What is the local pack?

The local pack is a SERP feature in Google Search: a box combining a map section with three local business listings that appears for searches with local intent, usually above the classic organic results. Each listing shows data from the company’s Google Business Profile: name, star rating, category, address, and opening hours. Because of its three slots, the element is also called the 3-pack or map pack.

When does Google show a local pack?

Google displays the local pack whenever a query carries local search intent. That happens in two cases:

• Explicitly local searches: The user names the place, for example “dentist munich” or “bakery near me”.
• Implicitly local searches: The user just types “car repair shop” or “hair salon”. Google recognizes from the type of query that a nearby business is meant and uses the device location.

The local pack appears on the regular results page (SERP) and pushes the classic blue links further down. For local service businesses, visibility is therefore often decided in the local pack, not on position 1 of the organic list.

Which factors decide local pack rankings?

In its help documentation, Google names three factors for local ranking: relevance, distance, and prominence.

• Relevance: How well the Google Business Profile matches the query. Complete details on services, categories, and opening hours help Google classify the business correctly.
• Distance: How far the location is from the searcher. You cannot optimize this factor, only support it with clear location data.
• Prominence: How present the business is online and offline. This is where Google reviews (count, star value, and recency), mentions in directories, and the strength of your website count.

How does your business get into the local pack?

The foundation is a fully maintained Google Business Profile. Four measures build on it:

• Complete the profile: Keep the right primary category, services, photos, opening hours, and posts up to date.
• Build reviews: Actively ask satisfied customers for a Google review and reply to every review.
• Consistent business data: Keep name, address, and phone number identical everywhere, from your website to directory listings.
• Local website content: A location or service page with genuine local relevance strengthens relevance and prominence at the same time.

These building blocks interlock and need continuous care. That is exactly the package our local SEO service bundles: from the business profile through review building to the local landing page.

Frequently asked questions about the local pack

Is the local pack advertising?
No. The three listings are organic results and cost nothing. Google may show additional local ads labeled “Sponsored” above the local pack; they are not part of the organic 3-pack.

What is the difference between the local pack and Google Maps?
The local pack is a section within Google Search showing exactly three results. Google Maps is the full map service with all listings. Clicking “More places” takes you from the local pack to the expanded map view, the local finder.

Does a business need a website to appear in the local pack?
No, a Google Business Profile is enough to be included. A linked, locally optimized website, however, raises relevance and prominence and with them the chance of winning one of the three slots.

What is an LLM?

An LLM is an AI model that understands and generates language; the abbreviation stands for Large Language Model. An LLM is trained on massive amounts of text and calculates the most likely continuation of an input, word by word. That is how it answers questions, summarizes texts, and translates. LLMs are the technology behind ChatGPT, Google Gemini, and Claude, and they write the answers of AI search.

How does an LLM work?

An LLM breaks text into tokens, small word and character units, and calculates from the text so far which token is most likely to follow. The model repeats this prediction until a complete answer has formed. The technical foundation is the transformer architecture, introduced by Google researchers in 2017: its attention mechanism weighs which parts of the input matter most for the next word.

A large language model acquires its knowledge in two phases:

1. Pretraining: The model processes billions of text pages from the web, from books, and from databases and learns the statistical patterns of language from them.
2. Fine-tuning: Human feedback trains the model to follow instructions and answer helpfully (Reinforcement Learning from Human Feedback).

The size of an LLM is measured in parameters, the adjustable weights of the neural network. Modern models reach hundreds of billions of parameters; that is exactly what the “large” in the name refers to.

Which LLMs exist?

The best-known LLM families come from five providers:

• GPT (OpenAI): The model series behind ChatGPT and ChatGPT search.
• Gemini (Google): Powers the Gemini app and writes the answers of AI Overviews and AI Mode in Google Search.
• Claude (Anthropic): Model family with a focus on long contexts and safe behavior.
• Llama (Meta): Models with open weights that companies can run themselves.
• Mistral (Mistral AI): European provider from France, also offering open models.

What do LLMs have to do with SEO and AI search?

