Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
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"use strict";
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/**
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* Ad Optimization Simulator
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*
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* Generates optimization scenario data including:
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* - Budget allocation simulations
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* - Bid strategy testing data
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* - Audience segmentation data
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* - Creative performance variations
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* - ROAS optimization scenarios
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.runOptimizationExamples = runOptimizationExamples;
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exports.simulateBudgetAllocation = simulateBudgetAllocation;
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exports.simulateBidStrategies = simulateBidStrategies;
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exports.simulateAudienceSegmentation = simulateAudienceSegmentation;
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exports.simulateCreativePerformance = simulateCreativePerformance;
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exports.simulateROASOptimization = simulateROASOptimization;
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exports.simulateOptimizationImpact = simulateOptimizationImpact;
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exports.simulateMultiVariateTesting = simulateMultiVariateTesting;
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exports.simulateDaypartingOptimization = simulateDaypartingOptimization;
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exports.simulateGeoTargetingOptimization = simulateGeoTargetingOptimization;
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exports.simulateBatchOptimization = simulateBatchOptimization;
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const index_js_1 = require("../../src/index.js");
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// Example 1: Budget allocation simulation
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async function simulateBudgetAllocation() {
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const synth = (0, index_js_1.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 budgetSchema = {
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scenarioId: { type: 'string', required: true },
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scenarioName: { type: 'string', required: true },
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totalBudget: { type: 'number', required: true },
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timeframe: { type: 'string', required: true },
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allocation: {
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type: 'object',
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required: true,
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properties: {
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googleAds: {
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type: 'object',
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properties: {
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budget: { type: 'number' },
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percentage: { type: 'number' },
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expectedImpressions: { type: 'number' },
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expectedClicks: { type: 'number' },
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expectedConversions: { type: 'number' },
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expectedRevenue: { type: 'number' },
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expectedROAS: { type: 'number' }
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}
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},
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facebookAds: {
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type: 'object',
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properties: {
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budget: { type: 'number' },
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percentage: { type: 'number' },
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expectedImpressions: { type: 'number' },
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expectedClicks: { type: 'number' },
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expectedConversions: { type: 'number' },
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expectedRevenue: { type: 'number' },
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expectedROAS: { type: 'number' }
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}
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},
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tiktokAds: {
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type: 'object',
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properties: {
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budget: { type: 'number' },
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percentage: { type: 'number' },
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expectedImpressions: { type: 'number' },
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expectedClicks: { type: 'number' },
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expectedConversions: { type: 'number' },
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expectedRevenue: { type: 'number' },
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expectedROAS: { type: 'number' }
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}
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}
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}
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},
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projectedROAS: { type: 'number', required: true },
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projectedRevenue: { type: 'number', required: true },
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riskScore: { type: 'number', required: true },
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confidenceInterval: { type: 'object', required: true }
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};
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const result = await synth.generateStructured({
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count: 50,
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schema: budgetSchema,
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constraints: {
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totalBudget: { min: 10000, max: 500000 },
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timeframe: ['daily', 'weekly', 'monthly', 'quarterly'],
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projectedROAS: { min: 1.0, max: 10.0 },
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riskScore: { min: 0.1, max: 0.9 }
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}
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});
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console.log('Budget Allocation Simulations:');
