LinkedIn Ads Metrics Hierarchy:
═══════════════════════════════════════════════════════════════════════════

REACH METRICS                    ENGAGEMENT METRICS
─────────────────                ───────────────────
├── Impressions                  ├── Clicks (All)
│   └── Unique per member        │   ├── Link Clicks
├── Reach                        │   ├── Social Actions
│   └── Deduplicated             │   └── Other Clicks
├── Frequency                    ├── Engagement Rate
│   └── Impressions/Reach        │   └── Engagements/Impressions
└── Share of Voice               ├── CTR (Click-Through Rate)
    └── Your Impr/Total          │   └── Clicks/Impressions
                                 └── Video Views (25%, 50%, 75%, 100%)

COST METRICS                     CONVERSION METRICS
─────────────────                ─────────────────────
├── Spend                        ├── Conversions
│   └── Total investment         │   ├── Lead Gen Submits
├── CPM                          │   ├── Website Conversions
│   └── Cost per 1,000 impr      │   └── Offline Conversions
├── CPC                          ├── Conversion Rate
│   └── Cost per click           │   └── Conversions/Clicks
├── CPL                          ├── ROAS
│   └── Cost per lead            │   └── Revenue/Spend
└── CPA                          └── Pipeline Influenced
    └── Cost per acquisition         └── Deals touched

// Performance Analysis Framework
interface LinkedInMetrics {
  impressions: number;
  clicks: number;
  conversions: number;
  spend: number;
  reach: number;
}

function analyzePerformance(metrics: LinkedInMetrics) {
  const {
    impressions,
    clicks,
    conversions,
    spend,
    reach,
  } = metrics;

  // Calculate core metrics
  const ctr = (clicks / impressions) * 100;
  const conversionRate = (conversions / clicks) * 100;
  const cpc = spend / clicks;
  const cpm = (spend / impressions) * 1000;
  const cpa = spend / conversions;
  const frequency = impressions / reach;

  // Efficiency scoring
  const efficiencyScore = calculateEfficiencyScore({
    ctr,
    conversionRate,
    cpa,
    frequency,
  });

  return {
    metrics: { ctr, conversionRate, cpc, cpm, cpa, frequency },
    efficiency: efficiencyScore,
    recommendations: generateRecommendations({
      ctr,
      conversionRate,
      frequency,
      cpa,
    }),
  };
}

function generateRecommendations(metrics: {
  ctr: number;
  conversionRate: number;
  frequency: number;
  cpa: number;
}): string[] {
  const recommendations: string[] = [];

  // CTR analysis
  if (metrics.ctr < 0.3) {
    recommendations.push(
      'Low CTR: Test new creative, headlines, or targeting'
    );
  } else if (metrics.ctr > 1.0) {
    recommendations.push(
      'High CTR: Consider expanding audience size'
    );
  }

  // Conversion rate analysis
  if (metrics.conversionRate < 5 && metrics.ctr > 0.5) {
    recommendations.push(
      'High CTR but low conversions: Optimize landing page or form'
    );
  }

  // Frequency analysis
  if (metrics.frequency > 5) {
    recommendations.push(
      'High frequency: Expand audience or refresh creative'
    );
  }

  return recommendations;
}

LinkedIn Bidding Strategy Selection:
═══════════════════════════════════════════════════════════════════════════

┌─────────────────────────────────────────────────────────────────────────┐
│                      BIDDING STRATEGY DECISION TREE                     │
└─────────────────────────────────────────────────────────────────────────┘

                            Campaign Phase?
                                  │
              ┌───────────────────┼───────────────────┐
              ▼                   ▼                   ▼
          Learning            Scaling           Efficiency
              │                   │                   │
              ▼                   ▼                   ▼
       ┌──────────┐        ┌──────────┐        ┌──────────┐
       │ Maximum  │        │  Cost    │        │  Manual  │
       │ Delivery │        │   Cap    │        │ Bidding  │
       └──────────┘        └──────────┘        └──────────┘
              │                   │                   │
              ▼                   ▼                   ▼
         Auto-bid           Target CPA          Fixed bid
       Maximize spend     Stay under cap      Full control
       Best for data      Best for scaling    Best for pros


STRATEGY COMPARISON:
────────────────────────────────────────────────────────────────────
Strategy          Control   Efficiency   Learning     Best For
────────────────────────────────────────────────────────────────────
Maximum Delivery    Low        Low        Fast        New campaigns
Cost Cap           Medium     High        Medium      Scaling
Manual CPC/CPM      High      Medium       Slow       Experienced
Enhanced CPC       Medium     Medium       Fast       Conversions
────────────────────────────────────────────────────────────────────

// Maximum Delivery Strategy Implementation
const maximumDeliveryStrategy = {
  name: 'Maximum Delivery',

  // When to use
  useCases: [
    'New campaign launch',
    'Testing new audiences',
    'Awareness objectives',
    'Limited historical data',
    'Time-sensitive campaigns',
  ],

  // Configuration
  settings: {
    bidStrategy: 'MAXIMUM_DELIVERY',
    optimizationGoal: 'LANDING_PAGE_CLICKS', // or IMPRESSIONS, LEADS
    dailyBudget: 150, // Higher budget speeds learning
  },

  // Success criteria to transition
  exitCriteria: {
    minimumConversions: 50,
    minimumDays: 7,
    stableCPA: true,
    sufficientVolume: true,
  },

  // Monitoring thresholds
  alerts: {
    cpaVariance: 0.30, // Alert if CPA varies >30%
    deliveryPacing: 0.80, // Alert if <80% budget delivered
    frequencyCap: 4, // Alert if frequency exceeds 4
  },
};

