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Website Builder Software Market Size 2024-2028

The website builder software market size is forecast to increase by USD 612.2 million at a CAGR of 5.1% between 2023 and 2028.

The market is experiencing significant growth due to the increasing importance of online branding for businesses. Functional websites have become essential for organizations to reach a wider audience and facilitate digital transformation. Cloud-based platforms and web development tools enable the creation of mobile-responsive designs, ensuring accessibility on various devices. AI applications integrated into website builders streamline various processes, such as big data analytics, media and entertainment, retail, and e-commerce. Website templates offer affordable website solutions, making it easier for businesses to establish an online presence. 
Moreover, the advancement of technology, including the AI revolution in website building, enhances the user experience. Open source options provide flexibility and customization opportunities. Website maintenance and security are crucial aspects, with cloud-based platforms offering reliable solutions to mitigate risks. In summary, the market is thriving, driven by the need for functional and secure online branding solutions.

What will be the Size of the Website Builder Software Market During the Forecast Period?

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Website builders have emerged as essential tools In the digital evolution, empowering businesses to create engaging websites and establish a strong online presence. These solutions facilitate digital adoption by individuals and organizations, enabling them to code and develop websites without extensive programming skills. In the context of the current business landscape, the integration of Artificial Intelligence (AI) infrastructure into website builders has become a significant trend. These applications include real-time data processing, NLP, video recognition, and parallel processing. By utilizing AI infrastructure, businesses can enhance their brand identity and optimize their online presence. Versatility and Sustainability: Website builders with AI capabilities offer versatility, allowing businesses to leverage advanced technologies without requiring specialized expertise. Moreover, the integration of AI chips and inference chips In these solutions ensures energy efficiency and reduced energy costs. Deep learning models and matrix multiplications are essential components of AI infrastructure. They enable website builders to provide advanced features such as personalized user experiences, predictive analytics, and automated content generation.
These capabilities can significantly improve user engagement and conversion rates. Cloud computing and parallel processing are essential technologies that support the integration of AI infrastructure into website builders. They facilitate efficient data-intensive computing and real-time processing, ensuring that businesses can quickly respond to market trends and customer demands. The integration of AI infrastructure into website builders has a profound impact on media, entertainment, retail, and e-commerce industries. For instance, media and entertainment companies can use AI to analyze user preferences and provide personalized content recommendations. Retailers can optimize their inventory management and offer personalized product recommendations based on user behavior. E-commerce platforms can leverage AI to enhance their search functionality and provide more accurate and relevant results. Website builders with AI infrastructure represent a significant advancement in digital evolution, enabling businesses to create engaging websites, optimize their online presence, and leverage advanced technologies without requiring specialized expertise. By integrating deep learning models, matrix multiplications, cloud computing, and parallel processing, these solutions offer versatility, sustainability, and significant improvements in user engagement and conversion rates. As businesses continue to adopt digital technologies, the role of AI-enabled website builders will become increasingly essential.

How is this Website Builder Software Industry segmented and which is the largest segment?

The website builder software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


End-user

  Commercial
  Individual


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period

Protein–ligand binding affinity reflects the equilibrium thermodynamics of the protein–ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure–kinetics relationship (QSKR) aim at predicting protein–ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein–ligand complexes with experimental dissociation rate constants (koff), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein–ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (koff) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein–ligand atom pair descriptors for predicting koff values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).

No Code Web Scraper Tool Market Outlook

As of 2023, the global market size for No Code Web Scraper Tools is valued at approximately USD 850 million and is projected to reach nearly USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5%. This growth is primarily driven by the increasing demand for simplified data extraction solutions that do not require extensive coding knowledge, enabling businesses of all sizes to efficiently collect and utilize web data.

The burgeoning need for data-driven decision-making across industries is a significant growth factor for the No Code Web Scraper Tool market. Organizations are increasingly recognizing the value of web data in gaining competitive insights, making informed business decisions, and optimizing operations. The ability to scrape web data without needing advanced technical skills democratizes data access, allowing non-technical users to harness the power of web data extraction. This trend is further propelled by the rise of small and medium enterprises (SMEs) that require cost-effective and efficient tools to stay competitive.

The growth of e-commerce and digital marketing also plays a pivotal role in the expansion of the No Code Web Scraper Tool market. As online retail continues to flourish, businesses are keen to monitor competitor pricing, track customer reviews, and gather market intelligence. No code web scraping tools provide an accessible and scalable solution for e-commerce platforms to automate these data collection processes. Furthermore, digital marketers utilize these tools to gather and analyze data from various online sources, supporting more targeted and effective marketing campaigns.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) in web scraping tools are additional drivers of market growth. These advancements enhance the capabilities of web scraping tools, making them more efficient, accurate, and user-friendly. AI and ML technologies facilitate the automatic adaptation to changes in website structures, reducing the need for manual intervention and ensuring continuous data extraction. This technological evolution not only improves the functionality of these tools but also broadens their applicability across different sectors.