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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: Bid strategy testing
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async function simulateBidStrategies() {
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const synth = (0, index_js_1.createSynth)({
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provider: 'gemini'
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});
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const bidStrategySchema = {
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strategyId: { type: 'string', required: true },
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strategyName: { type: 'string', required: true },
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platform: { type: 'string', required: true },
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strategyType: { type: 'string', required: true },
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configuration: {
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type: 'object',
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required: true,
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properties: {
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targetCPA: { type: 'number' },
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targetROAS: { type: 'number' },
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maxCPC: { type: 'number' },
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bidAdjustments: { type: 'object' }
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}
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},
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historicalPerformance: {
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type: 'object',
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required: true,
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properties: {
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avgCPC: { type: 'number' },
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avgCPA: { type: 'number' },
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avgROAS: { type: 'number' },
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conversionRate: { type: 'number' },
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impressionShare: { type: 'number' }
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}
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},
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simulatedResults: {
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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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scenario: { type: 'string' },
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budget: { 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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cost: { type: 'number' },
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revenue: { 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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}
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}
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},
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recommendedBid: { type: 'number', required: true },
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expectedImprovement: { type: 'number', required: true }
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};
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const result = await synth.generateStructured({
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count: 40,
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schema: bidStrategySchema,
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constraints: {
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platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads'],
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strategyType: [
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'manual_cpc',
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'enhanced_cpc',
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'target_cpa',
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'target_roas',
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'maximize_conversions',
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'maximize_conversion_value'
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],
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simulatedResults: { minLength: 3, maxLength: 5 },
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expectedImprovement: { min: -0.2, max: 0.5 }
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}
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});
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console.log('Bid Strategy Simulations:');
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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: Audience segmentation testing
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async function simulateAudienceSegmentation() {
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const synth = (0, index_js_1.createSynth)({
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provider: 'gemini'
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});
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const audienceSchema = {
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segmentId: { type: 'string', required: true },
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segmentName: { type: 'string', required: true },
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platform: { type: 'string', required: true },
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segmentType: { type: 'string', required: true },
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demographics: {
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type: 'object',
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required: true,
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properties: {
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ageRange: { type: 'string' },
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gender: { type: 'string' },
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location: { type: 'array' },
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income: { type: 'string' },
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education: { type: 'string' }
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}
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},
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interests: { type: 'array', required: true },
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behaviors: { type: 'array', required: true },
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size: { type: 'number', required: true },
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performance: {
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type: 'object',
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required: true,
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properties: {
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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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cpa: { type: 'number' },
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roas: { type: 'number' },
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ltv: { type: 'number' }
