As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

According to Pi Datametrics, the leading search term in the e-learning category on Google Australia from January to March 2020 was 'elearning', with a search volume of *** thousand. The second and third most popular search terms in this category were 'coaching' and 'online courses' respectively.

According to Pi Datametrics, the leading search term in the things to do at home category on Google Australia from January to March 2020 was 'pancake recipe', with a search volume of *** thousand. The second and third most popular search terms in this category were 'banana bread recipe' and 'jigsaw puzzle' respectively.

Throughout 2020, Google was the most popular online search engine in the Dominican Republic with a market share of nearly 95.6 percent. It was followed by Microsoft's Bing with 3.13 percent of share during that period. Likewise, Google also topped the ranking in Brazil.

When you’re at work or at home, there’s a high chance that you’re going to use Google. You may be using Google to find a plumber for your leaky bathroom sink or see where the best sushi is in town. When you’re on Google, you’re looking for the top results which means you’re not scrolling past page one, unless, you’re desperate. So, getting your company on the first page of Google is extremely important and impactful for success. But, how do you get your business there? Well, here’s how.

Know the Basics

Before you do anything, you need to know the basics of how online marketing and Google search functions. By knowing the basics, you won’t waste time performing outdated tasks or being overcharged by an SEO Agency http://www.whitehatagency.com.au/seo-agency or marketing companies that recognize your lack of knowledge. Education is the key to success.

Use SEO

Search Engine Optimization is the method of attracting online attention and visibility through organic means. In essence, the unpaid search results - the paid search results typically have “sponsored” or “paid advertisement” written below them. But you can naturally drive traffic towards your site just by using the right keywords. Certain keywords will push your content, allowing it to be shown in the top results.

Meet the Google Standards

Google has standards which your website must fulfil prior to appearing as a #1 website in the search results. Google will flag any errors they deem needed fixed and you’re going to want to fix them, for example, broken links. If you don’t meet the standards, they’ll penalize all your pages until you fix them. So, take some time out of your day and make sure your website is fully functioning.

Content is everything

You may invest some top dollars in the look and appeal of your site but at the end of the day, it doesn’t really matter what your site looks like. What truly matters is the content as that will drive viewers to your site. The Google search results are designed to provide users with the most relevant material on the web. If your content isn’t providing value to the viewers, your material won’t make it to #1.

Focus on links

Links play an important role when it comes to Google’s ranking system. The way Google works is that it pays attention to the hyperlinks in content to figure out what keywords are tied to the link being used. Though this doesn’t mean your entire article should be made of links, if you use too many they’ll deem it as suspicious activity and your website can be taken from Google.

Google loves mobile-friendly

If you want to come up with a #1 site then you need to show Google that you’re updated and relevant to the current technology. In other words, you need to make your site mobile-friendly. Many users read material while on their way to work, on the bus or on their couch. So, if you’re not catering to smartphones, well, Google isn’t going to favor you.

Lebrau, C. (2020). 6 Ways to Get on the First Page of Google, HydroShare, http://www.hydroshare.org/resource/5b487a7dc6104628b10c2b6921b595e1

In the eyes of French SEOs, if there was one point that mattered absolutely in terms of SEO for mobile first indexing, it was the adaptation of the size of the content to the size of the screen in 2020. Other than that, when crawl was ensured, it made it easier for the crawler or the Internet user to visit and to facilitate the discovery of a site by search engines.

According to Pi Datametrics, the leading search term in the food and drink category on Google Australia from January to March 2020 was 'supermarket', with a search volume of *** million. The second and third most popular search terms in this category were 'grocery store' and 'wine' respectively.

According to Pi Datametrics, the leading search term in the communication category on Google Australia from January to March 2020 was 'iphone', with a search volume of *** thousand. The second and third most popular search terms in this category were 'iphone xr' and 'samsung s10' respectively.

According to Pi Datametrics, the leading search term in the home and garden category on Google Australia from January to March 2020 was 'wallpaper', with a search volume of over ***********. The second and third most popular search terms in this category were 'desk' and 'office chair' respectively.