LLMs write a growing share of all search answers. AI Overviews, AI Mode, ChatGPT search, and Perplexity answer queries directly, and in each of these systems a large language model writes the answer text. Your content reaches these answers in two ways:

1. Through training data: What an AI crawler collected during training, the model can later reproduce as knowledge.
2. Through live retrieval: AI search systems pull current web pages into the answer at runtime, a technique called Retrieval-Augmented Generation (RAG). Only this path produces source links and, with them, visitors.

Preparing content so that LLMs understand and cite it is a discipline of its own: Generative Engine Optimization (GEO), also called LLMO. Citable definitions, clear entities, and machine-readable facts are the core of our optimization for AI visibility.

What are the limits of an LLM?

An LLM does not know truth, only probabilities. Three limits follow from that:

• Hallucinations: The model writes fluently even when the facts are missing, inventing plausible-sounding claims along the way.
• Knowledge cutoff: An LLM’s knowledge ends at the cutoff date of its training data. Anything later, the model only knows through a connected search.
• Missing sources: On its own, an LLM cannot prove where a statement comes from. Only the connection to a search index delivers verifiable source links.

Exactly these limits make well-sourced web content valuable: AI search systems check their answers against current, trustworthy pages and cite the sources that state facts most clearly.

Frequently asked questions about LLMs

Is ChatGPT an LLM?
Not quite: ChatGPT is the product, the underlying LLM is a model from OpenAI’s GPT series. ChatGPT adds a chat interface, a web search, and further tools on top of the language model. What that means for your visibility is covered in our article on ChatGPT SEO.

What does the “large” in Large Language Model mean?
The “large” refers to the size of the model and its training data: modern LLMs have hundreds of billions of parameters and were trained on text collections spanning trillions of tokens.

What is the difference between an LLM and AI?
AI is the umbrella term for all systems that perform human-like cognitive tasks. An LLM is a specific type of AI that specializes in language. Image recognition or recommendation algorithms are AI, but not LLMs.

Can an LLM access my website?
Not directly. Content reaches an LLM in two ways: AI crawlers collect texts for training, and AI search systems fetch pages at runtime through a connected web search. You control both paths via robots.txt.

What is llms.txt?

llms.txt is a standardized Markdown file in the web root of a website (https://domain.com/llms.txt) that gives large language models and AI crawlers a curated view of a site’s most important content and its structure. The purpose of llms.txt is to let language models such as ChatGPT, Claude or Perplexity understand a website correctly and use its central pages reliably. The format was proposed in September 2024 by Jeremy Howard, co-founder of Answer.AI. That makes llms.txt the AI counterpart to the classic sitemap.

The file does not address human visitors, it addresses machines. At one single, predictable address it bundles which content of a brand matters most and how a language model should classify it. That makes llms.txt one of the technical building blocks of Generative Engine Optimization (GEO), the optimization for AI search systems.

The term at a glance

Which problem does llms.txt solve?

llms.txt solves the context problem of language models: they cannot process a website as completely as a human can. The reason lies in two technical limits. First, LLMs have a limited context window and cannot read an arbitrary number of pages at once. Second, typical HTML carries a lot of extra material that is irrelevant to the pure information: navigation, ads, scripts, cookie banners and footers.

An llms.txt clears that ballast away. It delivers a curated selection of the most important URLs in clean Markdown, each with one explanatory sentence. The language model does not have to guess which pages are central, it gets the priorities straight from the brand. That raises the chance that an AI reproduces a company’s positioning, services and facts correctly instead of reconstructing them from scattered signals.

For brands that is a direct lever of AI visibility. If you want answer engines to quote your content correctly, you work on large language model optimization (LLMO), and llms.txt is a concrete tool you can put in place quickly.

How is an llms.txt structured?

An llms.txt follows a fixed Markdown structure of four building blocks. The standard from llmstxt.org prescribes exactly one H1, followed by a short summary and any number of thematic sections with link lists.