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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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recommendedBudget: { type: 'number' },
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recommendedBid: { type: 'number' },
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expectedROAS: { type: 'number' },
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scalingPotential: { type: 'string' }
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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: 60,
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schema: audienceSchema,
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constraints: {
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platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads'],
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segmentType: [
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'lookalike',
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'custom',
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'remarketing',
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'interest_based',
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'behavioral',
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'demographic'
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],
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size: { min: 10000, max: 10000000 },
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scalingPotential: ['low', 'medium', 'high']
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}
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});
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console.log('Audience Segmentation 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: Creative performance variations
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async function simulateCreativePerformance() {
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const synth = (0, index_js_1.createSynth)({
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provider: 'gemini'
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});
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const creativeSchema = {
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creativeId: { type: 'string', required: true },
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creativeName: { type: 'string', required: true },
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platform: { type: 'string', required: true },
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format: { type: 'string', required: true },
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elements: {
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type: 'object',
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required: true,
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properties: {
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headline: { type: 'string' },
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description: { type: 'string' },
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cta: { type: 'string' },
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imageUrl: { type: 'string' },
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videoUrl: { type: 'string' },
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videoDuration: { type: 'number' }
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}
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},
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variations: {
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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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variationId: { type: 'string' },
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variationName: { type: 'string' },
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changeDescription: { type: 'string' },
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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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cpa: { type: 'number' },
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roas: { type: 'number' },
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engagementRate: { type: 'number' }
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}
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}
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},
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bestPerforming: { type: 'string', required: true },
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performanceLift: { type: 'number', required: true },
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recommendation: { type: 'string', required: true }
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};
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const result = await synth.generateStructured({
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count: 50,
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schema: creativeSchema,
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constraints: {
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platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads', 'Instagram Ads'],
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format: [
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'image_ad',
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'video_ad',
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'carousel_ad',
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'collection_ad',
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'story_ad',
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'responsive_display'
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],
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variations: { minLength: 2, maxLength: 5 },
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performanceLift: { min: -0.3, max: 2.0 }
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}
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});
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console.log('Creative Performance Variations:');
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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 5: ROAS optimization scenarios
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async function simulateROASOptimization() {
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const synth = (0, index_js_1.createSynth)({
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provider: 'gemini'
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});
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const roasSchema = {
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optimizationId: { type: 'string', required: true },
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optimizationName: { type: 'string', required: true },
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currentState: {
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type: 'object',
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required: true,
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properties: {
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totalSpend: { type: 'number' },
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totalRevenue: { type: 'number' },
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currentROAS: { type: 'number' },
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campaignCount: { type: 'number' },
