They are usually complex mechanics used to retrieve information from its search index and present the information to a given query algorithm sifting through billions of pieces of content in the Google index, looking for phrases and keywords that match the query.
How does the search work?
- CRAWLING:- The search engine algorithm directs web crawlers to discover internet URLs and examine their content. A crawler is a program that runs through content and automatically indexes it. We can say it is a web crawler or web crawling,
- INDEXING:- URL content is tagged with attributes and metadata that help the search engine categorize the content or we also say it as web indexing.
- SEARCHING AND RANKING:- The user enters a query and the search engine ranks and returns content in relation to the query.
MAJOR SEARCH ALGORITHMS ARE:
GOOGLE HUMMINGBIRD:-
It was announced in September 2013.
It considers the context of the different words together, with the goal that pages matching the meaning do better, rather than pages matching just a few words. The name is derived from the speed and accuracy of the hummingbird.
It places greater emphasis on natural language queries, considering text, and page content, making search results more relevant and meaningful over individual keywords.
It looks deeper at content on individual pages of a website with an improved ability to lead users directly to the most appropriate page rather than just a website’s home page.
It is aimed at making interactions more human, in the sense that the search engine is capable of understanding the concepts and relationships between keywords.
GOOGLE BERT:-
Bidirectional encoder representation from transformers [BERT] is a family of language models introduced in 2018.
It is based on the transformer architecture especially Bert is composed of transform encoder layers - a deep learning model in which every output element is connected to every input element and the weightings between them are dynamically calculated based on their connection.
It is an open-source machine-learning framework for natural language processing.
It is designed to help computers understand the meaning of ambiguous language in the text by using surrounding text to establish context.
GOOGLE PENGUIN:-
It was first announced on 24 April 2012.
This update was aimed at decreasing the search engine ranking of websites that violate Google’s webmaster guidelines by using grey HAT SEM technique involved in increasing artificially the ranking of a webpage by manipulating the number of links pointing to the page.
The principle for the update was to penalize websites that were using manipulating techniques to achieve high ranking.
Pre-penguin sites commonly used negative link-building techniques to rank highly and get traffic.
The main purpose is to catch excessive spammers that use spamdexing.
It meant that the content's main key and those with great content would be recognized and those with little or spammy content would be penalized and receive no ranking benefit.
GOOGLE PANDA:-
It was first released in February 2011.
This mainly focuses on prioritizing high-quality content, very low-quality or spam content. Many websites publish unique through and useful content to boost their SERP ranking.
The name ‘PANDA’ comes from Google engineer Navneet Panda.
It affects the ranking of an entire site or a specific section, rather than just the individual pages on a site Panda patent states that it creates a ratio between inbound links, reference queries, or search queries and that ratio is used to create a sitewide modification factor.
The sitewide modification factor is then used to create a modification factor for a page based upon a search query,if the page fails to meet a certain threshold [doorway], the modification factor is applied and therefore, the page would rank lower in the search engine results page.
GOOGLE RANKBRAIN:-
It’s a machine learning-based search engine algorithm.
It was used on 26 October 2015.
It helps Google to process search results and provide more relevant search results for users.
If Mark's brain sees a word or phrase it isn’t familiar with , the machine can make a guess as to what words or phrases might have a similar meaning and filter the results accordingly, making it more effective at handling never-before-seen search queries or keywords .
It attempts to guess what people mean and records the results, which adapts the result to provide better user satisfaction.
It has helped hummingbirds to provide more accurate results because it can learn words or phrases as well as language, in which a query is made.
On what factor Rankbrain depends?
- Natural language - natural tone easy to speak also
- Search intent - informational, commercial, transactional
- Relevance content - specific and focus keywords
- CTR [Click Through Rate] - high rate
- Quality over content - less but good content
- Dwell time - high dwell time
- Improve old content - old ones should be focussed
GOOGLE HILLTOP:-
It is an algorithm used to find documents relevant to a particular keyword topic in a news search.
It was created by Krishna Bharat and it was first used by Google in 2003 February.
When you enter a keyword or query into the Google News search engine, this algorithm helps to find relevant keywords whose results are more informative about the query or keyword.
Its main purpose was to identify authoritative web pages to rank by first identifying expert pages [expert pages were pages specific to a topic and that link out to many other sites ].
Instead of drawing from the entire web, it draws from the sites linked to a set of ‘expert’ web pages that are organized by topic.
What is CTR?
In online advertising, the CTR [click-through rate ] is the percentage of individuals viewing a web page who view and then click on a specific advertisement that appears on that page. CTR measures how successful an ad has been in capturing a user’s attention.
:- CTR = Total number of clicks .100 / Total number of impression
A good Google ads CTR is 6-7 %.
How to improve it?
- Optimize your headline and copy - one or two focus keywords.
- Include CTAs- call to action.
- Use images - compressed and relevant images.
- Try using hashtags - increases the chances of reaching the target audience.
What is Dwelling time?
Dwell time or Terminal dwell time - refers to the amount of time a user spends on a page from the search results before returning to the search engine results page.
It is a measurement of a user's dwelling time and how long they linger on a page starting from the time they click on search results and ending when they navigate away from the page.
How to improve it?
Nail the search intent - informational, commercial, transactional.
Produce longer and better content.
Prioritize user experience - load time, mobile-friendly pages.
Make your content engaging.
Improve user engagement with Semrush - [ tools for content ].
What is the Bounce rate?
- It is an internet marketing term used in web traffic analysis. It is defined as the percentage of visitors that leave a webpage without taking an action, such as clicking on a link, filling out a form, or making a purchase.
- The average bounce rate range is between 41-51%.
- The exit rate is similar to the bounce rate but the only difference is bounce rate is the percentage of people who land on a page and leave and the exit rate is the percentage of people who leave a specific page [ even if they didn’t initially land on that page ].
How to improve a Bounce rate?
- Embed youtube videos on your page - more time.
- Sprinkle in Bucket Brigades - thumbnails.
- Loading speed - fast hosting, compressed images.
- Use the PPT introduction template - promise, proof, and transition.
- Make your content easy to read - white space, subheaders, short paragraphs.
- Satisfy search intent.
- Turn donkeys into unicorns.
- Use heatmap data to improve key landing pages.
- Add internal links to your page - helps to shift from one site to another.
- Impress visitors with amazing design.
- Use a table of content [with jump links ].
- Optimise your mobile UX.
- Link related to post and articles.
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