// Transition logic
function shouldTransitionFromMaxDelivery(
  campaignData: CampaignMetrics
): { transition: boolean; reason: string; targetStrategy: string } {
  const { conversions, days, cpaVariance, avgCPA } = campaignData;

  if (conversions >= 50 && days >= 7 && cpaVariance < 0.2) {
    return {
      transition: true,
      reason: 'Sufficient data collected with stable CPA',
      targetStrategy: 'COST_CAP',
    };
  }

  if (days >= 14 && conversions < 20) {
    return {
      transition: true,
      reason: 'Low conversion volume - consider Manual bidding',
      targetStrategy: 'MANUAL_CPC',
    };
  }

  return {
    transition: false,
    reason: 'Continue collecting data',
    targetStrategy: 'MAXIMUM_DELIVERY',
  };
}

// Cost Cap Strategy Implementation
interface CostCapStrategy {
  targetCPA: number;
  flexibility: 'STRICT' | 'FLEXIBLE';
  rampUpPeriod: number;
}

function implementCostCap(
  historicalCPA: number,
  targetEfficiency: number // 0.8 = 20% improvement target
): CostCapStrategy {
  // Start conservative, then optimize
  const phases = {
    initial: {
      // Start 10-20% above historical CPA
      targetCPA: historicalCPA * 1.15,
      flexibility: 'FLEXIBLE' as const,
      rampUpPeriod: 7, // days
    },

    optimization: {
      // Gradually lower to target
      targetCPA: historicalCPA * targetEfficiency,
      flexibility: 'STRICT' as const,
      rampUpPeriod: 0,
    },
  };

  return phases.initial;
}

// Cost Cap adjustment schedule
const costCapOptimization = {
  schedule: [
    { week: 1, adjustment: 1.15, note: 'Start above historical CPA' },
    { week: 2, adjustment: 1.10, note: 'Slight reduction if stable' },
    { week: 3, adjustment: 1.05, note: 'Approaching target' },
    { week: 4, adjustment: 1.00, note: 'At target CPA' },
    { week: 5, adjustment: 0.95, note: 'Push for efficiency' },
    { week: 6, adjustment: 0.90, note: 'Maximum efficiency' },
  ],

  rules: {
    // Only reduce if hitting >80% of daily budget
    deliveryThreshold: 0.80,
    // Only reduce if CPA is at or below cap
    cpaThreshold: 1.05,
    // Minimum conversions per week to reduce
    volumeThreshold: 15,
  },
};

// Manual Bid Calculator
function calculateOptimalManualBid(
  conversionValue: number,
  targetROAS: number,
  expectedCTR: number,
  expectedConversionRate: number
): { recommendedCPC: number; cpmEquivalent: number } {
  // Maximum CPC based on unit economics
  const maxCPC = (conversionValue / targetROAS) * expectedConversionRate;

  // Apply safety margin (bid 70-80% of max)
  const recommendedCPC = maxCPC * 0.75;

  // Calculate CPM equivalent for comparison
  const cpmEquivalent = recommendedCPC * expectedCTR * 1000;

  return {
    recommendedCPC: Math.round(recommendedCPC * 100) / 100,
    cpmEquivalent: Math.round(cpmEquivalent * 100) / 100,
  };
}

// Example calculation
const bidCalculation = calculateOptimalManualBid(
  500,   // Average deal value influenced by LinkedIn
  5,     // Target 5x ROAS
  0.005, // 0.5% expected CTR
  0.10   // 10% expected conversion rate
);

// Result: { recommendedCPC: 7.50, cpmEquivalent: 37.50 }

LinkedIn Learning Phase Requirements:
═══════════════════════════════════════════════════════════════════════════

LEARNING PHASE MECHANICS:
─────────────────────────
• Duration: ~15 conversions or 7 days (whichever first)
• Algorithm explores audience to find optimal delivery
• Performance may fluctuate significantly
• Changes restart learning phase

               ┌─────────────────────────────────────────────┐
               │           LEARNING PHASE TIMELINE           │
               └─────────────────────────────────────────────┘

Day 1-3     Day 4-7      Day 8-14     Day 15+
  │           │            │            │
  ▼           ▼            ▼            ▼
┌─────┐    ┌─────┐      ┌─────┐      ┌─────┐
│High │    │Mod. │      │Low  │      │Min  │
│Var. │───▶│Var. │─────▶│Var. │─────▶│Var. │
│     │    │     │      │     │      │     │
└─────┘    └─────┘      └─────┘      └─────┘
                                         │
                        STABLE ◄─────────┘


ACTIONS THAT RESTART LEARNING:
──────────────────────────────
✗ Changing bid strategy
✗ Changing optimization goal
✗ Budget change >30%
✗ Significant audience changes
✗ Pausing for >7 days

SAFE CHANGES (DON'T RESTART):
─────────────────────────────
✓ Small budget increases (<20%)
✓ Adding new ads to campaign
✓ Minor bid adjustments
✓ Schedule changes
✓ Name changes