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activeChannels: { type: 'array' }
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}
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},
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optimizationScenarios: {
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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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scenarioId: { type: 'string' },
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scenarioName: { type: 'string' },
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changes: { type: 'array' },
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projectedSpend: { type: 'number' },
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projectedRevenue: { type: 'number' },
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projectedROAS: { type: 'number' },
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roasImprovement: { type: 'number' },
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implementationDifficulty: { type: 'string' },
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estimatedTimeframe: { type: 'string' },
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riskLevel: { type: 'string' }
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}
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}
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},
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recommendations: {
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type: 'object',
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required: true,
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properties: {
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primaryRecommendation: { type: 'string' },
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quickWins: { type: 'array' },
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longTermStrategies: { type: 'array' },
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budgetReallocation: { type: 'object' }
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}
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},
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expectedOutcome: {
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type: 'object',
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required: true,
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properties: {
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targetROAS: { type: 'number' },
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targetRevenue: { type: 'number' },
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timeToTarget: { type: 'string' },
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confidenceLevel: { type: 'number' }
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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: roasSchema,
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constraints: {
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'currentState.currentROAS': { min: 0.5, max: 5.0 },
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optimizationScenarios: { minLength: 3, maxLength: 6 },
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'expectedOutcome.targetROAS': { min: 2.0, max: 10.0 },
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'expectedOutcome.confidenceLevel': { min: 0.6, max: 0.95 }
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}
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});
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console.log('ROAS Optimization Scenarios:');
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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: Time-series optimization impact
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async function simulateOptimizationImpact() {
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const synth = (0, index_js_1.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: 90,
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startDate: new Date(Date.now() - 90 * 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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'baseline_roas',
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'optimized_roas',
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'baseline_revenue',
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'optimized_revenue',
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'baseline_cpa',
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'optimized_cpa',
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'improvement_percentage'
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],
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trend: 'up',
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seasonality: true,
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noise: 0.1,
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constraints: {
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baseline_roas: { min: 2.0, max: 4.0 },
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optimized_roas: { min: 2.5, max: 8.0 },
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baseline_revenue: { min: 5000, max: 50000 },
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optimized_revenue: { min: 6000, max: 80000 },
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improvement_percentage: { min: 0, max: 100 }
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||||
}
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||||
});
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console.log('Optimization Impact Time-Series:');
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console.log(result.data.slice(0, 7));
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return result;
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||||
}
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// Example 7: Multi-variate testing simulation
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async function simulateMultiVariateTesting() {
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const synth = (0, index_js_1.createSynth)({
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provider: 'gemini'
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});
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const mvtSchema = {
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testId: { type: 'string', required: true },
|
||||
testName: { type: 'string', required: true },
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platform: { type: 'string', required: true },
|
||||
startDate: { type: 'string', required: true },
|
||||
endDate: { type: 'string', required: true },
|
||||
testFactors: {
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type: 'array',
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||||
required: true,
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||||
items: {
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||||
type: 'object',
|
||||
properties: {
|
||||
factor: { type: 'string' },
|
||||
variations: { type: 'array' }
|
||||
}
|
||||
}
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||||
},
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||||
combinations: {
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||||
type: 'array',
|
||||
required: true,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
combinationId: { type: 'string' },
|
||||
factors: { type: 'object' },
|
||||
