Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
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/**
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* Marketing Analytics Pipeline Examples
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*
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* Generates analytics data including:
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* - Attribution modeling data
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* - LTV (Lifetime Value) calculation datasets
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* - Funnel analysis data
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* - Seasonal trend simulation
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*/
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import { AgenticSynth, createSynth } from '../../src/index.js';
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// Example 1: Attribution modeling data
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async function generateAttributionModels() {
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const synth = createSynth({
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provider: 'gemini',
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apiKey: process.env.GEMINI_API_KEY
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});
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const attributionSchema = {
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modelId: { type: 'string', required: true },
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modelType: { type: 'string', required: true },
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analysisDate: { type: 'string', required: true },
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timeWindow: { type: 'string', required: true },
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totalConversions: { type: 'number', required: true },
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totalRevenue: { type: 'number', required: true },
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channelAttribution: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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channel: { type: 'string' },
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touchpoints: { type: 'number' },
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firstTouchConversions: { type: 'number' },
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lastTouchConversions: { type: 'number' },
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linearConversions: { type: 'number' },
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timeDecayConversions: { type: 'number' },
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positionBasedConversions: { type: 'number' },
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algorithmicConversions: { type: 'number' },
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attributedRevenue: { type: 'number' },
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attributedSpend: { type: 'number' },
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roas: { type: 'number' },
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efficiency: { type: 'number' }
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}
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}
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},
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crossChannelInteractions: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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path: { type: 'array' },
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conversions: { type: 'number' },
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revenue: { type: 'number' },
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avgPathLength: { type: 'number' },
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avgTimeToConversion: { type: 'number' }
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}
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}
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},
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insights: {
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type: 'object',
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required: true,
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properties: {
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topPerformingChannels: { type: 'array' },
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undervaluedChannels: { type: 'array' },
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overvaluedChannels: { type: 'array' },
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recommendedBudgetShift: { type: 'object' }
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}
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}
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};
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const result = await synth.generateStructured({
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count: 30,
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schema: attributionSchema,
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constraints: {
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modelType: [
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'first_touch',
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'last_touch',
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'linear',
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'time_decay',
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'position_based',
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'data_driven'
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],
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timeWindow: ['7_days', '14_days', '30_days', '60_days', '90_days'],
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totalConversions: { min: 100, max: 10000 },
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totalRevenue: { min: 10000, max: 5000000 },
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channelAttribution: { minLength: 4, maxLength: 10 }
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}
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});
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console.log('Attribution Model Data:');
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console.log(result.data.slice(0, 2));
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return result;
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}
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// Example 2: LTV (Lifetime Value) calculations
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async function generateLTVAnalysis() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const ltvSchema = {
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cohortId: { type: 'string', required: true },
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cohortName: { type: 'string', required: true },
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acquisitionChannel: { type: 'string', required: true },
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acquisitionDate: { type: 'string', required: true },
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cohortSize: { type: 'number', required: true },
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metrics: {
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type: 'object',
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required: true,
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properties: {
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avgFirstPurchase: { type: 'number' },
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avgOrderValue: { type: 'number' },
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purchaseFrequency: { type: 'number' },
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customerLifespan: { type: 'number' },