1. H1 with the name: one single heading carrying the brand or project name. It is the only required element.
2. Blockquote summary: a short paragraph (often as a > quote) describing the brand, its positioning and key facts in one or two sentences.
3. Thematic sections: H2 headings such as “Core pages” or “Services”, followed by link lists in the format [Title](URL): description.
4. Optional section “Optional”: an H2 named Optional for links an AI may skip when context is tight.

A minimal example for taismo would look like this:

# taismo | SEO and GEO agency from Munich

> taismo works with companies in competitive markets that have
> services in need of explanation. Focus: structured data, GEO
> and WordPress development. Based in Munich.

## Core pages
- [SEO services](https://taismo.de/en/seo-services/): Monthly SEO retainer for measurable growth.
- [About us](https://taismo.de/en/about-us/): Team, values and method of the agency.

## SEO glossary (excerpt)
- [What is GEO?](https://taismo.de/en/what-is/generative-engine-optimization/): Definition and levers of AI visibility.

## Optional
- [Imprint](https://taismo.de/en/imprint/): Legal information.

Every entry consists of a linked title and one dense explanatory sentence. Those one-sentence descriptions are the real value: they tell the model not only where a page sits, but also why it is relevant.

llms.txt vs. robots.txt: what is the difference?

llms.txt and robots.txt pursue opposite goals: llms.txt invites AI systems in and curates content, robots.txt steers and restricts crawler access. Both files sit in the web root, but they complement each other instead of replacing each other.

• Purpose: the robots.txt tells crawlers which paths they should not fetch. The llms.txt tells language models which content matters most to them.
• Content: robots.txt carries technical directives (Disallow, Allow, User-agent). llms.txt carries curated links with explanatory prose in Markdown.
• Effect: robots.txt restricts, llms.txt prioritizes and explains.
• Binding force: robots.txt is an established standard that is widely followed. llms.txt is a young proposal and using it is voluntary.

The line against the sitemap matters just as much: a sitemap.xml lists all indexable URLs for search engines. The llms.txt deliberately shows only a subset of the most important pages and adds context for LLMs. If you want the detail, see the term sitemap.

How do you create an llms.txt?

An llms.txt is created in four steps and stored as a file in the web root. The process is deliberately lean, because the file should be curated by hand and not generated automatically.

1. Pick the most important pages: money pages, about us, case studies and a selection of strong guide or glossary pages. Do not copy the whole sitemap, curate it.
2. Write the Markdown: H1 with the brand name, a short summary as a blockquote, then thematic sections with link lists in the format [Title](URL): description.
3. Check every URL: only absolute links that really resolve with HTTP status 200. Redirects and blocked pages do not belong in there.
4. Store the file: as llms.txt in the web root, so that it is served under https://domain.com/llms.txt as text/plain.

The file should stay compact, two to seven kilobytes is a good rule of thumb. taismo runs an llms.txt of its own at taismo.de/llms.txt that follows exactly this structure. If you take machine-readable signals seriously, combine llms.txt with clean JSON-LD: structured data hands machines the facts in a fixed schema, llms.txt hands them the priorities in prose.

Do AI systems use llms.txt yet?

At the moment none of the large AI providers officially evaluates llms.txt across the board. Google has stated several times that Googlebot does not use llms.txt as a ranking or crawling signal. For AI Overviews as well, no evaluation of the file is confirmed.

The picture is still more nuanced than that. The format is already supported widely by tools and documentation platforms, and some AI-adjacent services do read the file when someone queries a page specifically. The effort is small and the risk is zero: an llms.txt does no harm to classic search and is quickly written. For brands betting early on AI visibility, it is a cheap bet on a standard that may establish itself.

To put it in perspective: llms.txt is no substitute for good content, sound technology and structured data, it is an addition. It is one building block of considered SEO services that think classic search engines and AI systems together.

Frequently asked questions about llms.txt

Is llms.txt the same as robots.txt?
No. robots.txt restricts crawler access with technical directives, llms.txt curates and explains the most important content for language models. Both sit in the web root but pursue opposite goals.

