{{-- ── Overview KPI Cards ─────────────────────────────────── --}}

Overall Risk Score ⓘ

@if($loading)

@else {{ $avgProbability }}% @endif

Stable vs last run

High Risk Predictions ⓘ

@if($loading)

@else {{ $highRiskCount }} @endif

Actionable targets remaining

Primary Risk Factor ⓘ

@if($loading)

@else {{ $primaryFactor }} @endif

Dominant ML factor

Model Accuracy ⓘ

@if($loading)

@else {{ $modelAccuracy }}% @endif

Latest active validation

{{-- ── Model Registry Panel ────────────────────────────────── --}} @if(!$loading && !empty($modelStats))

AI Model Performance Registry ⓘ

@foreach([ ['label' => 'Accuracy', 'val' => $modelStats['accuracy']], ['label' => 'Precision', 'val' => $modelStats['precision']], ['label' => 'Recall', 'val' => $modelStats['recall']], ['label' => 'F1 Score', 'val' => $modelStats['f1']], ['label' => 'AUC-ROC', 'val' => $modelStats['auc']], ['label' => 'Version', 'val' => $modelStats['version']], ] as $stat)

@endforeach

@foreach([ ['label' => 'Training Samples', 'val' => $modelStats['samples']], ['label' => 'Positive Cases', 'val' => $modelStats['positives']], ['label' => 'Negative Cases', 'val' => $modelStats['negatives']], ['label' => 'Feature Columns', 'val' => $modelStats['features']], ['label' => 'Last Trained', 'val' => $modelStats['trained_at']], ] as $stat)

@endforeach

@endif {{-- ── Panels Row ──────────────────────────────────────────── --}}

Probability Distribution ⓘ

High (>80%)

@if($loading)

@else

@endif

@if($loading)

@else {{ $probHigh }} @endif

Medium (50-80%)

@if($loading)

@else

@endif

@if($loading)

@else {{ $probMed }} @endif

Low (<50%)

@if($loading)

@else

@endif

@if($loading)

@else {{ $probLow }} @endif

AI Pipeline Metrics ⓘ

Feature Store Integrity 99.8% OK

Last Inference Run {{ now()->subMinutes(12)->format('d M Y H:i A') }}

Next Scheduled Run {{ now()->addHours(23)->format('d M Y H:i A') }}

Model Engine Status ONLINE

Version Endpoint LightGBM v2.4

{{-- ── Subscription Expiry vs AI Risk ─────────────────────── --}}

Subscription Expiry vs. AI Risk ⓘ

@foreach([ ['label' => 'Expires in < 7 Days', 'val' => $e7, 'hi' => $e7h, 'pct' => $e7p, 'clr' => '#ef4444'], ['label' => 'Expires in 7 - 30 Days', 'val' => $e30, 'hi' => $e30h, 'pct' => $e30p, 'clr' => '#f59e0b'], ['label' => 'Expires in 30 - 45 Days','val' => $e45, 'hi' => $e45h, 'pct' => $e45p, 'clr' => '#f59e0b'], ['label' => 'Expires > 45 Days', 'val' => $e90, 'hi' => $e90h, 'pct' => $e90p, 'clr' => '#10b981'], ] as $bucket)

@if($loading)

@else {{ $bucket['val'] }} @endif

@if($loading)

@else

@endif

@if($loading)

@else {{ $bucket['hi'] }} High Risk ({{ $bucket['pct'] }}%) @endif

@endforeach

{{-- ═══════════════════════════════════════════════════════════ --}} {{-- WIN-BACK INTELLIGENCE SECTION --}} {{-- ═══════════════════════════════════════════════════════════ --}}

Win-Back Intelligence

Subscribers who have previously churned and may be re-engaged

Total Expired ⓘ

@if($loading)

@else {{ $totalExpired }} @endif

Historical subscription drop-offs

Win-Back Eligible ⓘ

@if($loading)

@else {{ $winBackEligible }} @endif

Priority reactivation targets

Already Won Back ⓘ

@if($loading)

@else {{ $alreadyWonBack }} @endif

Successfully reactivated

AI Win-Back Score ⓘ

Powered by your LightGBM model. Subscribers with a low churn score who still left are your easiest win-backs — they were loyal but left for situational reasons.

High (≤ 30% churn)

@if($loading)

@else

@endif

@if($loading)

@else {{ $wbScoreHigh }} @endif

Med (31–60% churn)

@if($loading)

@else

@endif

@if($loading)

@else {{ $wbScoreMed }} @endif

Low (> 60% churn)

@if($loading)

@else

@endif

@if($loading)

@else {{ $wbScoreLow }} @endif

Reactivation Window ⓘ

Time since subscription expired. Subscribers churned within 30 days are your hottest win-back targets.

@foreach([ ['cls' => 'wb-urgent', 'badge' => '🔥 Urgent', 'label' => 'Churned < 30 Days', 'val' => $r30, 'sub' => 'Highest win-back rate'], ['cls' => 'wb-warm', 'badge' => '⚡ Warm', 'label' => '31 – 60 Days Ago', 'val' => $r60, 'sub' => 'Still receptive'], ['cls' => 'wb-cool', 'badge' => '💤 Cooling', 'label' => '61 – 90 Days Ago', 'val' => $r90, 'sub' => 'Needs strong offer'], ['cls' => 'wb-cold', 'badge' => '❄️ Cold', 'label' => '> 90 Days Ago', 'val' => $rOld, 'sub' => 'Lower priority'], ] as $win)

@if($loading)

@else {{ $win['val'] }} @endif

@endforeach

AI Churn Management