Merge Google Search Console data with audit results, compute Pearson correlations, analyze trends, detect anomalies, and build dashboard-ready aggregated outputs.

@power-seo/analytics is the data intelligence layer of the @power-seo ecosystem. It answers the question that every SEO practitioner asks but most tools cannot answer well: does improving the SEO audit score of a page actually increase its organic traffic? By merging Google Search Console performance data with audit results — keyed on normalized URLs — and computing the Pearson correlation between audit scores and click counts, you can verify the relationship empirically across your own site.

Beyond correlation, the package provides a full trend analysis pipeline: time-series data flows through analyzeTrend (direction, rate of change, confidence) → buildTrendLines (chart-ready data points) → detectAnomalies (statistically significant spikes and drops). The ranking module groups your queries into position buckets (1–3, 4–10, 11–20, 21–50, 50+) matching how SEO professionals think about ranking tiers, and trackPositionChanges produces a before/after diff across two snapshots. Everything culminates in buildDashboardData, which aggregates all of the above into a single structured object ready to be consumed by any charting library or reporting UI.

Documentation

Package docs: apps/docs/src/content/docs/packages/analytics.mdx
Ecosystem overview: README.md
Contributing guide: CONTRIBUTING.md

Features

GSC + audit data merge — mergeGscWithAudit joins Google Search Console page performance data (clicks, impressions, CTR, position) with @power-seo/audit results by normalized URL, producing PageInsight objects with both dimensions
Pearson correlation — correlateScoreAndTraffic computes the Pearson correlation coefficient between audit scores and click counts across all merged page insights, returning the coefficient and a significance indicator
Trend direction analysis — analyzeTrend detects whether a time series is trending up, down, or stable, with a rate-of-change value and a confidence score based on the linearity of the data
Chart-ready trend lines — buildTrendLines converts raw data points into smoothed time-series arrays suitable for direct consumption by Recharts, Chart.js, or any other charting library
Anomaly detection — detectAnomalies flags statistically significant spikes and drops in a time series using a configurable standard deviation threshold, returning annotated anomaly objects with timestamps and delta values
Position bucket analysis — analyzeQueryRankings groups queries into five standard SERP ranking tiers: 1–3 (top spots), 4–10 (first page), 11–20 (second page), 21–50 (deep pages), 50+ (not ranking meaningfully)
Position change tracking — trackPositionChanges compares two ranking snapshots and produces a list of PositionChange objects showing which queries improved, declined, or are new/dropped
Dashboard aggregation — buildDashboardData accepts raw GSC pages, GSC queries, and audit results and returns a structured DashboardData object containing: overview metrics, top pages by traffic, top queries by clicks, trend lines, and a prioritized issue list
Normalized URL matching — URL normalization handles trailing slashes, protocol differences, and query string variations so that GSC URLs and audit URLs match correctly even when they are not identical strings
Zero runtime dependencies — pure TypeScript computation
Type-safe throughout — complete TypeScript types for all data structures

Installation

# npm
npm install @power-seo/analytics

# yarn
yarn add @power-seo/analytics

# pnpm
pnpm add @power-seo/analytics

Quick Start

import { mergeGscWithAudit, buildDashboardData } from '@power-seo/analytics';

const dashboard = buildDashboardData({
  gscPages: [
    { url: '/blog/react-seo', clicks: 1240, impressions: 18500, ctr: 0.067, position: 4.2 },
    { url: '/blog/meta-tags', clicks: 380, impressions: 9200, ctr: 0.041, position: 8.7 },
    { url: '/blog/seo-audit', clicks: 55, impressions: 3100, ctr: 0.018, position: 19.1 },
  ],
  gscQueries: [
    { query: 'react seo guide', clicks: 820, impressions: 9400, ctr: 0.087, position: 3.1 },
    { query: 'meta tags react', clicks: 290, impressions: 5800, ctr: 0.05, position: 7.4 },
  ],
  auditResults: [
    { url: '/blog/react-seo', score: 88, issues: [] },
    { url: '/blog/meta-tags', score: 71, issues: [] },
    { url: '/blog/seo-audit', score: 44, issues: [] },
  ],
});

console.log(dashboard.overview.totalClicks); // 1675
console.log(dashboard.overview.averagePosition); // 10.67
console.log(dashboard.overview.averageAuditScore); // 67.7
console.log(dashboard.topPages[0].url); // '/blog/react-seo'

Usage

Merge GSC and Audit Data

mergeGscWithAudit normalizes and joins GSC page data with audit results. Pages that exist in one set but not the other are included with null values for the missing dimension.