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retentionRate: { type: 'number' },
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churnRate: { type: 'number' },
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marginPerCustomer: { type: 'number' }
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}
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},
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ltvCalculations: {
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type: 'object',
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required: true,
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properties: {
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historicLTV: { type: 'number' },
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predictiveLTV: { type: 'number' },
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ltv30Days: { type: 'number' },
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ltv90Days: { type: 'number' },
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ltv180Days: { type: 'number' },
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ltv365Days: { type: 'number' },
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ltv3Years: { type: 'number' }
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}
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},
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acquisition: {
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type: 'object',
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required: true,
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properties: {
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cac: { type: 'number' },
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ltvCacRatio: { type: 'number' },
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paybackPeriod: { type: 'number' },
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roi: { type: 'number' }
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}
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},
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revenueByPeriod: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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period: { type: 'number' },
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activeCustomers: { type: 'number' },
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purchases: { type: 'number' },
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revenue: { type: 'number' },
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cumulativeRevenue: { type: 'number' },
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cumulativeLTV: { type: 'number' }
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}
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}
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},
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segments: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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segmentName: { type: 'string' },
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percentage: { type: 'number' },
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avgLTV: { type: 'number' },
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characteristics: { type: 'array' }
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}
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}
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}
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};
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const result = await synth.generateStructured({
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count: 40,
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schema: ltvSchema,
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constraints: {
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acquisitionChannel: [
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'google_ads',
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'facebook_ads',
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'tiktok_ads',
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'organic_search',
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'email',
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'referral',
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'direct'
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],
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cohortSize: { min: 100, max: 50000 },
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'metrics.customerLifespan': { min: 3, max: 60 },
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'acquisition.ltvCacRatio': { min: 0.5, max: 15.0 },
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revenueByPeriod: { minLength: 12, maxLength: 36 }
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}
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});
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console.log('LTV Analysis Data:');
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console.log(result.data.slice(0, 2));
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return result;
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}
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// Example 3: Marketing funnel analysis
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async function generateFunnelAnalysis() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const funnelSchema = {
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funnelId: { type: 'string', required: true },
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funnelName: { type: 'string', required: true },
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channel: { type: 'string', required: true },
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campaign: { type: 'string', required: true },
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dateRange: {
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type: 'object',
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required: true,
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properties: {
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start: { type: 'string' },
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end: { type: 'string' }
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}
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},
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stages: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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stageName: { type: 'string' },
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stageOrder: { type: 'number' },
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users: { type: 'number' },
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conversions: { type: 'number' },
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conversionRate: { type: 'number' },
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dropoffRate: { type: 'number' },
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avgTimeInStage: { type: 'number' },
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revenue: { type: 'number' },
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cost: { type: 'number' }
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}
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}
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},
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overallMetrics: {
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type: 'object',
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required: true,
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properties: {
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totalUsers: { type: 'number' },
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totalConversions: { type: 'number' },
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overallConversionRate: { type: 'number' },