Does every website need an llms.txt?
No, it is voluntary. For brands with a lot of content that want to appear correctly in AI systems, the small effort still pays off, because the file prioritizes their own positioning in a machine-readable way.

Does llms.txt improve my Google ranking?
No. By Google’s own statement, llms.txt is not used as a ranking signal. The benefit lies in AI visibility, in language models classifying and reproducing content correctly.

Where does the llms.txt have to sit?
In the web root of the domain, reachable at https://domain.com/llms.txt. Only at that predictable address do crawlers find the file reliably.

What is LLMO?

LLMO (Large Language Model Optimization) is the optimization of content and entities so that large language models such as ChatGPT, Gemini or Perplexity understand them, reproduce them correctly and cite them as a source. LLMO makes sure a brand shows up in the answers of AI assistants instead of only in the classic list of blue links on Google. The approach is closely related to Generative Engine Optimization (GEO) and counts as an extension of classic SEO into AI search.

Search is moving out of the results list and into the chat window. Anyone who no longer types a question into Google but asks ChatGPT or Perplexity gets a finished answer instead of ten links. LLMO is the discipline that makes your own brand part of that generated answer, with a mention and, in the best case, with a source link.

LLMO at a glance

What does LLMO stand for?

LLMO stands for Large Language Model Optimization, the targeted optimization of content for large language models. A large language model (LLM) is an AI model trained on huge amounts of text and built to understand and produce language. Well-known examples are GPT (ChatGPT), Gemini and the models behind Perplexity or Copilot.

LLMO covers 3 levels of optimization:

• Content level: writing texts so that a language model can extract the core statements cleanly and reproduce them correctly in its own words.
• Entity level: establishing the brand, the people and the products as clearly defined entities, so the model knows what a company stands for.
• Technical level: marking content up for machines, for example through structured data, a clean HTML structure and an llms.txt file.

The goal is always the same: when someone asks an AI about a topic, the model should draw on your content as a trustworthy source and mention the brand by name.

LLMO vs. GEO vs. SEO: what is the difference?

LLMO, GEO and SEO chase the same overall goal, visibility, but they differ in the channel they optimize for. SEO targets the classic list of search results, GEO targets AI search systems with a web search, and LLMO in the narrow sense targets the language models themselves.

• SEO (search engine optimization): optimizes for ranking positions in the Google results list. Success is measured as the position for a keyword.
• GEO (Generative Engine Optimization): optimizes for generative search systems that build one answer out of several sources, for example Perplexity or Google AI Overviews. The term Generative Engine Optimization explains that in more detail.
• LLMO (Large Language Model Optimization): optimizes so that the language model itself knows a brand, describes it correctly and cites it, even without an active web search at the moment of the question.

In practice the three disciplines overlap heavily. Many people use LLMO and GEO synonymously, because the same measures work in both: clear definitions, verifiable facts, structured data and a strong entity. Good SEO stays the foundation, because a large share of AI systems still fall back on the Google index or their own web search for up-to-date answers.

How does LLMO work and how do LLMs read content?

LLMO works by fitting content to the way language models process text. An LLM does not read a page from top to bottom the way a person does. It breaks the page into sections of meaning (chunks), turns those into vectors and pulls the most fitting sections back up when a question comes in. That process is exactly where LLMO starts.

The path from text to an AI answer runs in 4 steps:

1. Retrieval: the model either knows the content from its training data or fetches it at runtime through a web search.
2. Chunking: the text is split into chunks. A section that answers a question completely and on its own can be picked up cleanly as one unit.
3. Scoring: the system weighs which chunks fit the question and which source they come from. Clear statements, verifiable facts and a credible sender get preference.
4. Answer: the model phrases an answer out of the most relevant chunks and, depending on the system, names the source.

For LLMO that means, in concrete terms: every paragraph should stand on its own and deliver its core statement in the first sentence. Definition patterns such as “X is …”, concrete figures and unambiguous entity names instead of pronouns raise the chance that a model picks up the passage correctly.

Which measures belong to LLMO?