// Budget Allocation by Funnel Stage
interface BudgetAllocation {
  awareness: number;
  consideration: number;
  conversion: number;
  retention: number;
}

function allocateBudget(
  totalBudget: number,
  businessStage: 'launch' | 'growth' | 'mature',
  primaryGoal: 'awareness' | 'leads' | 'revenue'
): BudgetAllocation {
  const allocationModels: Record<
    string,
    Record<string, BudgetAllocation>
  > = {
    launch: {
      awareness: { awareness: 0.60, consideration: 0.25, conversion: 0.10, retention: 0.05 },
      leads: { awareness: 0.40, consideration: 0.35, conversion: 0.20, retention: 0.05 },
      revenue: { awareness: 0.30, consideration: 0.30, conversion: 0.35, retention: 0.05 },
    },
    growth: {
      awareness: { awareness: 0.40, consideration: 0.30, conversion: 0.20, retention: 0.10 },
      leads: { awareness: 0.25, consideration: 0.35, conversion: 0.30, retention: 0.10 },
      revenue: { awareness: 0.20, consideration: 0.30, conversion: 0.35, retention: 0.15 },
    },
    mature: {
      awareness: { awareness: 0.30, consideration: 0.30, conversion: 0.25, retention: 0.15 },
      leads: { awareness: 0.20, consideration: 0.30, conversion: 0.35, retention: 0.15 },
      revenue: { awareness: 0.15, consideration: 0.25, conversion: 0.40, retention: 0.20 },
    },
  };

  const model = allocationModels[businessStage][primaryGoal];

  return {
    awareness: Math.round(totalBudget * model.awareness),
    consideration: Math.round(totalBudget * model.consideration),
    conversion: Math.round(totalBudget * model.conversion),
    retention: Math.round(totalBudget * model.retention),
  };
}

// Example: $10,000/month for growth-stage company focused on leads
const allocation = allocateBudget(10000, 'growth', 'leads');
// Result: { awareness: 2500, consideration: 3500, conversion: 3000, retention: 1000 }

LinkedIn Optimal Delivery Times (B2B):
═══════════════════════════════════════════════════════════════════════════

ENGAGEMENT HEATMAP BY DAY/HOUR:
───────────────────────────────────────────────────────────────────────────
Hour    Mon    Tue    Wed    Thu    Fri    Sat    Sun
───────────────────────────────────────────────────────────────────────────
6-8am    ▓▓     ▓▓     ▓▓     ▓▓     ▓▓     ░░     ░░
8-10am   ▓▓▓▓   ▓▓▓▓   ▓▓▓▓   ▓▓▓▓   ▓▓▓    ░░     ░░
10-12pm  ▓▓▓▓▓  ▓▓▓▓▓  ▓▓▓▓▓  ▓▓▓▓▓  ▓▓▓    ░░     ░░
12-2pm   ▓▓▓    ▓▓▓    ▓▓▓    ▓▓▓    ▓▓     ░░     ░░
2-4pm    ▓▓▓▓   ▓▓▓▓   ▓▓▓▓   ▓▓▓▓   ▓▓     ░░     ░░
4-6pm    ▓▓▓    ▓▓▓    ▓▓▓    ▓▓▓    ▓▓     ░░     ░░
6-8pm    ▓▓     ▓▓     ▓▓     ▓▓     ░░     ░░     ▓▓
8-10pm   ▓      ▓      ▓      ▓      ░░     ░░     ▓

▓▓▓▓▓ = Peak     ▓▓▓ = High     ▓▓ = Medium     ▓ = Low     ░░ = Minimal


RECOMMENDED SCHEDULE BY OBJECTIVE:
──────────────────────────────────────────────────────────────────────────
Objective           Days              Hours (Local)      Bid Adjust
──────────────────────────────────────────────────────────────────────────
Lead Generation     Tue-Thu           9am-12pm          +15-20%
Brand Awareness     Mon-Fri           8am-6pm            Standard
Website Traffic     Mon-Thu           10am-3pm          +10-15%
Event Promotion     Tue-Wed           11am-1pm          +20-25%
Job Applicants      Tue-Thu           7am-9am, 5-7pm    +10-15%
──────────────────────────────────────────────────────────────────────────

// Budget Pacing Monitor
interface PacingConfig {
  dailyBudget: number;
  monthlyBudget: number;
  pacingType: 'STANDARD' | 'ACCELERATED';
}

function analyzePacing(
  config: PacingConfig,
  currentSpend: number,
  daysElapsed: number,
  daysInMonth: number
): {
  status: 'UNDERPACING' | 'ON_TRACK' | 'OVERPACING';
  projectedSpend: number;
  recommendedAction: string;
} {
  const expectedSpend = (config.monthlyBudget / daysInMonth) * daysElapsed;
  const spendRatio = currentSpend / expectedSpend;
  const dailyRunRate = currentSpend / daysElapsed;
  const projectedSpend = dailyRunRate * daysInMonth;

  let status: 'UNDERPACING' | 'ON_TRACK' | 'OVERPACING';
  let recommendedAction: string;

  if (spendRatio < 0.85) {
    status = 'UNDERPACING';
    recommendedAction = `
      Current pacing: ${(spendRatio * 100).toFixed(0)}% of target.
      Actions to consider:
      1. Increase bids by 15-25%
      2. Expand audience targeting
      3. Reduce targeting restrictions
      4. Add more ad variations
      5. Consider accelerated delivery
    `;
  } else if (spendRatio > 1.15) {
    status = 'OVERPACING';
    recommendedAction = `
      Current pacing: ${(spendRatio * 100).toFixed(0)}% of target.
      Actions to consider:
      1. Reduce bids by 10-15%
      2. Narrow audience targeting
      3. Add negative exclusions
      4. Switch to standard delivery
      5. Implement day-parting
    `;
  } else {
    status = 'ON_TRACK';
    recommendedAction = 'Pacing is optimal. Continue monitoring.';
  }

  return { status, projectedSpend, recommendedAction };
}

LinkedIn A/B Testing Framework:
═══════════════════════════════════════════════════════════════════════════

TEST PRIORITY MATRIX:
─────────────────────────────────────────────────────────────────────
Element            Impact    Ease    Priority    Min. Duration
─────────────────────────────────────────────────────────────────────
Headline           High      Easy    1           2 weeks
Value Proposition  High      Medium  2           2-3 weeks
Creative Image     High      Medium  2           2 weeks
Audience           High      Hard    3           3-4 weeks
Ad Format          Medium    Easy    4           2 weeks
CTA Button         Medium    Easy    5           2 weeks
Intro Text         Medium    Easy    5           2 weeks
Landing Page       High      Hard    3           3-4 weeks
─────────────────────────────────────────────────────────────────────