According to Pi Datametrics, the leading search term in the online gambling category on Google Australia from January to March 2020 was 'online games', with a search volume of *** thousand. The second and third most popular search terms in this category were 'online lotto' and 'online casino' respectively.

According to Cognitive Market Research, the global Website Builder market size will be USD 3951.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1580.6 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.8% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1185.4 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 908.8 million in 2024 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 197.58 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.0% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 79.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2024 to 2031.
The PC Website Builders category is the fastest-growing segment of the Website Builder industry

Market Dynamics of Website Builder Market

Key Drivers for Website Builder Market

Rising Demand for Online Presence to Boost Market Growth: Small and medium-sized enterprises (SMEs) and entrepreneurs are increasingly recognizing the need for a digital presence to expand their reach, boost credibility, and drive sales. According to Curate Labs, by 2024, approximately 2 billion websites exist online, including 1.13 billion on the World Wide Web. Each day, around 252,000 new websites are created, with about 10,500 launched every hour. Globally, over 28% of businesses engage in online activities, and as of 2023, 71% of businesses have a website. Additionally, 43% of small businesses plan to enhance their website's performance, reflecting the growing importance of digital engagement. GoDaddy's Data Observatory India 2023 reveals that 55% of small businesses in India were established in the last five years, and 62% of them use websites, e-commerce platforms, or online stores as their primary sales channels. Website builders offer these businesses affordable, easy-to-use solutions for creating professional websites without requiring technical skills. This demand is expected to grow as more businesses, especially in developing regions, adopt digital transformation strategies

Increasing Mobile Internet Usage to Drive Market Growth: As more consumers access the internet through mobile devices, the demand for mobile-responsive websites continues to rise. In 2020, 90% of people in high-income countries were internet users, which increased to 93% by 2023, nearing universal access. In contrast, only 27% of the population in low-income countries uses the internet, up from 24% in 2022. This 66-percentage-point gap highlights the stark digital divide between high-income and low-income regions. Despite this, internet usage in low-income countries has grown by 44.1% since 2020, with a 14.3% increase in the past year alone. Website builders have adapted by offering mobile-first templates and optimization tools, ensuring that websites perform seamlessly across devices—an essential feature for attracting a diverse and growing user base.

Key Restraint Factor for the Website Builder Market

Limited Customization and Scalability Will Limit Market Growth: Many website builders offer pre-designed templates that limit the customization options for users. Businesses that need highly tailored or unique website designs might find the available options insufficient. This limitation could push users toward hiring professional web developers or using more customizable platforms like WordPress or custom-built sites. Some website builders offer basic SEO tools, but they may lack advanced options for optimizing websites for search engines. Users looking to perform in-depth on-page SEO (such as schema markup, custom metadata, or advanced page load speed optimizations) might find the limitations frustrating, especially for websites where search engine ranking is critical for traffic generation. Most website builders rely on shared hosting, meaning multiple websites are hosted on the same server. This increases the risk of vulnerabilities or breaches affecting multiple websites. B...

According to Pi Datametrics, the leading search term in the health and household category on Google Australia from January to March 2020 was 'toilet paper', with a search volume of *** million. The second and third most popular search terms in this category were 'thermometers' and 'anti bacterial wipes' respectively.

This dataset contains citation-based impact indicators (a.k.a, "measures") for ~187,8M distinct PIDs (persistent identifiers) that correspond to research products (scientific publications, datasets, etc). In particular, for each PID, we have calculated the following indicators (organized in categories based on the semantics of the impact aspect that they better capture):

Influence indicators (i.e., indicators of the "total" impact of each research product; how established it is in general)

Citation Count: The total number of citations of the product, the most well-known influence indicator.

PageRank score: An influence indicator based on the PageRank [1], a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score).

Popularity indicators (i.e., indicators of the "current" impact of each research product; how popular the product is currently)

RAM score: A popularity indicator based on the RAM [2] method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).

AttRank score: A popularity indicator based on the AttRank [3] method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.

