vibecosystem

Product Analytics

Event Taxonomy Design

Event Naming Convention

<object>_<action>

Ornekler:
  user_signed_up
  page_viewed
  button_clicked
  feature_activated
  subscription_started
  payment_completed
  item_added_to_cart
  search_performed

Event Schema (TypeScript)

interface AnalyticsEvent {
  event_name: string;
  timestamp: string;           // ISO 8601
  user_id: string;
  anonymous_id?: string;       // pre-auth tracking
  session_id: string;
  properties: Record<string, unknown>;
  context: EventContext;
}

interface EventContext {
  app_version: string;
  platform: "web" | "ios" | "android";
  locale: string;
  timezone: string;
  page_url?: string;
  referrer?: string;
  utm?: UTMParams;
  device?: DeviceInfo;
}

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

Event Categories

| Kategori | Ornek Eventler | Amac | |----------|---------------|------| | Identity | user_signed_up, user_logged_in | Kim? | | Navigation | page_viewed, tab_switched | Nerede? | | Interaction | button_clicked, form_submitted | Ne yapti? | | Transaction | purchase_completed, subscription_started | Para akisi | | Feature | feature_activated, feature_used | Deger bulma | | System | error_occurred, api_timeout | Saglik |

Tracking Plan Template

const trackingPlan = {
  "user_signed_up": {
    description: "Kullanici kayit tamamladi",
    properties: {
      method: { type: "string", enum: ["email", "google", "github"], required: true },
      referral_code: { type: "string", required: false },
      plan: { type: "string", enum: ["free", "pro", "enterprise"], required: true },
    },
    triggers: ["Registration form submit"],
    owner: "growth-team",
  },
  "feature_activated": {
    description: "Kullanici bir feature'u ilk kez kulandi",
    properties: {
      feature_name: { type: "string", required: true },
      activation_method: { type: "string", required: true },
      time_since_signup_hours: { type: "number", required: true },
    },
    triggers: ["First use of any tracked feature"],
    owner: "product-team",
  },
};

AARRR Funnel (Pirate Metrics)

Funnel Tanimlari

Acquisition  --> Activation  --> Retention  --> Revenue  --> Referral
(Edinme)        (Aktiflesme)    (Tutunma)     (Gelir)      (Yonlendirme)

| Stage | Tanim | Ornek Metrik | Hedef | |-------|-------|-------------|-------| | Acquisition | Kullanici siteye geldi | Unique visitors, signup rate | %3-5 signup | | Activation | "Aha moment" yasandi | Onboarding completion, first value | %40-60 activation | | Retention | Geri geldi | D1/D7/D30 retention | D7 > %20 | | Revenue | Para odedi | Conversion to paid, ARPU | %2-5 conversion | | Referral | Baskasini getirdi | Invite sent, viral coefficient | K > 0.5 |

Funnel Analysis Query

-- Acquisition -> Activation -> Retention funnel
WITH funnel AS (
  SELECT
    user_id,
    MIN(CASE WHEN event = 'user_signed_up' THEN timestamp END) AS signed_up_at,
    MIN(CASE WHEN event = 'onboarding_completed' THEN timestamp END) AS activated_at,
    MIN(CASE WHEN event = 'feature_used' AND day_number >= 7 THEN timestamp END) AS retained_at,
    MIN(CASE WHEN event = 'subscription_started' THEN timestamp END) AS converted_at
  FROM events
  WHERE timestamp >= CURRENT_DATE - INTERVAL '30 days'
  GROUP BY user_id
)
SELECT
  COUNT(signed_up_at) AS acquisitions,
  COUNT(activated_at) AS activations,
  ROUND(100.0 * COUNT(activated_at) / NULLIF(COUNT(signed_up_at), 0), 1) AS activation_rate,
  COUNT(retained_at) AS retained,
  ROUND(100.0 * COUNT(retained_at) / NULLIF(COUNT(activated_at), 0), 1) AS retention_rate,
  COUNT(converted_at) AS converted,
  ROUND(100.0 * COUNT(converted_at) / NULLIF(COUNT(retained_at), 0), 1) AS conversion_rate
FROM funnel;

