vibecosystem

Marketing Analytics

UTM Tracking Setup

UTM Parameter Standartlari

https://example.com/landing?
  utm_source=google           # Trafik kaynagi (google, facebook, newsletter)
  &utm_medium=cpc             # Kanal tipi (cpc, email, social, organic)
  &utm_campaign=spring_2026   # Kampanya adi
  &utm_term=saas+analytics    # Arama terimi (paid search)
  &utm_content=hero_banner    # Reklam varyanti (A/B test)

UTM Naming Convention

| Parameter | Format | Ornekler | |-----------|--------|---------| | source | lowercase, platform adi | google, facebook, linkedin, newsletter | | medium | lowercase, kanal tipi | cpc, cpm, email, social, organic, referral | | campaign | snake_case, tarih dahil | spring_sale_2026, product_launch_q1 | | term | + ile ayrilmis | saas+analytics, project+management | | content | snake_case, varyant | hero_banner, sidebar_cta, email_v2 |

UTM Builder (TypeScript)

interface UTMConfig {
  baseUrl: string;
  source: string;
  medium: string;
  campaign: string;
  term?: string;
  content?: string;
}

function buildUTMUrl(config: UTMConfig): string {
  const params = new URLSearchParams();
  params.set("utm_source", config.source.toLowerCase());
  params.set("utm_medium", config.medium.toLowerCase());
  params.set("utm_campaign", config.campaign.toLowerCase().replace(/\s+/g, "_"));
  if (config.term) params.set("utm_term", config.term.toLowerCase());
  if (config.content) params.set("utm_content", config.content.toLowerCase());

  const separator = config.baseUrl.includes("?") ? "&" : "?";
  return `${config.baseUrl}${separator}${params.toString()}`;
}

// UTM parametrelerini parse et ve kaydet
function captureUTM(): UTMParams | null {
  const params = new URLSearchParams(window.location.search);
  const utm: UTMParams = {
    source: params.get("utm_source") || undefined,
    medium: params.get("utm_medium") || undefined,
    campaign: params.get("utm_campaign") || undefined,
    term: params.get("utm_term") || undefined,
    content: params.get("utm_content") || undefined,
  };

  if (utm.source) {
    // First-touch ve last-touch ayri kaydet
    if (!localStorage.getItem("utm_first_touch")) {
      localStorage.setItem("utm_first_touch", JSON.stringify({ ...utm, timestamp: Date.now() }));
    }
    localStorage.setItem("utm_last_touch", JSON.stringify({ ...utm, timestamp: Date.now() }));
    return utm;
  }
  return null;
}

Attribution Modeling

Attribution Modelleri

| Model | Aciklama | Ne Zaman Kullan | |-------|----------|----------------| | First Touch | Ilk temas %100 kredi alir | Awareness kampanyalari | | Last Touch | Son temas %100 kredi alir | Direct response kampanyalari | | Linear | Tum temaslar esit kredi alir | Tum kanallari esit degerlendirme | | Time Decay | Son temaslara daha cok kredi | Uzun satis dongusu | | U-Shaped | Ilk ve son temas %40, orta %20 | Balanced B2B attribution | | W-Shaped | Ilk, lead, opportunity %30, geri kalan %10 | Full-funnel B2B | | Data-Driven | Algoritmik (Markov chain, Shapley) | Yeterli veri varsa (10K+ conversion) |

Multi-Touch Attribution Query

-- U-Shaped Attribution
WITH touchpoints AS (
  SELECT
    conversion_id,
    user_id,
    channel,
    touch_timestamp,
    ROW_NUMBER() OVER (PARTITION BY conversion_id ORDER BY touch_timestamp) AS touch_order,
    COUNT(*) OVER (PARTITION BY conversion_id) AS total_touches
  FROM marketing_touches
  WHERE conversion_id IS NOT NULL
),
attributed AS (
  SELECT
    conversion_id,
    channel,
    CASE
      WHEN total_touches = 1 THEN 1.0
      WHEN total_touches = 2 THEN 0.5
      WHEN touch_order = 1 THEN 0.4                            -- first touch
      WHEN touch_order = total_touches THEN 0.4                -- last touch
      ELSE 0.2 / (total_touches - 2)                           -- middle touches
    END AS attribution_weight
  FROM touchpoints
)
SELECT
  channel,
  ROUND(SUM(attribution_weight), 2) AS attributed_conversions,
  ROUND(SUM(attribution_weight * c.revenue), 2) AS attributed_revenue
FROM attributed a
JOIN conversions c ON a.conversion_id = c.id
GROUP BY channel
ORDER BY attributed_revenue DESC;

