hallofshame.design

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.

Dark Patterns Hall of Shame is a case library that documents real products using manipulative interface design. Its entries pair a named company with the specific dark pattern involved, a short write-up, and a publication date. The cases below are the ones the site itself surfaces, so they double as a working list of documented examples and a sense of how current the coverage is.

Documented cases on the site

What the case mix tells you

The pattern names repeat more than the industries do. Preselection appears across social platforms, design tools, fintech apps, and email marketing, which makes it the most common single pattern in this set. Roach Motel and Obstruction cluster around subscriptions and cancellations, where the cost of friction falls on the user trying to leave. Nagging shows up specifically in mobile app prompts.

If you are using the site to check whether a product you rely on has been flagged, search by company name first, then read the pattern label. The label is what lets you compare one case to another; the write-up is what tells you whether the situation matches your own experience.

How current the coverage is

The entries above run from July 2024 to November 2025, with new cases appearing at a rough pace of one every few weeks. That cadence matters for two reasons: a company's absence from the list does not mean it has clean practices, and a company's presence reflects the state of its design at the time of publication, not necessarily today. Check the date on a case before treating it as a description of a product's current behavior.

Reading a case before you act on it

Each entry is short, often one to three minutes of reading. The useful sequence is: identify the pattern name, read what specifically triggered it, then decide whether the fix described (if any) applies to your account. For opt-out cases like Figma or X, the write-up is the part that tells you a setting exists at all. For cancellation cases like Audible or LinkedIn Premium, it tells you what the flow will put in your way before you start.

Dark Patterns Hall of Shame is a documentation site that catalogs real examples of manipulative interface design, organized by pattern type and company. You can use it defensively: before you sign up, subscribe, or accept a cookie banner, the site's case library shows you what the trick looks like in practice and what the exit path costs. The protection method it supports is not a single checklist but a habit — slow down at the exact moments where interfaces are designed to speed you up.

What the site actually documents

Each entry pairs a named dark pattern with a specific company and a short write-up. The pattern labels recur across cases, which is what makes the site useful as a reference rather than just a complaint log.

The three moments where protection actually happens

Before you submit a form

Preselection is the most common pattern in the site's catalog, and it's the cheapest to defeat. Scan every checkbox, toggle, and pre-filled field before hitting submit. The Groww case is the clearest illustration: a "pre-approved" loan offer with a "Get started" button, and below it a pre-checked box granting permission to pull your credit score. The button is the decoy; the checkbox is the decision.

The same applies to AI training opt-ins. The Figma entry describes AI features rolled out in early 2025 with all users automatically opted in to have their design files used for training. The LinkedIn entry covers the same mechanism for post content feeding AI models. In both cases the default was "in," and the only protection was finding the setting and reversing it.

When you accept a cookie banner

The HRAcuity case is a cookie wall: a video is inaccessible until you accept cookies, with no equivalent path to decline and still view the content. The practical response is to decide whether you actually need that specific piece of content, and if you do, to treat the acceptance as a one-time, site-specific concession rather than a blanket habit. Accepting on every site you visit is how a single wall becomes a default posture.

When you try to leave

Roach Motel entries are the most expensive to escape because the cost is your time. The Audible cancellation write-up describes a process built from several classic patterns stacked together to complicate the exit. LinkedIn Premium cancellation gets a four-minute read for the same reason. The defensive move is to plan the exit before you enter: check how cancellation works while you're still in the trial or first billing period, not when you've decided you're done.

Why the site is worth revisiting

Dark patterns change as companies change. The catalog spans entries from mid-2024 through late 2025, and the pattern mix shifts — AI training consent appears in the newer entries in a way it didn't earlier. Reading a handful of entries in the pattern category you're most exposed to (subscriptions, if you cancel things often; signups, if you trial a lot of tools) gives you the current shape of the trick rather than a stale list.

The site's own framing is that staying informed is the protection. That's accurate but incomplete: information only helps if it arrives before the click. The realistic use is to treat the catalog as a preview — read the Roach Motel entries before you start a subscription you might want to end, and read the Preselection entries before you fill in a form you'll submit in a hurry.

Dark Patterns Hall of Shame (hallofshame.design) is a website that documents real-world examples of deceptive and manipulative user interface designs — commonly called "dark patterns" — so that ordinary users and designers can recognize them and avoid being manipulated online. It works as a running catalog of case articles, each naming a specific company and the pattern it used, from pre-checked consent boxes to deliberately difficult cancellation flows.