In the realm of web scraping, a Hook Extractor is a crucial component that enhances the efficiency and accuracy of data extraction processes. This tool is designed to seamlessly integrate with existing web scraping frameworks, allowing users to capture specific data elements from complex web pages. By utilizing a Hook Extractor, businesses can streamline their data collection efforts, ensuring that they gather only the most relevant information for their needs. This is particularly beneficial in scenarios where web pages are dynamic and frequently updated, as the Hook Extractor can adapt to changes in the website structure without requiring manual reconfiguration. As a result, organizations can maintain a competitive edge by continuously accessing up-to-date data insights.

Regionally, North America holds a significant share of the No Code Web Scraper Tool market, driven by the high adoption rate of advanced technologies and the presence of numerous tech-savvy enterprises. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digitalization and increasing internet penetration. Europe also presents lucrative opportunities, supported by the growing awareness and adoption of data-driven strategies among businesses. Latin America, the Middle East, and Africa are gradually catching up, with increasing investments in IT infrastructure and digital transformation initiatives.

Component Analysis

The No Code Web Scraper Tool market by component is segmented into software and services. The software segment comprises the actual web scraping tools that businesses use to extract data from websites. These tools are designed to be user-friendly, often featuring drag-and-drop interfaces and pre-built templates that simplify the data extraction process. The software segment is expected to dominate the market, driven by continuous innovations and the development of more sophisticated, AI-powered scraping solutions. Additionally, the subscription-based pricing model for these tools makes them accessible to a wide range of users, from individual entrepreneurs to large enterprises.

In contrast,

Today, there is a proliferation of tools to meet the growing and evolving needs of professional designers. However, professional designers are not the only people participating in design. Novice designers are also increasingly taking part by creating flyers, websites, and presentations, but are forced to do so using either professional design tools or simplistic template-based tools. There exists an opportunity with these users to create design tools that cater to their specific abilities and tasks. In this dissertation, I describe some fundamental work I undertook to understand novices' ability to recognize visual appeal. I then discuss four different design tools I developed to assist novices in their creative tasks.

The first of these tools, Poirot, a web inspector tool, was built to help designers make changes to websites as they complete their work as novice end-user programmers. In an evaluation of Poirot compared to Chrome DevTools, Poirot led to higher task completion rates, faster task completion times, a lower perceived cognitive load, and was preferred by designers.

The three remaining tools are a series of user-steered, generative design tools, a new category of visual design tools that attempt to combine the natural abilities of novice designers with the power of computational design. The purpose of these tools is to allow novice designers to take greater part in the design process, experience an increase in their perceived creativity, and produce unique, high-quality designs all without increasing the burden of using the design tool. We formally evaluated the final of these design tools, DesignQ, a user-steered, generative design tool for creating flyers. Compared to Canva—a popular novice flyer design tool— novice designers felt better supported in their creative flyer design task and their perceived cognitive load was lower when using DesignQ, while there was no significant difference in the quality of the designs produced.

Mouse Brain MRI atlas (both in-vivo and ex-vivo) (repository relocated from the original webpage)

List of atlases

FVB_NCrl: Brain MRI atlas of the wild-type FVB_NCrl mouse strain (used as the background strain for the rTg4510 which is a tauopathy model mice express a repressible form of human tau containing the P301L mutation that has been linked with familial frontotemporal dementia.)

NeAt: Brain MRI atlas of the whld-type C57BL/6J mouse strain. Atlas was created based on the original MRM NeAt mouse brain atlas (template images reoriented and bias-corrected, left/right structure label seperated, and 4th ventricle manual segmentation added).

Tc1 Cerebellum: TC1 mouse cerebellar cortical sublayer lobules.This mouse cerebellar atlas can be used for mouse cerebellar morphometry.