STATISTICAL SIGNIFICANCE REQUIREMENTS:
──────────────────────────────────────────────────────────────────────

                    ┌────────────────────────────────────┐
                    │     SAMPLE SIZE CALCULATOR         │
                    └────────────────────────────────────┘

For 95% confidence, 80% power:

                    Baseline       Minimum Detectable     Required
                    Conv. Rate     Effect (MDE)           Sample
                    ───────────────────────────────────────────────
                    5%             20% relative           3,900/variant
                    10%            20% relative           1,750/variant
                    15%            20% relative           1,050/variant
                    5%             30% relative           1,750/variant
                    10%            30% relative           780/variant
                    15%            30% relative           470/variant


TEST STRUCTURE:
───────────────
                    Campaign Group: [Test Name]
                           │
          ┌────────────────┼────────────────┐
          ▼                ▼                ▼
    Campaign A       Campaign B       Campaign C
    (Control)        (Variant 1)      (Variant 2)
          │                │                │
          ▼                ▼                ▼
    Same audience    Same audience    Same audience
    Same budget      Same budget      Same budget
    Original ad      Test creative    Test creative

// A/B Test Configuration
interface ABTestConfig {
  testName: string;
  testType: 'CREATIVE' | 'AUDIENCE' | 'BIDDING' | 'LANDING_PAGE';
  variants: TestVariant[];
  allocation: number[]; // Traffic split per variant
  primaryMetric: string;
  minimumSampleSize: number;
  maximumDuration: number; // days
}

interface TestVariant {
  id: string;
  name: string;
  changes: Record<string, string>;
}

// Creative Testing Framework
const creativeTestFramework = {
  headlineTests: {
    variations: [
      { type: 'benefit_focused', example: 'Cut Your CAC by 40%' },
      { type: 'pain_point', example: 'Struggling with Lead Quality?' },
      { type: 'social_proof', example: '10,000+ Companies Trust Us' },
      { type: 'curiosity', example: 'The Secret to B2B Success' },
      { type: 'question', example: 'Ready to Scale Your Pipeline?' },
    ],
    bestPractices: [
      'Test one headline style at a time',
      'Keep body copy identical',
      'Run for minimum 2 weeks',
      'Need 100+ conversions per variant',
    ],
  },

  imageTests: {
    variations: [
      { type: 'product_screenshot', impact: 'High for SaaS' },
      { type: 'human_faces', impact: 'High for services' },
      { type: 'data_visualization', impact: 'Medium-High' },
      { type: 'illustration', impact: 'Medium' },
      { type: 'abstract', impact: 'Low' },
    ],
    bestPractices: [
      'Test imagery style first, then specific images',
      'Use consistent branding across variants',
      'Test mobile vs desktop optimized',
      'Consider LinkedIn feed context',
    ],
  },
};

// Statistical Significance Calculator
function calculateSignificance(
  controlConversions: number,
  controlSample: number,
  variantConversions: number,
  variantSample: number
): {
  controlRate: number;
  variantRate: number;
  lift: number;
  pValue: number;
  isSignificant: boolean;
  confidence: number;
} {
  const controlRate = controlConversions / controlSample;
  const variantRate = variantConversions / variantSample;
  const lift = ((variantRate - controlRate) / controlRate) * 100;

  // Calculate pooled standard error
  const pooledRate = (controlConversions + variantConversions) /
                     (controlSample + variantSample);
  const se = Math.sqrt(
    pooledRate * (1 - pooledRate) *
    (1 / controlSample + 1 / variantSample)
  );

  // Calculate z-score and p-value
  const zScore = (variantRate - controlRate) / se;
  const pValue = 2 * (1 - normalCDF(Math.abs(zScore)));

  return {
    controlRate: Math.round(controlRate * 10000) / 100,
    variantRate: Math.round(variantRate * 10000) / 100,
    lift: Math.round(lift * 100) / 100,
    pValue: Math.round(pValue * 1000) / 1000,
    isSignificant: pValue < 0.05,
    confidence: Math.round((1 - pValue) * 100),
  };
}

function normalCDF(x: number): number {
  const a1 =  0.254829592;
  const a2 = -0.284496736;
  const a3 =  1.421413741;
  const a4 = -1.453152027;
  const a5 =  1.061405429;
  const p  =  0.3275911;

  const sign = x < 0 ? -1 : 1;
  x = Math.abs(x) / Math.sqrt(2);

  const t = 1.0 / (1.0 + p * x);
  const y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.exp(-x * x);

  return 0.5 * (1.0 + sign * y);
}

// Audience Test Structure
interface AudienceTest {
  baseAudience: AudienceTargeting;
  testVariants: AudienceVariant[];
  isolationMethod: 'CAMPAIGN_SPLIT' | 'AUDIENCE_EXCLUSION';
}

interface AudienceVariant {
  name: string;
  targeting: Partial<AudienceTargeting>;
  hypothesis: string;
}