impressions: { type: 'number' },
|
||||
clicks: { type: 'number' },
|
||||
conversions: { type: 'number' },
|
||||
spend: { type: 'number' },
|
||||
revenue: { type: 'number' },
|
||||
ctr: { type: 'number' },
|
||||
cvr: { type: 'number' },
|
||||
cpa: { type: 'number' },
|
||||
roas: { type: 'number' },
|
||||
score: { type: 'number' }
|
||||
}
|
||||
}
|
||||
},
|
||||
winningCombination: { type: 'string', required: true },
|
||||
keyInsights: { type: 'array', required: true },
|
||||
implementationPlan: { type: 'string', required: true }
|
||||
};
|
||||
const result = await synth.generateStructured({
|
||||
count: 25,
|
||||
schema: mvtSchema,
|
||||
constraints: {
|
||||
platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads'],
|
||||
testFactors: { minLength: 2, maxLength: 4 },
|
||||
combinations: { minLength: 4, maxLength: 16 }
|
||||
}
|
||||
});
|
||||
console.log('Multi-Variate Testing Results:');
|
||||
console.log(result.data.slice(0, 2));
|
||||
return result;
|
||||
}
|
||||
// Example 8: Dayparting optimization
|
||||
async function simulateDaypartingOptimization() {
|
||||
const synth = (0, index_js_1.createSynth)({
|
||||
provider: 'gemini'
|
||||
});
|
||||
const daypartingSchema = {
|
||||
analysisId: { type: 'string', required: true },
|
||||
campaign: { type: 'string', required: true },
|
||||
platform: { type: 'string', required: true },
|
||||
timezone: { type: 'string', required: true },
|
||||
hourlyPerformance: {
|
||||
type: 'array',
|
||||
required: true,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
hour: { type: 'number' },
|
||||
dayOfWeek: { type: 'string' },
|
||||
impressions: { type: 'number' },
|
||||
clicks: { type: 'number' },
|
||||
conversions: { type: 'number' },
|
||||
spend: { type: 'number' },
|
||||
revenue: { type: 'number' },
|
||||
ctr: { type: 'number' },
|
||||
cvr: { type: 'number' },
|
||||
cpa: { type: 'number' },
|
||||
roas: { type: 'number' },
|
||||
competitionLevel: { type: 'string' }
|
||||
}
|
||||
}
|
||||
},
|
||||
recommendations: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
peakHours: { type: 'array' },
|
||||
bidAdjustments: { type: 'object' },
|
||||
budgetAllocation: { type: 'object' },
|
||||
expectedImprovement: { type: 'number' }
|
||||
}
|
||||
}
|
||||
};
|
||||
const result = await synth.generateStructured({
|
||||
count: 20,
|
||||
schema: daypartingSchema,
|
||||
constraints: {
|
||||
platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads'],
|
||||
hourlyPerformance: { minLength: 168, maxLength: 168 }, // 24 hours x 7 days
|
||||
'recommendations.expectedImprovement': { min: 0.05, max: 0.5 }
|
||||
}
|
||||
});
|
||||
console.log('Dayparting Optimization Data:');
|
||||
console.log(result.data.slice(0, 1));
|
||||
return result;
|
||||
}
|
||||
// Example 9: Geo-targeting optimization
|
||||
async function simulateGeoTargetingOptimization() {
|
||||
const synth = (0, index_js_1.createSynth)({
|
||||
provider: 'gemini'
|
||||
});
|
||||
const geoSchema = {
|
||||
analysisId: { type: 'string', required: true },
|
||||
campaign: { type: 'string', required: true },
|
||||
platform: { type: 'string', required: true },
|
||||
locationPerformance: {
|
||||
type: 'array',
|
||||
required: true,
|
||||
items: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
locationId: { type: 'string' },
|
||||
locationName: { type: 'string' },
|
||||
locationType: { type: 'string' },
|
||||
population: { type: 'number' },
|
||||
impressions: { type: 'number' },
|
||||
clicks: { type: 'number' },
|
||||
conversions: { type: 'number' },
|
||||
spend: { type: 'number' },
|
||||
revenue: { type: 'number' },
|
||||
ctr: { type: 'number' },
|
||||
cvr: { type: 'number' },
|
||||
cpa: { type: 'number' },
|
||||
roas: { type: 'number' },
|
||||
marketPotential: { type: 'string' }
|
||||
}
|
||||
}
|
||||
},
|
||||
optimization: {
|
||||
type: 'object',
|
||||
required: true,
|
||||
properties: {
|
||||
topPerformingLocations: { type: 'array' },
|
||||
underperformingLocations: { type: 'array' },
|
||||
expansionOpportunities: { type: 'array' },
|
||||
bidAdjustments: { type: 'object' },
|
||||
expectedROASImprovement: { type: 'number' }
|
||||
}
|
||||
}
|
||||
};
|
||||
const result = await synth.generateStructured({
|
||||
count: 15,
|
||||
schema: geoSchema,
|
||||
constraints: {
|
||||
platform: ['Google Ads', 'Facebook Ads', 'TikTok Ads'],
|
||||
locationPerformance: { minLength: 10, maxLength: 50 },
|
||||
'optimization.expectedROASImprovement': { min: 0.1, max: 1.0 }
|
||||
}
|
||||
});
|
||||
console.log('Geo-Targeting Optimization Data:');
|
||||
console.log(result.data.slice(0, 1));
|
||||
return result;
|
||||
}
|
||||
// Example 10: Batch optimization simulation
|
||||
async function simulateBatchOptimization() {
|
||||
const synth = (0, index_js_1.createSynth)({
|
||||
provider: 'gemini'
|
||||
});
|
||||
const scenarios = [
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
scenarioType: { type: 'string' },
|
||||
currentROAS: { type: 'number' },
|
||||
optimizedROAS: { type: 'number' },
|
||||
improvement: { type: 'number' }
|
||||
},
|
||||
constraints: { scenarioType: 'budget_allocation' }
|
||||
},
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
scenarioType: { type: 'string' },
|
||||
currentROAS: { type: 'number' },
|
||||
optimizedROAS: { type: 'number' },
|
||||
improvement: { type: 'number' }
|
||||
},
|
||||
constraints: { scenarioType: 'bid_strategy' }
|
||||
},
|
||||
{
|
||||
count: 20,
|
||||
schema: {
|
||||
scenarioType: { type: 'string' },
|
||||
currentROAS: { type: 'number' },
|
||||
optimizedROAS: { type: 'number' },
|
||||
improvement: { type: 'number' }
|
||||
},
|
||||
constraints: { scenarioType: 'audience_targeting' }
|
||||
}
|
||||
];
|
||||
const results = await synth.generateBatch('structured', scenarios, 3);
|
||||
console.log('Batch Optimization Results:');
|
||||
results.forEach((result, i) => {
|
||||
const types = ['Budget Allocation', 'Bid Strategy', 'Audience Targeting'];
|
||||
console.log(`${types[i]}: ${result.metadata.count} scenarios in ${result.metadata.duration}ms`);
|
||||
console.log('Sample:', result.data.slice(0, 2));
|
||||
});
|
||||
return results;
|
||||
}
|
||||
// Run all examples
|
||||
async function runOptimizationExamples() {
|
||||
console.log('=== Example 1: Budget Allocation ===');
|
||||
await simulateBudgetAllocation();
|
||||
console.log('\n=== Example 2: Bid Strategies ===');
|
||||
await simulateBidStrategies();
|
||||
console.log('\n=== Example 3: Audience Segmentation ===');
|
||||
await simulateAudienceSegmentation();
|
||||
console.log('\n=== Example 4: Creative Performance ===');
|
||||
await simulateCreativePerformance();
|
||||
console.log('\n=== Example 5: ROAS Optimization ===');
|
||||
await simulateROASOptimization();
|
||||
console.log('\n=== Example 6: Optimization Impact ===');
|
||||
await simulateOptimizationImpact();
|
||||
console.log('\n=== Example 7: Multi-Variate Testing ===');
|
||||
await simulateMultiVariateTesting();
|
||||
console.log('\n=== Example 8: Dayparting Optimization ===');
|
||||
await simulateDaypartingOptimization();
|
||||
console.log('\n=== Example 9: Geo-Targeting Optimization ===');
|
||||
await simulateGeoTargetingOptimization();
|
||||
console.log('\n=== Example 10: Batch Optimization ===');
|
||||
await simulateBatchOptimization();
|
||||
}
|
||||
// Uncomment to run
|
||||
// runOptimizationExamples().catch(console.error);
|
||||
//# sourceMappingURL=optimization-simulator.js.map
|
||||
Reference in New Issue
Block a user