import { mergeGscWithAudit } from '@power-seo/analytics';
import type { GscPageData, AuditSnapshot, PageInsight } from '@power-seo/analytics';

const gscPages: GscPageData[] = [
  {
    url: 'https://example.com/blog/post-1',
    clicks: 850,
    impressions: 12000,
    ctr: 0.071,
    position: 5.3,
  },
  {
    url: 'https://example.com/blog/post-2',
    clicks: 220,
    impressions: 6500,
    ctr: 0.034,
    position: 12.1,
  },
];

const auditResults: AuditSnapshot[] = [
  { url: '/blog/post-1', score: 91, issues: [] },
  {
    url: '/blog/post-2',
    score: 63,
    issues: [{ rule: 'meta-description', severity: 'error', message: '...' }],
  },
];

const insights: PageInsight[] = mergeGscWithAudit({ gscPages, auditResults });

insights.forEach(({ url, clicks, position, auditScore }) => {
  console.log(`${url}: ${clicks} clicks @ pos ${position}, score=${auditScore ?? 'N/A'}`);
});

Correlation Analysis

correlateScoreAndTraffic answers the core question: does better audit score correlate with more traffic?

import { mergeGscWithAudit, correlateScoreAndTraffic } from '@power-seo/analytics';

const insights = mergeGscWithAudit({ gscPages, auditResults });
const correlation = correlateScoreAndTraffic(insights);

console.log(`Pearson r: ${correlation.coefficient.toFixed(3)}`);
// e.g. 0.741 — strong positive correlation

console.log(`Statistically significant: ${correlation.significant}`);

if (correlation.coefficient > 0.5) {
  console.log('Strong positive relationship: improving audit scores tends to increase traffic');
}

Trend Analysis

import { analyzeTrend, buildTrendLines } from '@power-seo/analytics';
import type { TrendPoint, TrendAnalysis } from '@power-seo/analytics';

const weeklyClicks: TrendPoint[] = [
  { date: '2026-01-05', value: 1200 },
  { date: '2026-01-12', value: 1350 },
  { date: '2026-01-19', value: 1280 },
  { date: '2026-01-26', value: 1480 },
  { date: '2026-02-02', value: 1620 },
  { date: '2026-02-09', value: 1590 },
];

const trend: TrendAnalysis = analyzeTrend(weeklyClicks);
console.log(trend.direction); // 'up' | 'down' | 'stable'
console.log(trend.rate); // % change per period, e.g. 5.8
console.log(trend.confidence); // 0-1, linearity confidence, e.g. 0.87

// Build chart-ready trend line data
const trendLines = buildTrendLines(weeklyClicks);
// [{ date: '2026-01-05', actual: 1200, trend: 1195 }, ...]

Anomaly Detection

import { detectAnomalies } from '@power-seo/analytics';

const dailyImpressions: TrendPoint[] = [
  { date: '2026-02-01', value: 8500 },
  { date: '2026-02-02', value: 8900 },
  { date: '2026-02-03', value: 8700 },
  { date: '2026-02-04', value: 8600 },
  { date: '2026-02-05', value: 24800 }, // spike — content went viral?
  { date: '2026-02-06', value: 9100 },
  { date: '2026-02-07', value: 2100 }, // drop — server issue?
  { date: '2026-02-08', value: 8800 },
];

const anomalies = detectAnomalies(dailyImpressions, { threshold: 2.0 });
anomalies.forEach(({ date, value, type, delta }) => {
  console.log(`${date}: ${type} anomaly — value=${value}, delta=${delta.toFixed(0)} from baseline`);
});
// 2026-02-05: spike anomaly — value=24800, delta=16156 from baseline
// 2026-02-07: drop anomaly — value=2100, delta=-6514 from baseline

Ranking Analysis

import { analyzeQueryRankings } from '@power-seo/analytics';
import type { GscQueryData, RankingAnalysis } from '@power-seo/analytics';

const queries: GscQueryData[] = [
  { query: 'react seo', clicks: 820, impressions: 9400, ctr: 0.087, position: 2.1 },
  { query: 'seo audit tool', clicks: 340, impressions: 6200, ctr: 0.055, position: 6.8 },
  { query: 'meta tags guide', clicks: 180, impressions: 4800, ctr: 0.038, position: 14.3 },
  { query: 'sitemap generator', clicks: 30, impressions: 2100, ctr: 0.014, position: 28.7 },
  { query: 'seo typescript', clicks: 5, impressions: 1200, ctr: 0.004, position: 67.2 },
];

const analysis: RankingAnalysis = analyzeQueryRankings(queries);

console.log('Position 1-3:', analysis.buckets['1-3'].length, 'queries');
console.log('Position 4-10:', analysis.buckets['4-10'].length, 'queries');
console.log('Position 11-20:', analysis.buckets['11-20'].length, 'queries');
console.log('Position 21-50:', analysis.buckets['21-50'].length, 'queries');
console.log('Position 50+:', analysis.buckets['50+'].length, 'queries');

