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If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

A postal code API returns structured location data for a given ZIP Code or Canadian postal code. A typical response includes the code itself, city, state or province, county, latitude/longitude, and — where available — ZIP+4 detail. More complete services add time zone, area codes, boundary geometry, and demographic fields. You send a code (or an address), and the API sends back a machine-readable record you can store, validate, or display.

This article explains what those responses contain, how requests are usually shaped, and where postal code data fits into real applications.

First, clear up the word "code"

The keyword "code" is overloaded, and that causes real confusion for developers:

• Postal code — the ZIP Code (U.S.) or postal code (Canada) that identifies a delivery area.
• API key — the credential you use to authenticate your requests. It is not postal data.
• Source code — the program you write to call the API.

When someone searches for "postal code API code," they usually want example request/response code for a postal code service. The rest of this article treats it that way.

What a postal code API actually returns

Response fields vary by provider and endpoint, but the core set is fairly consistent. A single-code lookup commonly returns:

Richer datasets add 90+ fields: boundaries, population, income, elevation, and more. You rarely need all of them — request only what your application uses.

A representative JSON response

{
  "postal_code": "90210",
  "city": "Beverly Hills",
  "state": "CA",
  "county": "Los Angeles",
  "latitude": 34.0901,
  "longitude": -118.4065,
  "timezone": "America/Los_Angeles",
  "area_codes": ["310", "424"]
}

XML responses carry the same information in tag form. Choose based on what your stack parses most easily; JSON is the common default.

Common request patterns

Most postal code APIs support four patterns. Knowing which one you need prevents wasted calls.

1. Lookup by code

You have a code and want its details. This is the simplest and fastest call.

GET /lookup?code=90210

2. Reverse lookup by address

You have a street address and want to confirm or complete the code. This is the pattern behind checkout address validation.

GET /validate?street=...&city=...&state=...

3. Radius search

You have a center point and want all codes within a distance. Useful for store locators and delivery zones.

GET /radius?code=90210&miles=10

4. Batch validation

You have a file of addresses and want them cleaned in bulk. Batch endpoints trade latency for throughput and usually have their own limits.

Handling missing and ambiguous matches

Real data is messy. Plan for these cases:

• No match — the code doesn't exist or the address is malformed. Return a clear error rather than a silent empty object.
• Multiple matches — a city name may map to several codes, or a code may span several acceptable city names. Decide whether to pick the primary or return a list.
• Partial match — the street is valid but the ZIP+4 isn't. Fall back to the 5-digit code.
• Stale data — codes are added, retired, and reassigned. Refresh your dataset on a regular schedule.

A practical rule: validate at the point of entry, store the normalized result, and never re-derive it later from raw user input.

Practical use cases

• Checkout address validation — catch typos before shipping, reduce failed deliveries.
• Shipping zone lookup — map a code to a zone, carrier route, or rate table.
• Data enrichment — append county, coordinates, or demographics to existing records.
• Store and service locators — radius search to find nearby branches or coverage areas.
• Territory and tax logic — county and boundary data drive jurisdiction rules.

Licensing and data-source considerations

Postal code data originates with national authorities — USPS in the United States and Canada Post in Canada. Providers license and repackage it, which is why accuracy, update frequency, and field coverage differ between services. Before committing:

• Confirm the data source and how often it refreshes.
• Check whether ZIP+4 and boundary data are included or sold separately.
• Review usage limits and whether batch processing is allowed.
• Read the license terms for redistribution and storage.

Pricing and plan details change, so check the provider's current documentation rather than relying on secondhand figures.

Getting started

1. Decide which request pattern you need (lookup, reverse, radius, or batch).
2. Pick the fields you'll actually store.
3. Write a small test call and inspect the raw response.
4. Add error handling for no-match and ambiguous cases.
5. Cache results where the same codes repeat.

A postal code API is ultimately a translation layer: you give it a code or an address, and it gives back structured location facts. Understand the fields, match them to your use case, and handle the messy edges — that's most of the work.