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totalRevenue: { type: 'number' },
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totalCost: { type: 'number' },
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roas: { type: 'number' },
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avgTimeToConversion: { type: 'number' }
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}
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},
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dropoffAnalysis: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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fromStage: { type: 'string' },
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toStage: { type: 'string' },
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dropoffCount: { type: 'number' },
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dropoffRate: { type: 'number' },
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reasons: { type: 'array' },
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recoveryOpportunities: { type: 'array' }
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}
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}
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},
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optimization: {
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type: 'object',
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required: true,
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properties: {
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bottlenecks: { type: 'array' },
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recommendations: { type: 'array' },
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expectedImprovement: { type: 'number' },
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priorityActions: { type: 'array' }
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}
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}
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};
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const result = await synth.generateStructured({
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count: 35,
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schema: funnelSchema,
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constraints: {
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channel: ['google_ads', 'facebook_ads', 'tiktok_ads', 'email', 'organic'],
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stages: { minLength: 4, maxLength: 8 },
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'overallMetrics.overallConversionRate': { min: 0.01, max: 0.25 },
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'overallMetrics.roas': { min: 0.5, max: 10.0 }
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}
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});
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console.log('Funnel Analysis Data:');
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console.log(result.data.slice(0, 2));
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return result;
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}
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// Example 4: Seasonal trend analysis
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async function generateSeasonalTrends() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const result = await synth.generateTimeSeries({
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count: 365,
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startDate: new Date(Date.now() - 365 * 24 * 60 * 60 * 1000),
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endDate: new Date(),
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interval: '1d',
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metrics: [
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'impressions',
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'clicks',
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'conversions',
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'spend',
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'revenue',
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'roas',
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'ctr',
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'cvr',
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'cpa',
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'seasonality_index',
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'trend_index',
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'day_of_week_effect'
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],
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trend: 'up',
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seasonality: true,
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noise: 0.12,
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constraints: {
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impressions: { min: 50000, max: 500000 },
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clicks: { min: 500, max: 10000 },
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conversions: { min: 50, max: 1000 },
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spend: { min: 500, max: 20000 },
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revenue: { min: 1000, max: 100000 },
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roas: { min: 1.0, max: 12.0 },
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seasonality_index: { min: 0.5, max: 2.0 }
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}
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});
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console.log('Seasonal Trend Data (daily for 1 year):');
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console.log(result.data.slice(0, 7));
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console.log('Metadata:', result.metadata);
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return result;
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}
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// Example 5: Predictive analytics
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async function generatePredictiveAnalytics() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const predictiveSchema = {
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predictionId: { type: 'string', required: true },
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predictionDate: { type: 'string', required: true },
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predictionHorizon: { type: 'string', required: true },
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model: { type: 'string', required: true },
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historicalPeriod: { type: 'string', required: true },
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predictions: {
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type: 'object',
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required: true,
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properties: {
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expectedSpend: { type: 'number' },
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expectedRevenue: { type: 'number' },
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expectedConversions: { type: 'number' },
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expectedROAS: { type: 'number' },
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expectedCAC: { type: 'number' },
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expectedLTV: { type: 'number' }
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}
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},
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confidenceIntervals: {
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type: 'object',