What the site covers

Each entry on the site is a short case write-up. The homepage presents them as a feed of posts, each tagged with the type of dark pattern involved. The patterns named in the site's own headings and excerpts include:

• Preselection — options that are already checked or enabled by default, so users opt in without noticing.
• Privacy Zuckering — designs that push users into sharing more personal data than they intended.
• Trick Questions — wording or layout that makes a form answer mean the opposite of what the user expects.
• Roach Motel — a service that is easy to sign up for but hard to leave.
• Obstruction — deliberately adding friction to a task the company doesn't want you to complete.
• Misdirection — drawing attention away from the choice that matters.
• Cookie Wall — blocking access to content until cookies are accepted.
• Nagging — repeated prompts to do something the user has already declined.
• Fake Scarcity — manufactured urgency or limited availability.

Examples documented on the site

The case list shows how the site applies these labels to named products:

Who it's for

The site is written for two overlapping audiences. For everyday users, it functions as a warning list: if a company you use appears here, the entry tells you what to watch for and, in several cases, how to opt out or cancel. For designers and product people, it works as a reference of patterns to avoid, with each case showing how a specific design choice crosses the line from persuasion into manipulation.

How to use it

1. Open hallofshame.design and scan the feed for a company or product you use.
2. Read the case to identify which pattern is involved and what the deceptive mechanic is.
3. Where the entry covers a setting or subscription (for example, AI training opt-outs or a cancellation flow), follow the described steps to change your own configuration.
4. Use the pattern names — preselection, roach motel, nagging, and so on — as search terms when you want to check whether a new service uses the same tactic.

The site's stated purpose is to help people protect their online privacy and rights by staying informed about these designs. It is a documentation and awareness resource rather than a tool that blocks or fixes dark patterns for you.

Dark patterns are deceptive or manipulative user-interface designs that push people into choices they would not otherwise make—such as subscribing, sharing data, or staying in a service they want to leave. The Dark Patterns Hall of Shame is a case library that documents these designs in real products, tagging each entry with a pattern type and explaining how the manipulation works. It is useful if you want to recognize these tactics in your own accounts or cite concrete examples; it is not a legal authority or a complete taxonomy of every pattern in academic research.

How the site organizes its cases

Each entry pairs a named pattern with a specific company and a short write-up. The pattern label tells you the mechanism; the case tells you how it appeared in practice. From the site's own coverage, the recurring types include:

What the individual cases show

Preselection appears most often in the library. The pattern works because the default is treated as consent: a pre-ticked box, an auto-enabled setting, or an opt-out buried after the fact. Figma's 2025 AI rollout is described as automatically opting all users in to have their design files used for AI training—so the "choice" existed but the default did the deciding.

Obstruction and Roach Motel cover the exit side. The Audible cancellation write-up notes the flow follows classic patterns "designed to complicate the process," and the LinkedIn Premium piece frames cancellation as a case where a design promising ease serves a different purpose. The practical takeaway: when leaving is harder than joining, expect extra steps, retention offers, or hidden paths.

Nagging is the mildest but most frequent. Reddit's repeated app-download prompts and TikTok's repeated notification requests are both framed as pushing past a clear decline.

Cookie Wall and Trick Questions sit closer to consent and comprehension. A cookie wall trades access for consent; a trick question makes the "right" answer ambiguous through wording or layout.

How to use it

1. Identify the mechanism first. Before reading the case, ask what the interface is doing to your default behavior—pre-ticking, hiding, repeating, or blocking.
2. Check the pattern tag. The site's labels (Preselection, Nagging, Obstruction, etc.) give you vocabulary to describe what you're seeing in other products.
3. Read the case for the specific flow. The value is in the concrete steps—where the opt-out lives, what the cancellation path looks like—not the label alone.
4. Verify before acting on a claim. Entries are dated and some reference external events (like the StubHub lawsuit). For anything involving your account or a legal matter, confirm against the company's current settings or official sources.

Limits to keep in mind

The library is a curated collection of examples, not an exhaustive or peer-reviewed classification. Coverage is weighted toward well-known tech products and English-language services, and entries reflect the state of a product at the time of writing—interfaces change. Treat it as a starting point for recognizing manipulation, and check current behavior yourself before drawing conclusions about any specific service.