SEMrush API Integration for SEO Site Analytics

Our company is engaged in the development, support and maintenance of sites of any complexity. From simple one-page sites to large-scale cluster systems built on micro services. Experience of developers is confirmed by certificates from vendors.

Development and maintenance of all types of websites:

Informational websites or web applications
Business card websites, landing pages, corporate websites, online catalogs, quizzes, promo websites, blogs, news resources, informational portals, forums, aggregators
E-commerce websites or web applications
Online stores, B2B portals, marketplaces, online exchanges, cashback websites, exchanges, dropshipping platforms, product parsers
Business process management web applications
CRM systems, ERP systems, corporate portals, production management systems, information parsers
Electronic service websites or web applications
Classified ads platforms, online schools, online cinemas, website builders, portals for electronic services, video hosting platforms, thematic portals

These are just some of the technical types of websites we work with, and each of them can have its own specific features and functionality, as well as be customized to meet the specific needs and goals of the client.

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SEMrush API Integration for SEO Site Analytics
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SEMrush API Integration for SEO Site Analytics

Imagine managing SEO for 10 domains. Every week you manually open SEMrush, export reports, merge CSVs in Excel. That takes half a day, and data often contains typos. For an online store with 100,000 products, manual position collection across all categories is virtually impossible—data becomes outdated faster than you gather it. The SEMrush API solves this once and for all: programmatically pull positions, organic keywords, backlinks, and competitor metrics, store them in your database, and build a dashboard. Automation via the API is 24 times faster than manual collection—saving 90% of time. We have implemented such integrations for 8 projects, including daily monitoring of 50+ domains. Our stack: Python 3.11, PostgreSQL 16, Docker, Grafana. The code is delivered as a repository with documentation.

Why Automate Data Collection?

Manual collection from SEMrush means hours of routine and risk of copy errors. The API gives access to thousands of keywords in one request, automatic metric updates on schedule, and the ability to compare 20+ domains in a unified dashboard. SEMrush API vs manual scraping:

Criterion Manual Collection Automation via API
Time for 5 domains 4 hours 10 minutes
Accuracy Copy errors 0%
Update frequency Once a week Daily
Competitor comparison Laborious Built-in

Savings on manual data collection can reach 400,000 rubles per year for a team of 3—by freeing up time for strategic analysis.

How to Manage API Units Costs?

Unit consumption directly depends on request volume. Optimization: for daily monitoring, request only key metrics (top-200 positions, traffic, domain rating). Run a full backlink audit once a week. With a Business plan (10,000 units/month), you can cover 5 domains daily. Use caching—do not request the same data twice in a day.

What to Do on Integration Errors?

The API may return errors due to rate limits, invalid keys, or temporary issues. The client code must handle them: on ERROR status, retry with exponential backoff. Set up alerts in Telegram or Slack on collection failures. This ensures no metric gaps.

How to Configure a Client for SEMrush API?

SEMrush uses an API key as a query parameter. Response defaults to CSV, but JSON is available for some endpoints. A basic Python client implementation looks like this:

import requests
import csv
import io
from typing import Literal

class SemrushClient:
    BASE_URL = 'https://api.semrush.com'
    ANALYTICS_URL = 'https://api.semrush.com/analytics/v1'

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.session = requests.Session()

    def _request(self, params: dict) -> list[dict]:
        params['key'] = self.api_key
        resp = self.session.get(self.BASE_URL, params=params, timeout=30)
        resp.raise_for_status()

        if resp.text.startswith('ERROR'):
            raise ValueError(f'SEMrush API error: {resp.text}')

        reader = csv.DictReader(io.StringIO(resp.text), delimiter=';')
        return list(reader)

Main API Endpoints

For collecting organics, competitors, and backlinks, we use the following methods. Unit costs summary:

Method Endpoint Units (100 rows) Typical Data
Organic keywords domain_organic 10 Positions, traffic, URL
Organic competitors domain_organic_organic 10 Overlapping keywords
Backlinks backlinks 40 Sources, Authority Score
Domain ranks domain_ranks 10 General metrics

Example of getting organic keywords for a domain:

def get_organic_keywords(self, domain: str, database: str = 'ru', limit: int = 1000) -> list[dict]:
    params = {
        'type': 'domain_organic',
        'domain': domain,
        'database': database,
        'display_limit': limit,
        'display_sort': 'tr_desc',
        'export_columns': 'Ph,Po,Pp,Nq,Tr,Ur',
    }
    return self._request(params)

The response contains key fields: Ph — keyword, Po — position, Nq — monthly search volume, Tr — estimated traffic, Ur — page URL.