LLMO covers content, technical and brand measures that together raise how citable a website is. The 6 most important levers:

1. Quotable definitions: phrase every core statement as a sentence that stands on its own, following the pattern “X is …”. Language models pick up passages like these most reliably.
2. Structured data: mark content up with structured data (JSON-LD) so machines can grasp entities, definitions and relationships without guessing.
3. Strengthen entities: name the brand, the authors and the products consistently and establish them as a clear entity through an about page, author profiles and external mentions.
4. Show E-E-A-T: make experience, expertise, authoritativeness and trust visible, for example through named authors, sources and data. How E-E-A-T works in detail is part of what decides which source a model treats as trustworthy.
5. Clear structure: headings as questions, short paragraphs, lists and tables. That shape splits into clean chunks.
6. Machine readability: an llms.txt file, clean internal linking and crawlable content make access easier for AI systems.

No single lever decides this on its own. LLMO works as an interplay: only once content, technology and brand mesh does a language model treat a source as worth citing.

Why is LLMO becoming more important?

LLMO is becoming more important because a growing share of search queries gets answered inside AI systems instead of in the classic Google list. When ChatGPT, Perplexity or an AI answer right inside search settles the question, visibility is left only for the brands that show up in that answer.

Three developments drive the relevance of LLMO:

• AI answers in search: with AI Overviews, Google places generated answers directly above the organic results. Getting cited there wins visibility without anyone scrolling.
• Chat instead of click: more and more people research straight in the chat and click single links less often. Being mentioned in the answer becomes the actual visibility goal.
• Trust through citation: a recommendation that comes out of an AI answer reads to many people like neutral information and builds trust in the brand it names.

For companies that means: classic SEO stays the basis, but on its own it is no longer enough. Invest in LLMO early and you secure a place in the answers that will help decide purchases tomorrow. That is exactly where forward-looking SEO services come in, thinking search and AI visibility together.

Frequently asked questions about LLMO

Is LLMO the same as GEO?
LLMO and GEO are closely related and are often used synonymously. GEO stresses the optimization for generative search systems with a web search, LLMO the optimization for the language models themselves. In practice the measures overlap almost completely.

Does LLMO replace classic SEO?
No, LLMO does not replace SEO, it adds to it. Many AI systems still fall back on the Google index or their own web search for up-to-date answers, which is why solid SEO remains the foundation of every LLMO strategy.

How do you measure the success of LLMO?
You measure the success of LLMO by whether and how a brand shows up in AI answers. To do that, check regularly how systems such as ChatGPT or Perplexity answer relevant questions, whether the brand is named and described correctly and whether a source link is set.

Does every company need LLMO?
LLMO pays off for every company whose audience increasingly researches through AI assistants. Wherever decision-makers expect information quickly and pre-filtered, being named in AI answers turns into a competitive advantage.

What is Largest Contentful Paint (LCP)?

Largest Contentful Paint (LCP) is a load time metric from Google’s Core Web Vitals. LCP measures how long it takes until the largest visible content element in the viewport of a page has finished loading, counted from the moment the page load begins. A good LCP value is 2.5 seconds or below, measured at the 75th percentile of all page views on mobile and desktop devices. LCP therefore shows how quickly a visitor gets the feeling that the page has loaded.

LCP is one of the three Core Web Vitals and the central metric for perceived loading speed. Google uses LCP as part of the page experience signal for ranking. A slow LCP means that the visitor stares at a half empty page for too long, is more likely to bounce and writes the site off as sluggish.

Largest Contentful Paint at a glance

What does Largest Contentful Paint measure?

LCP measures the point in time at which the largest content element inside the visible area of the browser window has fully rendered. While the page loads, the browser keeps watching which element currently takes up the most space in the viewport and reports its render time. The start of the page load counts as the starting point.

One distinction matters: LCP does not measure when the whole page has finished loading, it measures when the most important visible content appears. A visitor rates a page as “loaded” as soon as the main image or the heading is there, even if scripts are still coming in behind the scenes. That impression is exactly what LCP captures.