Impulse indicators (i.e., indicators of the initial momentum that the research product received right after its publication)

Incubation Citation Count (3-year CC): This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.

More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found here and in the respective references (e.g., in [5]).

From version 5.1 onward, the impact indicators are calculated in two levels:

• The PID level (assuming that each PID corresponds to a distinct research product).
• The OpenAIRE-id level (leveraging PID synonyms based on OpenAIRE's deduplication algorithm [4] - each distinct article has its own OpenAIRE id).

Previous versions of the dataset only provided the scores at the PID level.

From version 12 onward, two types of PIDs are included in the dataset: DOIs and PMIDs (before that version, only DOIs were included).

Also, from version 7 onward, for each product in our files we also offer an impact class, which informs the user about the percentile into which the product score belongs compared to the impact scores of the rest products in the database. The impact classes are: C1 (in top 0.01%), C2 (in top 0.1%), C3 (in top 1%), C4 (in top 10%), and C5 (in bottom 90%).

Finally, before version 10, the calculation of the impact scores (and classes) was based on a citation network having one node for each product with a distinct PID that we could find in our input data sources. However, from version 10 onward, the nodes are deduplicated using the most recent version of the OpenAIRE article deduplication algorithm. This enabled a correction of the scores (more specifically, we avoid counting citation links multiple times when they are made by multiple versions of the same product). As a result, each node in the citation network we build is a deduplicated product having a distinct OpenAIRE id. We still report the scores at PID level (i.e., we assign a score to each of the versions/instances of the product), however these PID-level scores are just the scores of the respective deduplicated nodes propagated accordingly (i.e., all version of the same deduplicated product will receive the same scores). We have removed a small number of instances (having a PID) that were assigned (by error) to multiple deduplicated records in the OpenAIRE Graph.

For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided) where each line follows the format "identifier

From version 9 onward, we also provide topic-specific impact classes for PID-identified products. In particular, we associated those products with 2nd level concepts from OpenAlex; we chose to keep only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3. Then, for each product and impact measure, we compute its class within its respective concepts. We provide finally the "topic_based_impact_classes.txt" file where each line follows the format "identifier

The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v7.1.0, including data from (a) OpenCitations' COCI & POCI dataset, (b) MAG [6,7], and (c) Crossref. The union of all distinct citations that could be found in these sources have been considered. In addition, versions later than v.10 leverage the filtering rules described here to remove from the dataset PIDs with problematic metadata.

References:

[1] R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.

[2] Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380

[3] I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020)

[4] P. Manghi, C. Atzori, M. De Bonis, A. Bardi, Entity deduplication in big data graphs for scholarly communication, Data Technologies and Applications (2020).

[5] I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 (early access)

[6] Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839

[7] K. Wang et al., "A Review of Microsoft Academic Services for Science of Science Studies", Frontiers in Big Data, 2019, doi: 10.3389/fdata.2019.00045

Find our Academic Search Engine built on top of these data here. Further note, that we also provide all calculated scores through BIP! Finder's API.

Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.

More details about BIP! DB can be found in our relevant peer-reviewed publication:

Thanasis Vergoulis, Ilias Kanellos, Claudio Atzori, Andrea Mannocci, Serafeim Chatzopoulos, Sandro La Bruzzo, Natalia Manola, Paolo Manghi: BIP! DB: A Dataset of Impact Measures for Scientific Publications. WWW (Companion Volume) 2021: 456-460

We kindly request that any published research that makes use of BIP! DB cite the above article.

Deep Learning Hard (DL-HARD) is an annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) questions extensively annotated with query intent categories, answer types, wikified entities, topic categories, and result type metadata from a leading web search engine. DL-HARD contains 50 queries from the official 2019/2020 evaluation benchmark, half of which are newly and independently assessed. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex queries.

This dataset contains impact measures (metrics/indicators) for ~119Μ scientific articles. In particular, for each article we have calculated the following measures:

Citation count: The total number of citations, reflecting the "influence" (i.e., the total impact) of an article.

