
Observed evidence · investigate, do not assume causeSocial lead quality
2.0% conversionTraffic is present but commercial completion remains weak
Observed evidence · investigate, do not assume causeRetention
See who comes back without durable person tracking.
Daily first-seen cohorts use Funl's rotating pseudonymous analytics identity. Cross-month retention may therefore undercount returns by design.
| Cohort | D0 | D1 | D7 | D14 |
|---|---|---|---|---|
| 01 Oct | 100% | 38% | 24% | 17% |
| 02 Oct | 100% | 41% | 26% | 18% |
| 03 Oct | 100% | 35% | 23% | — |
| 04 Oct | 100% | 39% | — | — |
Correlations
Find behaviours associated with conversion or drop-off.
Funl compares page/event-label prevalence between converted and drop-off sessions using bounded descriptive odds-ratio mechanics. Association is not causation or significance.
Observed behaviourOdds ratioInterpretationSignal
Viewed reviews before quote2.4×More common among convertersAssociated
Returned to pricing twice1.8×More common among drop-offsInvestigate
Used site search1.3×Slightly more common among convertersWeak
Interventions
Measure what changed after a commercial intervention.
Funl compares an equal-duration baseline with the observed period after a change. It measures; it does not allocate traffic, claim causality or choose a winner.
Shorter mobile booking form
Quote → booking+11.8%Observed change versus an equal pre-intervention baseline.
New finance explainer
Lead submission+4.6%Observed change versus an equal pre-intervention baseline.
Social landing-page rewrite
Tracked revenue-2.1%Observed change versus an equal pre-intervention baseline.
Real-user performance
See how real visitors experience the pages in your funnel.
Funl measures browser-observed LCP, INP, CLS, FCP and TTFB when performance collection is explicitly enabled. p75 is used as the primary comparison, and the evidence is never presented as proof that speed caused a conversion result.
Page evidence
Sample · p75Where experience deserves investigation
/booking246 measurements
Needs improvementLCP · 3.62 s/services/plumbing381 measurements
Needs improvementLCP · 2.74 s/pricing328 measurements
GoodLCP · 2.21 sPerformance is technical evidence to investigate alongside funnels and journeys. This sample does not claim the slower page caused the observed booking leakage.
Privacy-bounded heatmaps
See interaction density without recording the page itself.
Heatmaps are a separately controlled capability. Funl samples click coordinates and scroll-depth thresholds only—no DOM text, form values, selectors, screenshots or session-replay stream.
Click density
Illustrative sample/booking
1,126 sampled clicks across 284 sampled visits on this path.
The product view is an abstract coordinate canvas. Funl does not fetch or store a screenshot for this heatmap.
Scroll depth
/bookingHow far sampled visits reach
20%92%sampled visits reached
40%76%sampled visits reached
60%54%sampled visits reached
80%31%sampled visits reached
100%18%sampled visits reached
Privacy boundary
Heatmaps remain separate from full session replay. The current product does not record DOM snapshots or playback sessions.
Trusted outcomes
Connect browser behaviour to server-confirmed commercial outcomes.
Funl keeps trusted backend outcomes separate from ordinary browser analytics and preserves whether a server outcome was linked to an existing browser session.
OutcomeProvenanceBrowser linkValue
appointment_bookedTrusted serverLinked$320
quote_acceptedTrusted serverLinked$1,480
job_completedTrusted serverUnlinked$860
Evidence boundary
Unlinked server outcomes remain commercially useful, but Funl does not fabricate a browser journey or acquisition source for them.
The public tour is illustrative. The protected product computes these views from tenant-scoped evidence.
Funl already implements the analytics capabilities shown here. Authentication and tenant admission remain separate from the analytics product itself.
All values, company names and outcomes on this page are illustrative sample data. They are not customer results, testimonials or independent validation.