Diagnose and reverse sudden signup drops
Signups dropped 30% this week? Kaya cross-references traffic cohorts, page-level conversion, and recent deploys to isolate the culprit.
Direct Product Simulation
How Kaya executes on this exact play
All data below mirrors the exact logic and guardrails of the Kaya engine.
Crisis Diagnostic · 28% Drop in Signups
Governance enforced · Deterministic rules verified
28% drop over 5 rolling days
Google OAuth button on Safari Mobile returned HTTP 500
Desktop & Chrome normal (+2%)
Patch deployed · Back to baseline within 24h
Before / After
Why legacy approaches fall short
Dashboard panic without actionable explanation
Staring at a red chart without knowing if it's a broken form, an SEO penalty, or competitor action causes days of paralyzed debate.
- ✕Conflicting theories among founders and team
- ✕Hasty website tweaks that introduce further regressions
- ✕Silent signup form breakage discovered days late
Automated root-cause analysis and immediate remediation
Kaya decomposes traffic by landing page, device, and referrer, surfacing the exact point of divergence.
Form & Auth Health Checks
Validates that authentication, OAuth providers, and checkout flows respond cleanly.
Cohort & Device Segmentation
Isolates the drop: is it limited to Safari mobile, organic search, or a specific region?
Daily Growth Alert Brief
Proactive alert with verified cause and pre-staged corrective experiments.
The Growth Cycle
Kaya's 4-step growth loop
Understand → Decide → Experiment → Measure: no guesswork, no hallucinated prompts.
Anomaly Detection
Flags anomalies that deviate statistically from 4-week rolling baselines.
Hypothesis Elimination
Accounts for seasonality, holidays, and external platform outages first.
Remediation Experiment
Rolls back ungrounded copy changes or re-allocates budget to proven baseline channels.
Business Memory Update
Codifies the incident in business memory so future strategies avoid the same pitfall.
Your guardrails for this use case
The agent is never a black box. Every external mutation is governed by deterministic rules hard-coded in our policy engine.
Frequently asked questions
Everything you need to know to get started with this play.
How does Kaya separate random weekend dips from real issues?
Kaya compares day-of-week cohorts against 4-week baselines, filtering out normal weekend drops.
Discover other plays
Every way to grow with Kaya
Initial Acquisition
Get your first 100 customers without burning ad budget
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Win high-intent competitor comparison searches
Paid Acquisition
Scale paid search with immutable budget guardrails
Your next chapter
You build. What comes next grows with Kaya.
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From the first signal to the next milestone.