Cohort Analysis

Retention Cohort Query

-- Weekly retention cohort
WITH user_cohort AS (
  SELECT
    user_id,
    DATE_TRUNC('week', MIN(timestamp)) AS cohort_week
  FROM events
  WHERE event = 'user_signed_up'
  GROUP BY user_id
),
user_activity AS (
  SELECT
    e.user_id,
    uc.cohort_week,
    DATE_TRUNC('week', e.timestamp) AS activity_week,
    (DATE_TRUNC('week', e.timestamp) - uc.cohort_week) / 7 AS week_number
  FROM events e
  JOIN user_cohort uc ON e.user_id = uc.user_id
)
SELECT
  cohort_week,
  week_number,
  COUNT(DISTINCT user_id) AS active_users,
  ROUND(100.0 * COUNT(DISTINCT user_id) /
    FIRST_VALUE(COUNT(DISTINCT user_id)) OVER (
      PARTITION BY cohort_week ORDER BY week_number
    ), 1) AS retention_pct
FROM user_activity
GROUP BY cohort_week, week_number
ORDER BY cohort_week, week_number;

Cohort Visualization Data

interface CohortData {
  cohort: string;         // "2026-W01"
  size: number;           // cohort buyuklugu
  retention: number[];    // [100, 45, 32, 28, 25, 23, 22, 21]
}

function buildCohortTable(cohorts: CohortData[]): string[][] {
  const header = ["Cohort", "Size", "W0", "W1", "W2", "W3", "W4", "W5", "W6", "W7"];
  const rows = cohorts.map(c => [
    c.cohort,
    String(c.size),
    ...c.retention.map(r => `${r}%`),
  ]);
  return [header, ...rows];
}

A/B Testing Framework

Experiment Design

interface Experiment {
  id: string;
  name: string;
  hypothesis: string;                    // "X degisikligi Y metrigini Z kadar arttirir"
  primary_metric: string;                // tek bir karar metrigi
  secondary_metrics: string[];
  guardrail_metrics: string[];           // bozulmamasi gereken metrikler
  variants: Variant[];
  traffic_allocation: number;            // %10-50 arasi basla
  min_sample_size: number;
  min_duration_days: number;
  status: "draft" | "running" | "analyzing" | "completed";
}

interface Variant {
  id: string;
  name: string;                          // "control" | "treatment_a" | "treatment_b"
  weight: number;                        // 0.5 = %50
  description: string;
}

Sample Size Calculator

function calculateSampleSize(
  baselineRate: number,       // mevcut conversion rate (0.05 = %5)
  mde: number,                // minimum detectable effect (0.10 = %10 relative)
  alpha: number = 0.05,       // significance level
  power: number = 0.80        // statistical power
): number {
  const p1 = baselineRate;
  const p2 = baselineRate * (1 + mde);
  const zAlpha = 1.96;        // two-tailed
  const zBeta = 0.84;

  const pooledP = (p1 + p2) / 2;
  const numerator = Math.pow(
    zAlpha * Math.sqrt(2 * pooledP * (1 - pooledP)) +
    zBeta * Math.sqrt(p1 * (1 - p1) + p2 * (1 - p2)),
    2
  );
  const denominator = Math.pow(p1 - p2, 2);

  return Math.ceil(numerator / denominator);
}

// Ornek: %5 baseline, %10 relative MDE
// calculateSampleSize(0.05, 0.10) => ~31,000 per variant

Statistical Significance Check

function checkSignificance(
  controlConversions: number,
  controlTotal: number,
  treatmentConversions: number,
  treatmentTotal: number
): { significant: boolean; pValue: number; lift: number; ci: [number, number] } {
  const p1 = controlConversions / controlTotal;
  const p2 = treatmentConversions / treatmentTotal;
  const pooledP = (controlConversions + treatmentConversions) / (controlTotal + treatmentTotal);
  const se = Math.sqrt(pooledP * (1 - pooledP) * (1 / controlTotal + 1 / treatmentTotal));
  const z = (p2 - p1) / se;
  const pValue = 2 * (1 - normalCDF(Math.abs(z)));
  const lift = (p2 - p1) / p1;
  const liftSE = Math.sqrt(p2 * (1 - p2) / treatmentTotal + p1 * (1 - p1) / controlTotal) / p1;
  const ci: [number, number] = [lift - 1.96 * liftSE, lift + 1.96 * liftSE];

  return {
    signi