const audienceTestExamples: AudienceTest[] = [
  {
    // Seniority Level Test
    baseAudience: {
      jobFunctions: ['Information Technology'],
      companySize: ['51-200', '201-500'],
    },
    testVariants: [
      {
        name: 'IC Contributors',
        targeting: { seniorities: ['Entry', 'Senior'] },
        hypothesis: 'Lower CPL, lower quality leads',
      },
      {
        name: 'Managers',
        targeting: { seniorities: ['Manager'] },
        hypothesis: 'Moderate CPL, good conversion',
      },
      {
        name: 'Directors+',
        targeting: { seniorities: ['Director', 'VP', 'CXO'] },
        hypothesis: 'Higher CPL, higher deal value',
      },
    ],
    isolationMethod: 'CAMPAIGN_SPLIT',
  },
  {
    // Company Size Test
    baseAudience: {
      jobTitles: ['Marketing Manager', 'Marketing Director'],
      seniorities: ['Manager', 'Director'],
    },
    testVariants: [
      {
        name: 'SMB',
        targeting: { companySize: ['11-50', '51-200'] },
        hypothesis: 'Faster sales cycle, lower ACV',
      },
      {
        name: 'Mid-Market',
        targeting: { companySize: ['201-500', '501-1000'] },
        hypothesis: 'Balanced metrics',
      },
      {
        name: 'Enterprise',
        targeting: { companySize: ['1001-5000', '5001-10000', '10001+'] },
        hypothesis: 'Longer cycle, higher ACV',
      },
    ],
    isolationMethod: 'CAMPAIGN_SPLIT',
  },
];

Horizontal Scaling Framework:
═══════════════════════════════════════════════════════════════════════════

EXPANSION PRIORITY ORDER:
─────────────────────────────────────────────────────────────────────────
Priority   Expansion Type              Risk    Volume Impact    CPA Impact
─────────────────────────────────────────────────────────────────────────
1          Lookalike Audiences         Low     High (+50-100%)  +5-15%
2          Adjacent Job Functions      Low     Medium (+30%)    +10-20%
3          Additional Seniorities      Medium  Medium (+40%)    +15-25%
4          New Company Sizes           Medium  High (+50%)      +10-30%
5          Geographic Expansion        Medium  Variable         Variable
6          New Industries              High    High (+60%)      +20-40%
─────────────────────────────────────────────────────────────────────────


LOOKALIKE EXPANSION STRATEGY:
────────────────────────────────────────────────────────────────────────

Source Audience              Lookalike Type         Expected Quality
────────────────────────────────────────────────────────────────────────
Customers                    Similar members        Highest
Lead Gen Form Submits        Contact targeting      High
Website Visitors (Conv.)     Similar members        High
Website Visitors (All)       Similar members        Medium
Email List (Engaged)         Contact targeting      Medium-High
Email List (All)             Contact targeting      Medium
────────────────────────────────────────────────────────────────────────

                    ┌─────────────────────────────────────┐
                    │     EXPANSION SEQUENCE DIAGRAM      │
                    └─────────────────────────────────────┘

            Week 1-2           Week 3-4           Week 5-6
               │                  │                  │
               ▼                  ▼                  ▼
         ┌──────────┐      ┌──────────┐      ┌──────────┐
         │ Core     │ ───▶ │ + Look-  │ ───▶ │ + Adj.   │
         │ Audience │      │ alikes   │      │ Functions│
         │          │      │          │      │          │
         │ 100K     │      │ 250K     │      │ 400K     │
         └──────────┘      └──────────┘      └──────────┘
               │                  │                  │
            Watch:            Watch:            Watch:
         - CPA stable      - CPA +10-15%     - CPA +15-25%
         - CTR >0.5%       - CTR >0.4%       - CTR >0.35%

// Vertical Scaling Implementation
interface ScalingDecision {
  currentBudget: number;
  recommendedIncrease: number;
  rationale: string;
  riskLevel: 'LOW' | 'MEDIUM' | 'HIGH';
  conditions: string[];
}

function calculateVerticalScaling(
  currentMetrics: CampaignMetrics,
  targetMetrics: TargetMetrics
): ScalingDecision {
  const {
    currentBudget,
    avgCPA,
    conversionVolume,
    daysStable,
    audienceSize,
    impressionShare,
  } = currentMetrics;

  // Calculate headroom
  const cpaHeadroom = (targetMetrics.maxCPA - avgCPA) / targetMetrics.maxCPA;
  const volumeUtilization = conversionVolume / targetMetrics.targetConversions;
  const reachSaturation = impressionShare;

  let recommendedIncrease: number;
  let rationale: string;
  let riskLevel: 'LOW' | 'MEDIUM' | 'HIGH';
  const conditions: string[] = [];

  // Decision logic
  if (cpaHeadroom > 0.3 && reachSaturation < 0.4 && daysStable >= 7) {
    // Strong performance, room to grow
    recommendedIncrease = 0.30; // 30% increase
    rationale = 'Strong CPA headroom with low saturation';
    riskLevel = 'LOW';
    conditions.push('Monitor CPA daily for first 5 days');
  } else if (cpaHeadroom > 0.15 && reachSaturation < 0.6 && daysStable >= 7) {
    // Moderate headroom
    recommendedIncrease = 0.20; // 20% increase
    rationale = 'Moderate headroom available';
    riskLevel = 'MEDIUM';
    conditions.push('Reduce if CPA increases >15%');
  } else if (cpaHeadroom > 0 && reachSaturation < 0.8) {
    // Limited headroom
    recommendedIncrease = 0.10; // 10% increase
    rationale = 'Limited headroom, cautious scaling';
    riskLevel = 'MEDIUM';
    conditions.push('Consider horizontal expansion first');
  } else {
    // No headroom or high saturation
    recommendedIncrease = 0;
    rationale = reachSaturation >= 0.8
      ? 'Audience saturation detected - expand horizontally'
      : 'CPA at or above target - optimize before scaling';
    riskLevel = 'HIGH';
    conditions.push('Focus on efficiency improvements');
  }

  return {
    currentBudget,
    recommendedIncrease: Math.round(currentBudget * recommendedIncrease),
    rationale,
    riskLevel,
    conditions,
  };
}