// Quick-win opportunities: ranked 11-20 with good impressions
const quickWins = analysis.buckets['11-20'].filter((q) => q.impressions > 2000);
console.log(
  'Quick-win queries:',
  quickWins.map((q) => q.query),
);

Position Change Tracking

import { trackPositionChanges } from '@power-seo/analytics';
import type { PositionChange } from '@power-seo/analytics';

const snapshot1: GscQueryData[] = [
  { query: 'react seo guide', clicks: 320, impressions: 8200, ctr: 0.039, position: 8.4 },
  { query: 'seo audit', clicks: 45, impressions: 1900, ctr: 0.024, position: 22.0 },
];

const snapshot2: GscQueryData[] = [
  { query: 'react seo guide', clicks: 580, impressions: 9800, ctr: 0.059, position: 5.1 },
  { query: 'seo audit', clicks: 88, impressions: 2200, ctr: 0.04, position: 14.3 },
  { query: 'seo typescript', clicks: 12, impressions: 800, ctr: 0.015, position: 31.0 },
];

const changes: PositionChange[] = trackPositionChanges(snapshot1, snapshot2);
changes.forEach(({ query, before, after, delta, direction }) => {
  const arrow = direction === 'improved' ? '↑' : direction === 'declined' ? '↓' : '→';
  console.log(`${arrow} "${query}": ${before ?? 'new'} → ${after ?? 'dropped'}`);
});
// ↑ "react seo guide": 8.4 → 5.1
// ↑ "seo audit": 22.0 → 14.3
// → "seo typescript": new → 31.0

Dashboard Data

import { buildDashboardData } from '@power-seo/analytics';
import type { DashboardInput, DashboardData } from '@power-seo/analytics';

const dashboard: DashboardData = buildDashboardData({
  gscPages: allGscPages,
  gscQueries: allGscQueries,
  auditResults: allAuditResults,
  topN: 10,
});

// Overview metrics
console.log(dashboard.overview.totalClicks);
console.log(dashboard.overview.totalImpressions);
console.log(dashboard.overview.averageCtr);
console.log(dashboard.overview.averagePosition);
console.log(dashboard.overview.averageAuditScore);

// Top pages by clicks
dashboard.topPages.forEach(({ url, clicks, auditScore }) =>
  console.log(`${url}: ${clicks} clicks, score=${auditScore ?? 'N/A'}`),
);

// Top queries
dashboard.topQueries.forEach(({ query, clicks, position }) =>
  console.log(`"${query}": ${clicks} clicks @ position ${position.toFixed(1)}`),
);

// Trend lines ready for Recharts / Chart.js
dashboard.trendLines; // TrendPoint[]

// Prioritized issues — high-traffic pages with low audit scores
dashboard.issues.forEach(({ rule, severity, affectedPages }) =>
  console.log(`[${severity}] ${rule}: affects ${affectedPages} pages`),
);

API Reference

`mergeGscWithAudit(input)`

Parameter	Type	Default	Description
`input.gscPages`	`GscPageData[]`	required	Google Search Console page performance data
`input.auditResults`	`AuditSnapshot[]`	required	Audit results with URL, score, and issues

Returns PageInsight[] — merged records keyed by normalized URL.

`correlateScoreAndTraffic(insights)`

Parameter	Type	Default	Description
`insights`	`PageInsight[]`	required	Merged page insights with both audit score and click data

Returns { coefficient: number; significant: boolean; sampleSize: number }.

`analyzeTrend(points)`

Parameter	Type	Default	Description
`points`	`TrendPoint[]`	required	Time-ordered `{ date: string; value: number }` array

Returns TrendAnalysis: { direction: TrendDirection; rate: number; confidence: number }.

`buildTrendLines(points)`

Parameter	Type	Default	Description
`points`	`TrendPoint[]`	required	Time-ordered data points

Returns Array<{ date: string; actual: number; trend: number }>.

`detectAnomalies(points, options?)`

Parameter	Type	Default	Description
`points`	`TrendPoint[]`	required	Time-ordered data points
`options.threshold`	`number`	`2.0`	Standard deviation multiplier for anomaly detection

Returns Array<{ date: string; value: number; type: 'spike' | 'drop'; delta: number }>.

`analyzeQueryRankings(queries)`

Parameter	Type	Default	Description
`queries`	`GscQueryData[]`	required	GSC query data with position values

Returns RankingAnalysis: { buckets: Record<RankingBucket, GscQueryData[]>; totalQueries: number }.

`trackPositionChanges(before, after)`

Parameter	Type	Default	Description
`before`	`GscQueryData[]`	required	Ranking snapshot from the earlier period
`after`	`GscQueryData[]`	required	Ranking snapshot from the later period

Returns PositionChange[].