Citation

If you use the segmented brain structure, or use the atlas along with the automatic mouse brain MRI segmentation tools, we ask you to kindly cite the following papers:

Ma D, Cardoso MJ, Modat M, Powell N, Wells J, Holmes H, Wiseman F, Tybulewicz V, Fisher E, Lythgoe MF, Ourselin S. Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion. PloS one. 2014 Jan 27;9(1):e86576. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086576

Ma D, Holmes HE, Cardoso MJ, Modat M, Harrison IF, Powell NM, O'Callaghan J, Ismail O, Johnson RA, O’Neill MJ, Collins EC, Mirza F. Beg, Karteek Popuri, Mark F. Lythgoe, and Sebastien Ourselin Study the longitudinal in vivo and cross-sectional ex vivo brain volume difference for disease progression and treatment effect on mouse model of tauopathy using automated MRI structural parcellation. Frontiers in Neuroscience. 2019;13:11. https://www.frontiersin.org/articles/10.3389/fnins.2019.00011

If you use the brain MR images of the FVB_NCrl mouse strain (the wildtype background of rTg4510), we ask you to kindly cite the following papers:

Wells JA, O'Callaghan JM, Holmes HE, Powell NM, Johnson RA, Siow B, Torrealdea F, Ismail O, Walker-Samuel S, Golay X, Rega M. In vivo imaging of tau pathology using multi-parametric quantitative MRI. Neuroimage. 2015 May 1;111:369-78. https://www.sciencedirect.com/science/article/pii/S105381191500124X

Holmes HE, Colgan N, Ismail O, Ma D, Powell NM, O'Callaghan JM, Harrison IF, Johnson RA, Murray TK, Ahmed Z, Heggenes M. Imaging the accumulation and suppression of tau pathology using multiparametric MRI. Neurobiology of aging. 2016 Mar 1;39:184-94. https://www.sciencedirect.com/science/article/pii/S0197458015006053

Holmes HE, Powell NM, Ma D, Ismail O, Harrison IF, Wells JA, Colgan N, O'Callaghan JM, Johnson RA, Murray TK, Ahmed Z. Comparison of in vivo and ex vivo MRI for the detection of structural abnormalities in a mouse model of tauopathy. Frontiers in neuroinformatics. 2017 Mar 31;11:20. https://www.frontiersin.org/articles/10.3389/fninf.2017.00020/full

If you're using the mouse MRI T2* Active Starining Cerebellar atlas, we ask you to please kindly cite the following papers:

Ma, D., Cardoso, M. J., Zuluaga, M. A., Modat, M., Powell, N. M., Wiseman, F. K., Cleary, J. O., Sinclair, B., Harrison, I. F., Siow, B., Popuri, K., Lee, S., Matsubara, J. A., Sarunic, M. V, Beg, M. F., Tybulewicz, V. L. J., Fisher, E. M. C., Lythgoe, M. F., & Ourselin, S. (2020). Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellum of the Tc1 mouse model of Down Syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI. NeuroImage, 117271. https://doi.org/https://doi.org/10.1016/j.neuroimage.2020.117271 Ma, D., Cardoso, M. J., Zuluaga, M. A., Modat, M., Powell, N., Wiseman, F., Tybulewicz, V., Fisher, E., Lythgoe, M. F., & Ourselin, S. (2015). Grey Matter Sublayer Thickness Estimation in the Mouse Cerebellum. In Medical Image Computing and Computer Assisted Intervention 2015 (pp. 644–651). https://doi.org/10.1007/978-3-319-24574-4_77

Reference

For the original information of the NeAt atlas, please please refer to the website: http://brainatlas.mbi.ufl.edu/, and the following two reference papers: Ma Yu, Smith David, Hof Patrick R, Foerster Bernd, Hamilton Scott, Blackband Stephen J, Yu Mei, Benveniste Helene In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy. Front. Neuroanat. 2, 1 (2008). Ma Yu, Hof P R, Grant S C, Blackband S J, Bennett R, Slatest L, McGuigan M D, Benveniste H A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience 135, 1203–15 (2005).

Funding The works in this repositories received multiple funding from EPSRC, UCL Leonard Wolfson Experimental Neurology center, Medical Research Council (MRC), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research center, the UK Regenerative Medicine Platform Safety Hub, and the Kings College London and UCL Comprehensive Cancer Imaging center CRUK & EPSRC in association with the MRC and DoH (England), UCL Faculty of Engineering funding scheme, Alzheimer Society Reseasrch Program from Alzheimer Society Canada, NSERC, CIHR, MSFHR Canada， Eli Lilly and Company, Wellcome Trust, the Francis Crick Institute, Cancer Research UK, and University of Melbourne McKenzie Fellowship.

The extension integrates within CKAN by adding templates and static assets (images) to the CKAN installation. The provided instructions detail how data portal administrators can synchronize the code repository within the Andino portal's container and update/restart the necesary services to activate new template code, images or changes. Benefits & Impact: The gcbaandinotheme extension streamlines the visual customization process for CKAN-based Andino portals, ensuring a consistent user experience and brand identity. The provided instructions assist in configuring and deploying a custom look and feel, as well as in creating and maintaining engaging content sections, directly impacting user engagement and information dissemination. Furthermore, the added instructions for installing and configuring ckanext-security and update procedures makes the system more secure.