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required: true,
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properties: {
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spend: { type: 'object' },
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revenue: { type: 'object' },
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conversions: { type: 'object' },
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roas: { type: 'object' }
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}
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},
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scenarios: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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scenarioName: { type: 'string' },
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probability: { type: 'number' },
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predictedROAS: { type: 'number' },
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predictedRevenue: { type: 'number' },
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factors: { type: 'array' }
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}
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}
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},
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riskFactors: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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factor: { type: 'string' },
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impact: { type: 'string' },
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probability: { type: 'number' },
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mitigation: { type: 'string' }
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}
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}
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},
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recommendations: { type: 'array', required: true }
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};
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const result = await synth.generateStructured({
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count: 25,
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schema: predictiveSchema,
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constraints: {
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predictionHorizon: ['7_days', '30_days', '90_days', '180_days', '365_days'],
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model: ['arima', 'prophet', 'lstm', 'random_forest', 'xgboost', 'ensemble'],
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scenarios: { minLength: 3, maxLength: 5 },
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'predictions.expectedROAS': { min: 1.0, max: 15.0 }
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}
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});
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console.log('Predictive Analytics Data:');
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console.log(result.data.slice(0, 2));
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return result;
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}
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// Example 6: Channel performance comparison
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async function generateChannelComparison() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const comparisonSchema = {
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reportId: { type: 'string', required: true },
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||||
reportDate: { type: 'string', required: true },
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dateRange: {
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type: 'object',
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required: true,
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||||
properties: {
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start: { type: 'string' },
|
||||
end: { type: 'string' }
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||||
}
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||||
},
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||||
channels: {
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type: 'array',
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||||
required: true,
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items: {
|
||||
type: 'object',
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||||
properties: {
|
||||
channel: { type: 'string' },
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||||
platform: { type: 'string' },
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||||
campaigns: { type: 'number' },
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impressions: { type: 'number' },
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||||
clicks: { type: 'number' },
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||||
conversions: { type: 'number' },
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||||
spend: { type: 'number' },
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||||
revenue: { type: 'number' },
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||||
ctr: { type: 'number' },
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cvr: { type: 'number' },
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||||
cpc: { type: 'number' },
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cpa: { type: 'number' },
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roas: { type: 'number' },
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||||
marketShare: { type: 'number' },
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||||
efficiency: { type: 'number' },
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||||
scalability: { type: 'string' }
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||||
}
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||||
}
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||||
},
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||||
crossChannelMetrics: {
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type: 'object',
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||||
required: true,
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||||
properties: {
|
||||
totalSpend: { type: 'number' },
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||||
totalRevenue: { type: 'number' },
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||||
overallROAS: { type: 'number' },
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||||
channelDiversity: { type: 'number' },
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||||
portfolioRisk: { type: 'number' }
|
||||
}
|
||||
},
|
||||
recommendations: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
scaleUp: { type: 'array' },
|
||||
maintain: { type: 'array' },
|
||||
optimize: { type: 'array' },
|
||||
scaleDown: { type: 'array' },
|
||||
budgetReallocation: { type: 'object' }
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const result = await synth.generateStructured({
|
||||
count: 30,
|
||||
schema: comparisonSchema,
|
||||
constraints: {
|
||||
channels: { minLength: 4, maxLength: 10 },
|
||||
'crossChannelMetrics.overallROAS': { min: 2.0, max: 8.0 }
|
||||
}
|
||||
});
|
||||
|
||||
console.log('Channel Comparison Data:');
|
||||
console.log(result.data.slice(0, 2));
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// Example 7: Incrementality testing
|
||||
async function generateIncrementalityTests() {
|
||||
const synth = createSynth({
|
||||
provider: 'gemini'
|
||||
});
|
||||
|
||||
const incrementalitySchema = {
|
||||
testId: { type: 'string', required: true },
|
||||
testName: { type: 'string', required: true },
|
||||
channel: { type: 'string', required: true },
|
||||
testType: { type: 'string', required: true },
|
||||
startDate: { type: 'string', required: true },