// Scaling safeguards
const scalingSafeguards = {
  rules: [
    {
      name: 'Stability Check',
      condition: 'daysStable >= 7',
      action: 'BLOCK_SCALING',
      message: 'Campaign must be stable for 7+ days',
    },
    {
      name: 'Volume Check',
      condition: 'weeklyConversions >= 15',
      action: 'BLOCK_SCALING',
      message: 'Need 15+ conversions/week before scaling',
    },
    {
      name: 'CPA Guard',
      condition: 'cpa > maxCPA * 1.2',
      action: 'REDUCE_BUDGET',
      message: 'CPA 20% above target - reducing spend',
    },
    {
      name: 'Frequency Guard',
      condition: 'frequency > 6',
      action: 'PAUSE_SCALING',
      message: 'High frequency - expand audience first',
    },
  ],

  autoActions: {
    cpaSpike: 'Reduce budget by 20% if CPA increases 30%+',
    deliveryDrop: 'Alert if delivery drops below 60% of budget',
    frequencyAlert: 'Alert when frequency exceeds 4',
  },
};

// Creative Refresh Schedule
interface CreativeRotation {
  adVariants: number;
  rotationFrequency: number; // days
  refreshThreshold: {
    ctrDecline: number;
    conversionDecline: number;
    frequencyMax: number;
  };
}

const creativeScalingBestPractices: CreativeRotation = {
  // Maintain 4-6 active ad variants
  adVariants: 5,

  // Rotate every 4-6 weeks for most campaigns
  rotationFrequency: 35,

  // Triggers for creative refresh
  refreshThreshold: {
    ctrDecline: 0.20, // 20% CTR decline from peak
    conversionDecline: 0.25, // 25% conversion rate decline
    frequencyMax: 5, // Average frequency above 5
  },
};

// Creative fatigue detection
function detectCreativeFatigue(
  historicalMetrics: DailyMetrics[],
  windowDays: number = 7
): {
  isFatigued: boolean;
  fatigueScore: number;
  symptoms: string[];
  recommendation: string;
} {
  const recentMetrics = historicalMetrics.slice(-windowDays);
  const previousMetrics = historicalMetrics.slice(-windowDays * 2, -windowDays);

  // Calculate trends
  const recentAvgCTR = average(recentMetrics.map(m => m.ctr));
  const previousAvgCTR = average(previousMetrics.map(m => m.ctr));
  const ctrChange = (recentAvgCTR - previousAvgCTR) / previousAvgCTR;

  const recentAvgConvRate = average(recentMetrics.map(m => m.conversionRate));
  const previousAvgConvRate = average(previousMetrics.map(m => m.conversionRate));
  const convRateChange = (recentAvgConvRate - previousAvgConvRate) / previousAvgConvRate;

  const currentFrequency = recentMetrics[recentMetrics.length - 1].frequency;

  // Assess fatigue
  const symptoms: string[] = [];
  let fatigueScore = 0;

  if (ctrChange < -0.15) {
    symptoms.push(`CTR declined ${Math.abs(ctrChange * 100).toFixed(0)}%`);
    fatigueScore += 30;
  }
  if (convRateChange < -0.20) {
    symptoms.push(`Conversion rate declined ${Math.abs(convRateChange * 100).toFixed(0)}%`);
    fatigueScore += 35;
  }
  if (currentFrequency > 4) {
    symptoms.push(`High frequency: ${currentFrequency.toFixed(1)}`);
    fatigueScore += 25;
  }

  const isFatigued = fatigueScore >= 50;

  let recommendation: string;
  if (fatigueScore >= 70) {
    recommendation = 'Urgent: Replace all creative and expand audience';
  } else if (fatigueScore >= 50) {
    recommendation = 'Introduce 2-3 new ad variants and test new messaging';
  } else if (fatigueScore >= 30) {
    recommendation = 'Monitor closely; prepare backup creative';
  } else {
    recommendation = 'Creative performing well; continue current strategy';
  }

  return { isFatigued, fatigueScore, symptoms, recommendation };
}

function average(numbers: number[]): number {
  return numbers.reduce((a, b) => a + b, 0) / numbers.length;
}

LinkedIn Ads Troubleshooting Matrix:
═══════════════════════════════════════════════════════════════════════════

SYMPTOM: LOW IMPRESSIONS / DELIVERY
───────────────────────────────────────────────────────────────────────────
Cause                        Diagnosis                    Solution
───────────────────────────────────────────────────────────────────────────
Audience too narrow          Check audience size          Expand targeting
Bid too low                  Check auction insights       Increase bid 20-30%
Budget too low               Daily budget < $50           Increase budget
Low ad quality               Check relevance score        Improve creative
High competition             Seasonal/industry spike      Wait or bid higher
Account issues               Review account status        Contact support
───────────────────────────────────────────────────────────────────────────


SYMPTOM: HIGH IMPRESSIONS, LOW CLICKS (CTR < 0.3%)
───────────────────────────────────────────────────────────────────────────
Cause                        Diagnosis                    Solution
───────────────────────────────────────────────────────────────────────────
Weak headline                A/B test headlines           Test benefit-focused
Poor creative                Image doesn't stand out      Test new images
Wrong audience               Targeting misaligned         Refine targeting
Weak value prop              Message not compelling       Clarify offer
Ad fatigue                   Frequency > 4                Refresh creative
───────────────────────────────────────────────────────────────────────────


SYMPTOM: HIGH CLICKS, LOW CONVERSIONS
───────────────────────────────────────────────────────────────────────────
Cause                        Diagnosis                    Solution
───────────────────────────────────────────────────────────────────────────
Landing page mismatch        Compare ad to LP             Align messaging
Slow page speed              Check Core Web Vitals        Optimize page
Form too long                Count form fields            Reduce to 3-5
Poor mobile experience       Test on mobile               Mobile optimize
Traffic quality              Check audience insights      Tighten targeting
Tracking issues              Verify Insight Tag           Debug tracking
───────────────────────────────────────────────────────────────────────────