`buildDashboardData(input)`

Parameter	Type	Default	Description
`input.gscPages`	`GscPageData[]`	required	GSC page performance data
`input.gscQueries`	`GscQueryData[]`	required	GSC query performance data
`input.auditResults`	`AuditSnapshot[]`	required	Audit results for correlation and issue aggregation
`input.trendData`	`TrendPoint[]`	`[]`	Optional time-series data for trend charts
`input.topN`	`number`	`10`	Number of top pages/queries to include

Returns DashboardData.

Types

import type {
  GscPageData, // { url, clicks, impressions, ctr, position }
  GscQueryData, // { query, clicks, impressions, ctr, position }
  AuditSnapshot, // { url, score, issues }
  TrendPoint, // { date: string; value: number }
  TrendDirection, // 'up' | 'down' | 'stable'
  TrendAnalysis, // { direction, rate, confidence }
  PageInsight, // Merged GscPageData + AuditSnapshot
  RankingBucket, // '1-3' | '4-10' | '11-20' | '21-50' | '50+'
  RankingAnalysis, // { buckets: Record<RankingBucket, GscQueryData[]>; totalQueries }
  PositionChange, // { query, before?, after?, delta?, direction }
  DashboardInput, // { gscPages, gscQueries, auditResults, trendData?, topN? }
  DashboardOverview, // { totalClicks, totalImpressions, averageCtr, averagePosition, averageAuditScore, pagesWithErrors }
  DashboardData, // { overview, topPages, topQueries, trendLines, issues }
} from '@power-seo/analytics';

The @power-seo Ecosystem

@power-seo/analytics is part of the @power-seo monorepo — a complete, modular SEO toolkit for modern JavaScript applications.

Package	Install	Description
`@power-seo/core`	`npm i @power-seo/core`	Framework-agnostic utilities, types, validators, and constants
`@power-seo/react`	`npm i @power-seo/react`	React SEO components — meta, Open Graph, Twitter Card, breadcrumbs
`@power-seo/meta`	`npm i @power-seo/meta`	SSR meta helpers for Next.js App Router, Remix v2, and generic SSR
`@power-seo/schema`	`npm i @power-seo/schema`	Type-safe JSON-LD structured data — 20 builders + 18 React components
`@power-seo/content-analysis`	`npm i @power-seo/content-analysis`	Yoast-style SEO content scoring engine with React components
`@power-seo/readability`	`npm i @power-seo/readability`	Readability scoring — Flesch-Kincaid, Gunning Fog, Coleman-Liau, ARI
`@power-seo/preview`	`npm i @power-seo/preview`	SERP, Open Graph, and Twitter/X Card preview generators
`@power-seo/sitemap`	`npm i @power-seo/sitemap`	XML sitemap generation, streaming, index splitting, and validation
`@power-seo/redirects`	`npm i @power-seo/redirects`	Redirect engine with Next.js, Remix, and Express adapters
`@power-seo/links`	`npm i @power-seo/links`	Link graph analysis — orphan detection, suggestions, equity scoring
`@power-seo/audit`	`npm i @power-seo/audit`	Full SEO audit engine — meta, content, structure, performance rules
`@power-seo/images`	`npm i @power-seo/images`	Image SEO — alt text, lazy loading, format analysis, image sitemaps
`@power-seo/ai`	`npm i @power-seo/ai`	LLM-agnostic AI prompt templates and parsers for SEO tasks
`@power-seo/analytics`	`npm i @power-seo/analytics`	Merge GSC + audit data, trend analysis, ranking insights, dashboard
`@power-seo/search-console`	`npm i @power-seo/search-console`	Google Search Console API — OAuth2, service account, URL inspection
`@power-seo/integrations`	`npm i @power-seo/integrations`	Semrush and Ahrefs API clients with rate limiting and pagination
`@power-seo/tracking`	`npm i @power-seo/tracking`	GA4, Clarity, PostHog, Plausible, Fathom — scripts + consent management

About CyberCraft Bangladesh

CyberCraft Bangladesh is a Bangladesh-based enterprise-grade software engineering company specializing in ERP system development, AI-powered SaaS and business applications, full-stack SEO services, custom website development, and scalable eCommerce platforms. We design and develop intelligent, automation-driven SaaS and enterprise solutions that help startups, SMEs, NGOs, educational institutes, and large organizations streamline operations, enhance digital visibility, and accelerate growth through modern cloud-native technologies.


Website	ccbd.dev
GitHub	github.com/cybercraftbd
npm Organization	npmjs.com/org/power-seo
Email	info@ccbd.dev

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Package last updated on 22 Feb 2026

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@power-seo/analytics