|
||||
endDate: { type: 'string', required: true },
|
||||
methodology: { type: 'string', required: true },
|
||||
testGroup: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
size: { type: 'number' },
|
||||
spend: { type: 'number' },
|
||||
conversions: { type: 'number' },
|
||||
revenue: { type: 'number' }
|
||||
}
|
||||
},
|
||||
controlGroup: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
size: { type: 'number' },
|
||||
spend: { type: 'number' },
|
||||
conversions: { type: 'number' },
|
||||
revenue: { type: 'number' }
|
||||
}
|
||||
},
|
||||
results: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
incrementalConversions: { type: 'number' },
|
||||
incrementalRevenue: { type: 'number' },
|
||||
incrementalityRate: { type: 'number' },
|
||||
trueROAS: { type: 'number' },
|
||||
reportedROAS: { type: 'number' },
|
||||
overestimation: { type: 'number' },
|
||||
statisticalSignificance: { type: 'boolean' },
|
||||
confidenceLevel: { type: 'number' }
|
||||
}
|
||||
},
|
||||
insights: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
cannibalizedRevenue: { type: 'number' },
|
||||
brandLiftEffect: { type: 'number' },
|
||||
spilloverEffect: { type: 'number' },
|
||||
recommendedAction: { type: 'string' }
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const result = await synth.generateStructured({
|
||||
count: 20,
|
||||
schema: incrementalitySchema,
|
||||
constraints: {
|
||||
channel: ['google_ads', 'facebook_ads', 'tiktok_ads', 'display', 'video'],
|
||||
testType: ['geo_holdout', 'user_holdout', 'time_based', 'psm'],
|
||||
methodology: ['randomized_control', 'quasi_experimental', 'synthetic_control'],
|
||||
'results.incrementalityRate': { min: 0.1, max: 1.0 }
|
||||
}
|
||||
});
|
||||
|
||||
console.log('Incrementality Test Data:');
|
||||
console.log(result.data.slice(0, 2));
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// Example 8: Marketing mix modeling
|
||||
async function generateMarketingMixModel() {
|
||||
const synth = createSynth({
|
||||
provider: 'gemini'
|
||||
});
|
||||
|
||||
const mmmSchema = {
|
||||
modelId: { type: 'string', required: true },
|
||||
modelDate: { type: 'string', required: true },
|
||||
timeRange: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
start: { type: 'string' },
|
||||
end: { type: 'string' }
|
||||
}
|
||||
},
|
||||
modelMetrics: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
rSquared: { type: 'number' },
|
||||
mape: { type: 'number' },
|
||||
rmse: { type: 'number' },
|
||||
decomposition: { type: 'object' }
|
||||
}
|
||||
},
|
||||
channelContributions: {
|
||||
type: 'array',
|
||||
required: true,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
channel: { type: 'string' },
|
||||
spend: { type: 'number' },
|
||||
contribution: { type: 'number' },
|
||||
contributionPercent: { type: 'number' },
|
||||
roi: { type: 'number' },
|
||||
saturationLevel: { type: 'number' },
|
||||
carryoverEffect: { type: 'number' },
|
||||
elasticity: { type: 'number' }
|
||||
}
|
||||
}
|
||||
},
|
||||
optimization: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
currentROI: { type: 'number' },
|
||||
optimizedROI: { type: 'number' },
|
||||
improvementPotential: { type: 'number' },
|
||||
optimalAllocation: { type: 'object' },
|
||||
scenarioAnalysis: { type: 'array' }
|
||||
}
|
||||
},
|
||||
externalFactors: {
|
||||
type: 'array',
|
||||
required: true,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
factor: { type: 'string' },
|
||||
impact: { type: 'number' },
|
||||
significance: { type: 'string' }
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const result = await synth.generateStructured({
|
||||
count: 15,
|
||||
schema: mmmSchema,
|
||||
constraints: {
|
||||
'modelMetrics.rSquared': { min: 0.7, max: 0.95 },
|
||||
channelContributions: { minLength: 5, maxLength: 12 },
|
||||
'optimization.improvementPotential': { min: 0.05, max: 0.5 }
|
||||
}
|
||||
});
|
||||
|
||||
console.log('Marketing Mix Model Data:');
|
||||
console.log(result.data.slice(0, 1));
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// Example 9: Real-time streaming analytics
|
||||
async function streamAnalyticsData() {
|
||||
const synth = createSynth({
|
||||
provider: 'gemini',
|
||||
streaming: true
|
||||
});
|
||||
|
||||
console.log('Streaming real-time analytics:');
|
||||
|
||||
let count = 0;
|
||||
for await (const metric of synth.generateStream('structured', {
|
||||
count: 15,
|
||||
schema: {
|
||||
timestamp: { type: 'string', required: true },
|
||||
channel: { type: 'string', required: true },
|
||||
impressions: { type: 'number', required: true },
|
||||
clicks: { type: 'number', required: true },
|
||||
conversions: { type: 'number', required: true },
|
||||
spend: { type: 'number', required: true },
|
||||
revenue: { type: 'number', required: true },
|
||||
roas: { type: 'number', required: true },
|
||||
alert: { type: 'string', required: false }
|
||||
}
|
||||
})) {
|
||||
count++;
|
||||
console.log(`[${count}] Metric received:`, metric);
|
||||
}
|
||||
}
|
||||
|
||||
// Example 10: Comprehensive analytics batch
|
||||
async function generateAnalyticsBatch() {
|
||||
const synth = createSynth({
|
||||
provider: 'gemini'
|
||||
});
|
||||
|
||||
const analyticsTypes = [
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
type: { type: 'string' },
|
||||
metric: { type: 'string' },
|
||||
value: { type: 'number' },
|
||||
change: { type: 'number' }
|
||||
},
|
||||
constraints: { type: 'attribution' }
|
||||
},
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
type: { type: 'string' },
|
||||
metric: { type: 'string' },
|
||||
value: { type: 'number' },
|
||||
change: { type: 'number' }
|
||||
},
|
||||
constraints: { type: 'ltv' }
|
||||
},
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
type: { type: 'string' },
|
||||
metric: { type: 'string' },
|
||||
value: { type: 'number' },
|
||||
change: { type: 'number' }
|
||||
},
|
||||
constraints: { type: 'funnel' }
|
||||
}
|
||||
];
|
||||
|
||||
const results = await synth.generateBatch('structured', analyticsTypes, 3);
|
||||
|
||||
console.log('Analytics Batch Results:');
|
||||
results.forEach((result, i) => {
|
||||
const types = ['Attribution', 'LTV', 'Funnel'];
|
||||
console.log(`${types[i]}: ${result.metadata.count} metrics in ${result.metadata.duration}ms`);
|
||||
});
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
// Run all examples
|
||||
export async function runAnalyticsExamples() {
|
||||
console.log('=== Example 1: Attribution Models ===');
|
||||
await generateAttributionModels();
|
||||
|
||||
console.log('\n=== Example 2: LTV Analysis ===');
|
||||
await generateLTVAnalysis();
|
||||
|
||||
console.log('\n=== Example 3: Funnel Analysis ===');
|
||||
await generateFunnelAnalysis();
|
||||
|
||||
console.log('\n=== Example 4: Seasonal Trends ===');
|
||||
await generateSeasonalTrends();
|
||||
|
||||
console.log('\n=== Example 5: Predictive Analytics ===');
|
||||
await generatePredictiveAnalytics();
|
||||
|
||||
console.log('\n=== Example 6: Channel Comparison ===');
|
||||
await generateChannelComparison();
|
||||
|
||||
console.log('\n=== Example 7: Incrementality Tests ===');
|
||||
await generateIncrementalityTests();
|
||||
|
||||
console.log('\n=== Example 8: Marketing Mix Model ===');
|
||||
await generateMarketingMixModel();
|
||||
|
||||
console.log('\n=== Example 10: Analytics Batch ===');
|
||||
await generateAnalyticsBatch();
|
||||
}
|
||||
|
||||
// Export individual functions
|
||||
export {
|
||||
generateAttributionModels,
|
||||
generateLTVAnalysis,
|
||||
generateFunnelAnalysis,
|
||||
generateSeasonalTrends,
|
||||
generatePredictiveAnalytics,
|
||||
generateChannelComparison,
|
||||
generateIncrementalityTests,
|
||||
generateMarketingMixModel,
|
||||
streamAnalyticsData,
|
||||
generateAnalyticsBatch
|
||||
};
|
||||
|
||||
// Uncomment to run
|
||||
// runAnalyticsExamples().catch(console.error);
|
||||
Reference in New Issue
Block a user