Incubation Citation Count (3-year CC): This

is a time-restricted version of the citation count, where the time window length is fixed for all papers and the time window depends on the publication date of the paper, i.e., only citations 3 years after each paper’s publication are counted. This measure can be seen as an indicator of a paper's "impulse", i.e., its initial momentum directly after its publication.

PageRank score: This is a citation-based measure reflecting the "influence" (i.e., the total impact) of an article. It is based on the PageRank1

network analysis method. In the context of citation networks, PageRank estimates the importance of each article based on its centrality in the whole network.

RAM score: This is a citation-based measure reflecting the "popularity" (i.e., the current impact) of an article. It is based on the RAM2

method and is essentially a citation count where recent citations are considered as more important. This type of “time awareness” alleviates problems of methods like PageRank, which are biased against recently published articles (new articles need time to receive a “sufficient” number of citations). Hence, RAM is more suitable to capture the current “hype” of an article.

AttRank score: This is a citation network 

analysis-based measure reflecting the "popularity" (i.e., the current impact) of an article. It is based on the AttRank3 method. AttRank alleviates PageRank’s bias against recently published papers by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher’s preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current “hype” of an article.

We provide five compressed CSV files (one for each measure/score provided) where each line follows the format “DOI score”. The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study4 and the configuration of AttRank in the original paper.3

The data used to produce the citation network on which we calculated the provided measures have been gathered from (a) the OpenCitations’ COCI dataset (Sep-2021 version), (b) a MAG5,6 snapshot from Jul-2021, and (c) a Crossref snapshot from Jan-2021. The union of all distinct DOI-to-DOI citations that could be found in these sources have been considered (entries without a DOI were omitted). Note: This is the 6th release of this dataset. You can find the previous releases here: https://doi.org/10.5281/zenodo.4386934

References:

R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank 

Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.

Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina 

Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380

I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. 

Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020)

I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. 

Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 (early access)

Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June 

(Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839

K. Wang et al., “A Review of Microsoft Academic Services for Science

of Science Studies”, Frontiers in Big Data, 2019, doi: 10.3389/fdata.2019.00045

Find our Academic Search Engine built on top of these data here. Further note, that we also provide all calculated scores through BIP! Finder’s API.

Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

More details about BIP! DB can be found in our pre-print:

T. Vergoulis, I. Kanellos, C. Atzori, A. Mannocci, S. Chatzopoulos, S. La Bruzzo, N. Manola, P. Manghi: BIP! DB: A Dataset of Impact Measures for Scientific Publications. arXiv 2021, 2101.12001

We kindly request that any published research that makes use of BIP! DB cite the above article:

  Thanasis Vergoulis, Ilias Kanellos, Claudio Atzori,

Andrea Mannocci, Serafeim Chatzopoulos, Sandro La Bruzzo, Natalia Manola, Paolo Manghi. "BIP! DB: A Dataset of Impact Measures for Scientific Publications". arXiv:2101.12001

According to Pi Datametrics, in June 2020 the leading food and drinks websites by share of voice on Google Australia was wikipedia.org, with **** percent share of voice. The second and third most popular websites in this category were danmurphys.com.au and wineaustralia.com.

According to Pi Datametrics, in 2020 the leading e-learning websites by share of voice on Google Australia was skillsyouneed.com., with **** percent share of voice. The second and third most popular websites in this category were wikipedia.org and hbr.org.

According to Pi Datametrics, the leading search term in the insurance category on Google Australia from January to March 2020 was 'travel insurance', with a search volume of *** thousand. The second and third most popular search terms in this category were 'health insurance' and 'compare health insurance' respectively.

According to Pi Datametrics, in 2020 the leading things to do at home websites by share of voice on Google Australia was taste.com.au, with **** percent share of voice. The second and third most popular websites in this category were officeworks.com.au and bbsgoodfood.com.

According to Pi Datametrics, from January to March 2020 the leading health and household website by share of voice on Google Australia was whogivesacrap.org, with **** percent share of voice. The second and third most popular websites in this category were chemistwarehouse.com and officeworks.com.au.