Google splits every LCP value into 4 subparts that add up to the total duration. This breakdown shows where a slow page actually loses time.

What is a good LCP value?

A good LCP value is 2.5 seconds or below. Google splits the measurements into 3 rating bands that apply to mobile and desktop devices alike:

• Good: an LCP of 0 to 2.5 seconds.
• Needs improvement: an LCP of 2.5 to 4.0 seconds.
• Poor: an LCP above 4.0 seconds.

What counts is the 75th percentile: Google does not rate the average, it rates the value that 75 percent of all page views reach or beat. Only once three out of four visitors see a page in 2.5 seconds or faster does the LCP count as good. This field data comes from the Chrome User Experience Report (CrUX), which collects real visits over the past 28 days.

You find these field values in Google Search Console in the “Core Web Vitals” report and in PageSpeed Insights. Lab tools such as Lighthouse also give you a simulated LCP value, which helps with debugging but does not feed into ranking.

Which element is the LCP element?

The LCP element is the largest content element that appears in the visible area during loading. The browser only takes certain element types into account. There are 4 candidates for the LCP element:

1. Images: an <img> element or an <image> inside an <svg>.
2. Videos: the poster frame of a <video> element.
3. Background images: an element with a background image loaded through the CSS url() function.
4. Text blocks: block level elements with text, for example a large heading or a paragraph.

In practice, the LCP element on most pages is the hero image at the top or the main heading. PageSpeed Insights and the Chrome DevTools point out which element it is on your page. Only once you know the LCP element can you optimize with purpose, because every second of LCP hangs on that one element.

The LCP element can change during loading. If the heading appears first and the larger hero image follows, the image becomes the final LCP element. The browser reports the last candidate as soon as the visitor interacts with the page or loading has finished.

How do you improve your LCP value?

You improve a poor LCP by delivering and rendering the LCP element faster. Because LCP is made up of 4 subparts, the strongest levers attack exactly those phases. The 6 most important measures:

1. Cut the server response time (TTFB): fast hosting, server side caching and a content delivery network (CDN) shorten the time to first byte.
2. Never delay the LCP image with lazy loading: if the hero image is lazy loaded, the download starts too late. Load it early instead.
3. Preload the LCP resource: give the decisive image priority with <link rel="preload"> and fetchpriority="high" so that the browser requests it right away.
4. Compress images and serve them in modern formats: modern image formats on the web such as WebP or AVIF plus responsive sizes (srcset) cut the load time noticeably.
5. Remove render blocking resources: inline the critical CSS and defer non-critical JavaScript (defer or async) so that the browser can paint the LCP element sooner.
6. Take the sting out of web fonts: host your fonts yourself, preload them and serve them with font-display: swap so that a text LCP does not wait for the font file.

The order is deliberate: on most websites, TTFB and image delivery deliver the biggest jump. Which measure works hardest in a specific case is shown by the subpart analysis from figure 1. A structured check of all levers comes with a technical SEO audit, which uncovers the LCP weak spots page by page.

How does LCP relate to the Core Web Vitals?

LCP is one of three Core Web Vitals that Google uses to measure the user experience of a page. Each of the three metrics covers a different aspect:

• LCP (Largest Contentful Paint): measures the perception of loading, so how quickly the main content becomes visible.
• INP (Interaction to Next Paint): measures responsiveness, so how quickly the page reacts to clicks and input. INP replaced the earlier metric First Input Delay (FID) in March 2024.
• CLS (Cumulative Layout Shift): measures visual stability, so how much elements move around while the page loads.

A page only passes the Core Web Vitals when all three values sit in the green range. LCP replaced the older metric First Meaningful Paint (FMP) in 2020, because FMP was too imprecise and hard to measure. Since the page experience update in 2021, the Core Web Vitals feed into Google ranking. Together, the three of them form the backbone of technical performance, which solid SEO services keep under review.

Frequently asked questions about LCP

Is LCP a ranking factor?
Yes, as part of the Core Web Vitals, LCP has been a confirmed ranking signal since 2021. The effect is moderate: LCP works above all as a tiebreaker between pages of equal content quality, good content stays the stronger factor.