SYMPTOM: RISING CPA
───────────────────────────────────────────────────────────────────────────
Cause                        Diagnosis                    Solution
───────────────────────────────────────────────────────────────────────────
Audience saturation          Frequency > 5                Expand audience
Creative fatigue             CTR declining                Refresh creative
Increased competition        CPM rising                   Test new segments
Seasonality                  Check YoY data               Adjust expectations
Algorithm exploration        Recent changes made          Allow learning
Budget misallocation         Check segment CPA            Reallocate budget
───────────────────────────────────────────────────────────────────────────

// Comprehensive Campaign Diagnostic
interface DiagnosticResult {
  overallHealth: 'HEALTHY' | 'WARNING' | 'CRITICAL';
  healthScore: number;
  issues: DiagnosticIssue[];
  prioritizedActions: string[];
}

interface DiagnosticIssue {
  category: string;
  severity: 'LOW' | 'MEDIUM' | 'HIGH';
  metric: string;
  currentValue: number;
  benchmark: number;
  impact: string;
  solution: string;
}

function runCampaignDiagnostic(
  campaign: CampaignData
): DiagnosticResult {
  const issues: DiagnosticIssue[] = [];
  let healthScore = 100;

  // Delivery diagnostics
  if (campaign.deliveryRate < 0.7) {
    issues.push({
      category: 'Delivery',
      severity: campaign.deliveryRate < 0.5 ? 'HIGH' : 'MEDIUM',
      metric: 'Delivery Rate',
      currentValue: campaign.deliveryRate,
      benchmark: 0.85,
      impact: 'Reduced reach and conversion volume',
      solution: 'Increase bid, expand audience, or raise budget',
    });
    healthScore -= campaign.deliveryRate < 0.5 ? 25 : 15;
  }

  // Engagement diagnostics
  if (campaign.ctr < 0.003) { // 0.3%
    issues.push({
      category: 'Engagement',
      severity: campaign.ctr < 0.002 ? 'HIGH' : 'MEDIUM',
      metric: 'CTR',
      currentValue: campaign.ctr * 100,
      benchmark: 0.4,
      impact: 'Lower quality score and higher costs',
      solution: 'Test new headlines and creative; refine targeting',
    });
    healthScore -= campaign.ctr < 0.002 ? 20 : 10;
  }

  // Conversion diagnostics
  if (campaign.conversionRate < 0.05) { // 5%
    issues.push({
      category: 'Conversion',
      severity: campaign.conversionRate < 0.02 ? 'HIGH' : 'MEDIUM',
      metric: 'Conversion Rate',
      currentValue: campaign.conversionRate * 100,
      benchmark: 8,
      impact: 'Higher CPA and reduced ROI',
      solution: 'Optimize landing page; review targeting quality',
    });
    healthScore -= campaign.conversionRate < 0.02 ? 25 : 15;
  }

  // Efficiency diagnostics
  const cpaRatio = campaign.cpa / campaign.targetCPA;
  if (cpaRatio > 1.2) {
    issues.push({
      category: 'Efficiency',
      severity: cpaRatio > 1.5 ? 'HIGH' : 'MEDIUM',
      metric: 'CPA vs Target',
      currentValue: campaign.cpa,
      benchmark: campaign.targetCPA,
      impact: 'Below-target ROI',
      solution: 'Reduce bids, tighten targeting, or improve conversions',
    });
    healthScore -= cpaRatio > 1.5 ? 30 : 15;
  }

  // Frequency diagnostics
  if (campaign.frequency > 5) {
    issues.push({
      category: 'Saturation',
      severity: campaign.frequency > 7 ? 'HIGH' : 'MEDIUM',
      metric: 'Frequency',
      currentValue: campaign.frequency,
      benchmark: 3,
      impact: 'Ad fatigue leading to declining performance',
      solution: 'Expand audience or refresh creative',
    });
    healthScore -= campaign.frequency > 7 ? 20 : 10;
  }

  // Determine overall health
  let overallHealth: 'HEALTHY' | 'WARNING' | 'CRITICAL';
  if (healthScore >= 75) {
    overallHealth = 'HEALTHY';
  } else if (healthScore >= 50) {
    overallHealth = 'WARNING';
  } else {
    overallHealth = 'CRITICAL';
  }

  // Prioritize actions
  const prioritizedActions = issues
    .sort((a, b) => {
      const severityOrder = { HIGH: 3, MEDIUM: 2, LOW: 1 };
      return severityOrder[b.severity] - severityOrder[a.severity];
    })
    .slice(0, 3)
    .map(issue => `${issue.category}: ${issue.solution}`);

  return {
    overallHealth,
    healthScore,
    issues,
    prioritizedActions,
  };
}

// pxlpeak LinkedIn Optimization Integration
import { pxlpeak } from '@/lib/analytics';

// Configure automated optimization
await pxlpeak.linkedInOptimization.configure({
  accountId: process.env.LINKEDIN_AD_ACCOUNT_ID,

  // Automated monitoring
  monitoring: {
    enabled: true,
    checkInterval: 'hourly',

    alerts: {
      // Performance alerts
      cpaThreshold: {
        warning: 1.2, // 20% above target
        critical: 1.5, // 50% above target
      },
      ctrThreshold: {
        warning: 0.003, // Below 0.3%
        critical: 0.002, // Below 0.2%
      },
      deliveryThreshold: {
        warning: 0.7, // Below 70% of budget
        critical: 0.5, // Below 50% of budget
      },
      frequencyThreshold: {
        warning: 5,
        critical: 7,
      },