Markov Chain Attribution

interface TransitionMatrix {
  [fromState: string]: {
    [toState: string]: number;  // probability
  };
}

// Removal effect: Her kanalin conversion'a katki oranini hesapla
function calculateRemovalEffect(
  matrix: TransitionMatrix,
  channels: string[]
): Record<string, number> {
  const baseConversionRate = simulateConversions(matrix, channels);
  const effects: Record<string, number> = {};

  for (const channel of channels) {
    const withoutChannel = channels.filter(c => c !== channel);
    const reducedRate = simulateConversions(matrix, withoutChannel);
    effects[channel] = (baseConversionRate - reducedRate) / baseConversionRate;
  }

  // Normalize to sum to 1
  const total = Object.values(effects).reduce((a, b) => a + b, 0);
  for (const channel of channels) {
    effects[channel] = effects[channel] / total;
  }

  return effects;
}

CAC (Customer Acquisition Cost)

CAC Hesaplama

interface CACMetrics {
  totalMarketingSpend: number;        // Toplam marketing harcamasi
  totalSalesSpend: number;            // Toplam sales harcamasi (maas dahil)
  newCustomers: number;               // Kazanilan musteri sayisi
  period: string;                     // "2026-Q1"
}

function calculateCAC(metrics: CACMetrics): {
  blendedCAC: number;
  paidCAC: number;
  organicCAC: number;
} {
  const totalSpend = metrics.totalMarketingSpend + metrics.totalSalesSpend;
  return {
    blendedCAC: totalSpend / metrics.newCustomers,
    paidCAC: metrics.totalMarketingSpend / (metrics.newCustomers * 0.6),  // %60 paid
    organicCAC: (metrics.totalSalesSpend * 0.3) / (metrics.newCustomers * 0.4),
  };
}

CAC by Channel Query

SELECT
  channel,
  SUM(spend) AS total_spend,
  COUNT(DISTINCT conversion_user_id) AS new_customers,
  ROUND(SUM(spend) / NULLIF(COUNT(DISTINCT conversion_user_id), 0), 2) AS cac,
  ROUND(AVG(first_order_value), 2) AS avg_first_order
FROM (
  SELECT
    a.channel,
    a.spend,
    c.user_id AS conversion_user_id,
    c.revenue AS first_order_value
  FROM ad_spend a
  LEFT JOIN conversions c ON c.attributed_channel = a.channel
    AND c.conversion_date BETWEEN a.date AND a.date + INTERVAL '30 days'
  WHERE a.date >= CURRENT_DATE - INTERVAL '90 days'
) channel_data
GROUP BY channel
ORDER BY cac;

CAC Benchmarks

| Industry | Median CAC | Iyi CAC | Target LTV:CAC | |----------|-----------|---------|---------------| | SaaS B2B (SMB) | $200-500 | < $200 | 3:1+ | | SaaS B2B (Enterprise) | $5K-20K | < $5K | 5:1+ | | SaaS B2C | $20-100 | < $30 | 3:1+ | | E-commerce | $10-50 | < $15 | 3:1+ | | Fintech | $100-500 | < $100 | 4:1+ | | Marketplace | $50-200 | < $50 | 3:1+ |

ROAS (Return on Ad Spend)

ROAS Calculator

function calculateROAS(
  revenue: number,
  adSpend: number
): { roas: number; roasPercentage: number; profitable: boolean } {
  const roas = revenue / adSpend;
  return {
    roas: Math.round(roas * 100) / 100,
    roasPercentage: Math.round(roas * 100),
    profitable: roas > 1,
  };
}

// Hedef ROAS hesapla (break-even icin)
function targetROAS(grossMargin: number): number {
  // Minimum ROAS = 1 / Gross Margin
  // %70 margin -> minimum 1.43 ROAS
  return Math.round((1 / grossMargin) * 100) / 100;
}

ROAS Dashboard Query

SELECT
  campaign_name,
  channel,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  ROUND(100.0 * SUM(clicks) / NULLIF(SUM(impressions), 0), 2) AS ctr_pct,
  SUM(spend) AS spend,
  SUM(conversions) AS conversions,
  ROUND(SUM(spend) / NUL