Where do I see my LCP value?
You find your LCP in PageSpeed Insights, in the Core Web Vitals report of Google Search Console and in the Chrome DevTools. Search Console and PageSpeed Insights show data from real visitors, DevTools and Lighthouse give you lab measurements for debugging.

What is the difference between LCP and load time?
LCP only measures when the largest visible element appears, not when the whole page has finished loading. A page can still be loading in the background while the LCP has long been reached and the page is already usable.

Why does my LCP value keep changing?
LCP fluctuates because it comes from real visits with different devices, connections and locations. The CrUX report averages these values over 28 days, which is why optimizations only show up fully in the 75th percentile after a few weeks.

What is an LSI keyword?

An LSI keyword (LSI stands for latent semantic indexing) is a term or phrase closely connected to a main keyword or topic. LSI keywords are not simply synonyms or direct extensions of the main keyword, but words that appear frequently in the context of the main topic and are therefore semantically related. They help search engines such as Google understand the content of a web page better and judge how relevant it is for a given query. Working with LSI keywords helps you cover the topic of a page more completely and strengthens your search engine optimization, because it gives search engines more context.

Examples of LSI keywords

Say the main keyword is “apple“. In that case LSI keywords could be “fruit“, “Granny Smith“, “vitamins“, “healthy snacks” and “apple juice“. All of these terms stand in direct connection with the main topic “apple” and widen the picture of what the page is about. Another example: for the main keyword “online marketing”, LSI keywords could be “digital advertising”, “SEO”, “content marketing“, “social media strategies” and “Google Ads”. Words and phrases like these help search engines see how broad and how deep the content around the main topic goes.

Are LSI keywords important for SEO?

Yes, LSI keywords play an important part in search engine optimization. By supplying extra context and meaning around the main content of a page, LSI keywords can improve how visible a website is in search engine results pages (SERPs). They make it easier for search engines to pin down the relevance and the quality of the content, which can lead to better placements. Working with LSI keywords also lowers the risk of keyword stuffing, because it opens up a more natural and more rounded way of writing content. All in all, LSI keywords improve the user experience, because they make sure that content is more complete and more relevant to what people actually search for.

What is a long-tail keyword?

A long-tail keyword is a search term made up of several words that extends a main keyword with adjectives or attributes. The name comes from the picture of a keyword with a long tail hanging off it. Because a query with a long-tail keyword is more precise, the results are more specific than for a search with the main keyword alone.

Long-tail keywords are more specialized, so they trade some search volume for a better chance of success. The spelling longtail keyword is common as well and means the same thing.

What does a search with a long-tail keyword look like?

An example: you are looking for a bike for your child.

Your first query in the search engine is probably “kids bike”. Your child, however, wants a bike in blue, so the next query very likely looks like this:

“blue kids bike”

And with that you have searched with a long-tail keyword and you get different results than before.

Long-tail keywords work well in local SEO too.

Where are the opportunities in long-tail keywords?

Long-tail keywords often carry a lower search volume than the main keyword, but the positions on the SERP are less contested as well.

That gives you a realistic chance of overtaking the competition on some terms, because many website owners optimize their content for main keywords only and not for long-tail keywords. Long-tail keywords also fit certain niches well. Always check the search volume of a long-tail keyword first, though.

Picking the right terms for a whole site is the first step of every project in our SEO services.

What is link juice?

Link juice, also known as link equity, is a term from online marketing that describes the value and effectiveness of a link from one web page to another. That value is set by factors such as the authority of the linking page, how relevant the content of the two pages is to each other, and how fresh the link is. Link juice can be seen as a form of recommendation: a page that many other high authority pages link to receives a kind of seal of quality, which lifts its potential in search rankings. Other names for link juice are link power and link authority.

How can you measure link equity?