      // Notification channels
      channels: ['slack', 'email'],
      slackWebhook: process.env.SLACK_WEBHOOK_URL,
      emailRecipients: ['marketing@company.com'],
    },
  },

  // Automated actions
  automation: {
    enabled: true,

    rules: [
      {
        name: 'CPA Spike Protection',
        trigger: { metric: 'cpa', condition: 'gt', threshold: 1.5, windowHours: 24 },
        action: { type: 'reduceBudget', value: 0.25 },
        notification: true,
      },
      {
        name: 'High Performer Scale',
        trigger: { metric: 'cpa', condition: 'lt', threshold: 0.7, windowHours: 168 },
        action: { type: 'increaseBudget', value: 0.15, maxBudget: 500 },
        notification: true,
      },
      {
        name: 'Creative Fatigue Alert',
        trigger: { metric: 'ctrDecline', condition: 'gt', threshold: 0.2, windowDays: 7 },
        action: { type: 'alert', message: 'Creative fatigue detected' },
        notification: true,
      },
    ],
  },

  // Reporting
  reporting: {
    frequency: 'weekly',
    recipients: ['marketing@company.com'],
    metrics: [
      'spend', 'impressions', 'clicks', 'ctr',
      'conversions', 'conversionRate', 'cpa', 'roas',
    ],
    includeRecommendations: true,
    comparePeriod: 'previousWeek',
  },
});

// Real-time optimization dashboard
const optimizationDashboard = await pxlpeak.linkedIn.getOptimizationInsights({
  dateRange: 'last30days',

  analysis: {
    // Campaign health scores
    healthScores: true,

    // Performance trends
    trends: {
      metrics: ['cpa', 'ctr', 'conversionRate'],
      granularity: 'daily',
    },

    // Audience insights
    audienceInsights: {
      topPerformingSegments: true,
      underperformingSegments: true,
      expansionOpportunities: true,
    },

    // Creative insights
    creativeInsights: {
      topPerformingAds: 5,
      fatigueDetection: true,
      testRecommendations: true,
    },

    // Budget optimization
    budgetInsights: {
      allocationRecommendations: true,
      pacingAnalysis: true,
      forecastedPerformance: true,
    },
  },
});

// Cross-platform optimization with pxlpeak
const crossPlatformOptimization = await pxlpeak.attribution.analyze({
  platforms: ['linkedin', 'google', 'meta'],
  dateRange: 'last90days',

  // Attribution model
  model: 'data_driven',
  lookbackWindow: {
    click: 30,
    view: 7,
  },

  // Analysis dimensions
  analysis: {
    // Path analysis
    conversionPaths: {
      enabled: true,
      includeAssists: true,
      minPathLength: 2,
    },

    // Platform contribution
    platformContribution: {
      metric: 'revenue',
      includeAssisted: true,
    },

    // Budget reallocation
    budgetRecommendations: {
      enabled: true,
      optimizeFor: 'roas',
      constraints: {
        minPlatformBudget: 0.1, // Min 10% per platform
        maxPlatformBudget: 0.6, // Max 60% per platform
      },
    },
  },
});

// Example output structure
interface CrossPlatformInsights {
  platformContribution: {
    linkedin: {
      directConversions: number;
      assistedConversions: number;
      revenue: number;
      currentBudget: number;
      recommendedBudget: number;
      expectedROASChange: number;
    };
    // ... other platforms
  };

  topPaths: {
    path: string[];
    conversions: number;
    revenue: number;
    avgTimeToConvert: number;
  }[];

  recommendations: {
    platform: string;
    action: string;
    expectedImpact: string;
    priority: 'high' | 'medium' | 'low';
  }[];
}

Weekly LinkedIn Ads Optimization Checklist:
═══════════════════════════════════════════════════════════════════════════

MONDAY: PERFORMANCE REVIEW
─────────────────────────
□ Review previous week's KPIs vs targets
□ Check all campaigns for delivery issues
□ Review frequency and saturation metrics
□ Identify top and bottom performers

TUESDAY: AUDIENCE OPTIMIZATION
──────────────────────────────
□ Analyze audience insights report
□ Identify underperforming segments
□ Check lookalike audience performance
□ Review exclusion lists

WEDNESDAY: CREATIVE REVIEW
──────────────────────────
□ Check creative fatigue indicators
□ Review A/B test results
□ Plan new creative variants
□ Update underperforming ads

THURSDAY: BUDGET & BIDDING
──────────────────────────
□ Review budget pacing
□ Adjust bids based on performance
□ Reallocate budget from low to high performers
□ Check for scaling opportunities

FRIDAY: REPORTING & PLANNING
────────────────────────────
□ Generate weekly performance report
□ Document key learnings
□ Plan tests for next week
□ Update optimization log

Monthly LinkedIn Ads Audit:
═══════════════════════════════════════════════════════════════════════════

STRATEGIC REVIEW
────────────────
□ Campaign objective alignment with business goals
□ Funnel stage budget allocation
□ Cross-platform performance comparison
□ Competitor activity analysis

AUDIENCE AUDIT
──────────────
□ Full audience overlap analysis
□ Segment performance deep dive
□ New audience opportunity research
□ Matched Audience list refresh

CREATIVE AUDIT
──────────────
□ Full creative performance analysis
□ Message testing results
□ Format effectiveness comparison
□ Creative refresh planning

TECHNICAL AUDIT
───────────────
□ Insight Tag health check
□ Conversion tracking validation
□ Attribution window review
□ Data quality assessment

EFFICIENCY AUDIT
────────────────
□ CPA/ROAS trend analysis
□ Wasted spend identification
□ Bid strategy effectiveness
□ Budget utilization review