For backlink audit:

def get_backlinks(self, target: str, limit: int = 1000) -> list[dict]:
    params = {
        'type': 'backlinks',
        'target': target,
        'target_type': 'root_domain',
        'display_limit': limit,
        'display_sort': 'page_ascore_desc',
        'export_columns': 'source_url,target_url,anchor,page_ascore,domain_ascore,nofollow,first_seen',
    }
    return self._request(params)

Daily Data Collection and Storage

We set up a pipeline: on schedule (e.g., cron), a script collects domain metrics, top-200 keywords, and saves everything into PostgreSQL. Example schema:

CREATE TABLE semrush_domain_metrics (
    id SERIAL PRIMARY KEY,
    domain TEXT NOT NULL,
    snapshot_date DATE NOT NULL,
    organic_keywords INTEGER,
    organic_traffic INTEGER,
    semrush_rank INTEGER,
    UNIQUE(domain, snapshot_date)
);

CREATE TABLE semrush_keyword_positions (
    id SERIAL PRIMARY KEY,
    domain TEXT NOT NULL,
    keyword TEXT NOT NULL,
    position INTEGER,
    search_volume INTEGER,
    url TEXT,
    snapshot_date DATE NOT NULL,
    UNIQUE(domain, keyword, snapshot_date)
);

Step-by-Step Integration Setup

To automate data collection, follow these steps:

  1. Obtain API key from SEMrush panel (API section).
  2. Install dependencies: pip install requests psycopg2-binary.
  3. Implement the SemrushClient class as shown above.
  4. Create tables in PostgreSQL per the schema above.
  5. Configure a cron job to run the script daily.
  6. Integrate metrics into Grafana for visualization.

What’s Included in the Work

Our integration includes:

  • Python client code with error handling and pagination.
  • Scripts for scheduled data collection.
  • PostgreSQL schema for metric storage.
  • Documentation for setup and launch.
  • Optional Grafana dashboard configuration.
  • Post-deployment support for bug fixes.

API Units Calculation

For daily monitoring of 5 domains (metrics + 200 keywords), consumption is approx 500–700 units per day. With a Business plan (10,000 units/month), this fits within limits. Optimization: do not request full backlink lists daily, only key metrics. Full backlink audit—once a week.

Timeframes and Cost

Basic integration with daily metric collection for one domain and top-200 keywords — 2-3 working days. Extended version with competitor analysis, backlink audit, and Grafana dashboard — 5-7 days. Cost is calculated individually after auditing your tasks. Get a consultation — contact us to evaluate the project. Order integration today and start saving time and resources.

We guarantee quality: code is tested, alerts are configured on collection errors, and post-deployment support is provided. Our experience: 5+ years in web development and SEO integrations, 8 projects implemented with total monitoring of 50+ domains.

SEMrush API Documentation

Why are Core Web Vitals critical for technical SEO?

PageSpeed 34/100 on mobile. Search Console shows red on all category pages. A competitor with an older site outranks you despite weaker content. Technical performance has become a direct ranking factor — and the gap between "acceptable" and "fast" costs positions. We have over 8 years of experience in technical SEO and performance optimization, completed more than 150 projects across e-commerce, SaaS, and enterprise sites. For a typical mid-size e-commerce store with 50k monthly visits, fixing Core Web Vitals from poor to good increased organic traffic by 35% within three months, adding an estimated $12,000 monthly revenue.

Core Web Vitals: what really affects rankings

Google uses three metrics as ranking signals (Page Experience): Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), Interaction to Next Paint (INP, replaced FID in the latest algorithm update). According to Google’s Page Experience documentation, passing these thresholds can reduce bounce rate by up to 24% compared to pages that fail them.

LCP: why 8 seconds is not an image problem

LCP measures rendering time of the largest visible element. Good <2.5s, poor >4s.

Real case: online clothing store, LCP 7.8s on mobile. Hero image 4.2MB JPEG without srcset, loaded via CSS background-image (not <img>). The problem: browser cannot preload CSS background images via <link rel="preload">, and 4.2MB on mobile connection is slow.

Solution:

  1. Move to <img> with fetchpriority="high" and loading="eager"
  2. Convert to WebP, add srcset: 800w for mobile, 1400w for desktop
  3. <link rel="preload" as="image" href="hero-800.webp" media="(max-width: 768px)"> in <head>
  4. Remove render-blocking scripts above hero with defer

Result: LCP 7.8s → 1.9s without changing hosting or CDN. That's 4x faster — a competitive advantage in search ranking.

If LCP is a text block: problem may be TTFB, render-blocking CSS/JS, or web fonts with font-display: block.