Measuring link equity directly is not possible, because search engines like Google do not disclose their exact algorithms. There are SEO tools, though, that offer their own metrics for estimating it. Those metrics are usually built on factors such as the number and quality of incoming backlinks, the authority of the linking domains, and the topical relevance between the linked pages. Tools like Moz’s Domain Authority, Ahrefs’ Domain Rating or Majestic’s Trust Flow can give you useful reference points here. It is important to understand, however, that these values are approximations and do not represent the actual link equity in the sense that search engines use it.

What effect does link juice have on SEO?

Link juice is an essential part of search engine optimization. Links with high link equity can improve the ranking of a web page in the search results considerably. They signal to search engines that a page is trustworthy, relevant and of high quality, and search engines then treat that page favorably in their results. Strong link equity can also help new content get indexed faster and rank better. What counts is that you use natural link building methods, because artificial or manipulative links can be recognized by search engines and counted against you.

Link equity does not only arrive from other websites. Every internal link passes part of the value of one page on to the next, which is why internal linking is the part of link equity you control completely. If you want to know how the equity of your own site is distributed and where it gets stuck, that is part of ongoing SEO support. More terms from this field are in the SEO glossary.

What is lazy loading?

Lazy loading, also called deferred loading, is an optimization technique for online content, above all for images and videos on web pages. The point of lazy loading is to improve the loading time of a page: resources that sit outside the current browser window are only loaded once the visitor scrolls them into view. Instead of loading every element of a page at the same time, only the objects that are visible to the visitor right now get loaded.

This cuts the amount of data loaded at one time, which shortens loading times and lowers bandwidth use.

How do I enable lazy loading in HTML?

To switch lazy loading on by hand in HTML, you use the loading attribute in your <img> and <iframe> tags. Here is a simple example of how lazy loading is implemented for an image:

<img src="/image-url.jpg" loading="lazy" alt="Description of the image">

In this example the loading="lazy" attribute is added to enable deferred loading of the image. The same works for videos and iframes:

<iframe src="/video-url" loading="lazy" width="560" height="315"></iframe>

By using the loading="lazy" attribute the developer tells the browser that the element should be loaded later. Worth knowing: every current browser supports this technique.

Why does lazy loading matter for SEO?

Lazy loading matters for search engine optimization for several reasons. One of the biggest benefits is the gain in page loading speed. Because search engines such as Google treat page speed as one of their ranking factors, lazy loading can directly help the position of your website in the search results.

A faster website also gives visitors a better experience, which is a critical factor for SEO in its own right. Deferring content that sits outside the visible area lets server resources be used more efficiently and lifts the overall performance of the page. Lazy loading also brings the bounce rate down, because visitors are less likely to leave a slow page early.

Whether lazy loading is set up correctly on your own pages, and where it still costs you loading time, is one of the points we look at in an SEO audit. More terms around speed and page experience are collected in the SEO glossary.

What is a landing page?

A landing page is a single page of a web presence that serves as the entry page for new visitors and potential new customers. A landing page is often part of an online marketing campaign and speaks to people interested in one specific product or service. In most cases, a click on an ad does not lead to the general website of the company but to an optimized landing page. The goal of a landing page is usually the generation of leads or conversions.

Which elements should a landing page contain?

As a rule, these elements work well on landing pages:

• the (company) logo
• product images and videos
• the USP of the product or the service
• call-to-action buttons
• testimonials

What types of landing page are there?

• Landing page as a subpage: in online shops, these can be product or category pages, for example.
• Landing page as a page of its own: a landing page can even sit on its own domain, independent of the rest of the site. Such a page is usually built in the corporate identity of the remaining website.

What is a landing page for in online marketing?

Landing pages are meant to make new users aware of products or services. The benefits should be visible at a glance, and visitors should be encouraged to interact. That could be ordering a product, filling in a contact form or signing up for an email list. In most cases, a paid ad comes before a landing page.

Which page a visitor arrives on decides how far that visitor moves through the sales funnel, so the page has to match the target audience of the campaign. On phones, responsive design decides whether it works at all. Landing pages that also carry organic traffic belong in your SEO strategy, not only in your ad account. More terms on the subject are waiting in the SEO glossary.