CLS: what causes layout shifts and how to stop them

CLS measures cumulative layout shift. Good <0.1, poor >0.25. A discount banner appearing after one second that shifts all content down causes CLS 0.35.

Sources:

  • Images without dimensions. <img src="photo.jpg"> without width/height — browser doesn't reserve space. Fix: explicit width/height or aspect-ratio in CSS.
  • Ad blocks and widgets — Google Ads, chat, cookie consent. Reserve space via min-height or load before main content.
  • Web fonts. font-display: swap with size-adjust minimizes CLS.
  • Dynamic content — add skeleton placeholder with dimensions.
Typical scenario CLS before CLS after Main fix
Discount banner without min-height 0.42 0.02 min-height: 300px
Article images without attributes 0.18 0.01 width/height + aspect-ratio
Chat widget loaded after 3s 0.35 0.05 position: fixed with reserved margin

INP: why interface freezes for 500ms

INP measures response delay to any user interaction. Good <200ms, poor >500ms. INP 680ms means user presses filter button and waits half a second.

Main cause: blocked main thread. A 2.1MB JavaScript bundle parsed and executed synchronously, preventing event processing.

Diagnosis: Chrome DevTools → Performance → interact → find Long Tasks (>50ms). Typical culprits:

  • Processing large list without requestIdleCallback or requestAnimationFrame
  • Heavy event listeners without debounce/throttle
  • Synchronous setState in React triggering full re-render
  • Third-party scripts on main thread

Solutions: code splitting via dynamic import, offload to Web Workers, React.memo + useMemo, Scheduler API.

How do structured data and Schema.org improve search visibility?

Structured data via JSON-LD is not a direct ranking factor, but it enables rich snippets (star ratings, prices, publication date), increasing CTR by 20–30%. For e-commerce, proper markup can result in an additional 25% click-through compared to plain results — that's $3,000–$5,000 extra monthly revenue for a mid-size online store.

Markup types by scenario:

  • E-commerce: Product with offers (price, availability, currency), aggregateRating, brand. BreadcrumbList, ItemList.
  • Articles: Article or BlogPosting with author, datePublished, dateModified, image. Organization and WebSite.
  • Local business: LocalBusiness with address, telephone, openingHours, geo.
  • FAQ: FAQPage with mainEntity — questions appear as expandable block.

Validation: Google Rich Results Test, Schema Markup Validator. Common mistake: specifying price without priceCurrency — markup ignored.

How to conduct a technical SEO audit

Crawlability. robots.txt blocks necessary pages or doesn't block service pages. Canonical URLs incorrectly set — duplicates with UTM parameters. Sitemap contains noindex pages. Tools like Screaming Frog or Sitebulb show this in an hour.

Core Web Vitals at scale. Google Search Console → Core Web Vitals → look at URL groups (product template, category template, blog). Problem is usually systemic.

JavaScript SEO. Google renders JS with delay. For critical content, SSR or SSG are mandatory. Check via Search Console → Inspect URL → View Crawled Page.

Internal linking. Orphan pages lose PageRank. Broken links (404) are a quality signal.

Common mistakes when implementing Schema.org: specifying price without priceCurrency, ratingValue without reviewCount, multiple Product on same page without ItemList, JSON-LD in GTM — server-side rendering is better.

What does the optimization process look like?

Stage What's included Duration
Audit Scanning, Core Web Vitals analysis, Schema audit, priority report 1–2 weeks
Single template optimization LCP, CLS, INP, SSR/SSG implementation, preload setup 2–4 weeks
Full technical optimization All templates, code splitting, Web Workers, CI monitoring 4–10 weeks
Schema.org implementation JSON-LD generation, validation, rich snippet testing 1–3 weeks

What deliverables do you receive?

  • Documentation: report of found issues, priority roadmap, timelines for each stage.
  • Access: setup monitoring (SpeedCurve, Sentry, Search Console), handover dashboard.
  • Training: one or two calls reviewing typical mistakes for your team.
  • Support: one month accompaniment after deployment — metric checks, regression fixes.

How many positions can you regain through technical SEO?

We have 5+ years on the market and 150+ projects completed. For a case study: a SaaS platform with 200k monthly visits had LCP 6.2s, CLS 0.45, INP 600ms. After optimization, LCP dropped to 1.8s, CLS to 0.02, INP to 180ms. Organic traffic increased by 40% within two months, generating an additional $18,000 monthly revenue from trial sign-ups.

Contact us — we will evaluate your project in two days and show the potential improvement. Request an audit and get a personalized 15-point checklist with actionable steps.