Introduction

Landing Page Statistics: Landing pages are dedicated web pages designed to convert visitors into leads or customers by focusing on a single, clear call to action. In 2024, the median landing page conversion rate across industries is 6.6%, with top-performing pages exceeding 20%. Email-driven traffic achieves the highest average conversion rate at 19.3%, outperforming paid search (10.9%) and paid social (12%).

Mobile devices account for 82.9% of landing page traffic, yet desktop users exhibit a higher average conversion rate of 12.1% compared to 11.2% for mobile users. Speed is crucial; a one-second delay in page load time can reduce conversions by 7%. Incorporating videos can boost conversions by 86%, and personalized landing pages can convert 202% better than generic ones.

Design elements significantly impact performance. Landing pages with five or fewer form fields convert 120% better than those with more fields. Pages with a single, clear call to action achieve a 13.5% conversion rate, compared to 11.9% for pages with multiple CTAs. Additionally, 38.6% of marketers report that videos enhance landing page conversion rates more than any other element.

Let us check out some of the Landing page statistics concerning landing page performance and the secrets of landing page success.

It was found in 2021 that the share of businesses which included video on their landing pages amounted to ** percent, a lower result than in the previous year, when ** percent of companies were using video on their corporate landing pages.

In this table, you’ll see the average landing page conversions based on the subscription rate they generated across industries.

The Landing Page Optimization Tool market has emerged as a vital segment within the broader digital marketing landscape, catering to businesses looking to enhance their online conversion rates. These tools are designed to help marketers create, test, and optimize landing pages that effectively capture visitor attent

A 2024 study showed that direct-to-consumer (D2C) brands engaged with their users by asking them to sign up for newsletters. Nearly ** percent of French and UK websites had this feature on their landing page. Payments information were much less common, with roughly ********* of European brands showing them before the checkout.

The global landing page builders market is projected to grow significantly over the next decade. Valued at USD 715.5 million in 2025, the market is forecast to reach approximately USD 2.72 billion by 2035.

Attributes	Description
Historical Size, 2024	USD 630.1 million
Estimated Size, 2025	USD 715.5 million
Projected Size, 2035	USD 2.72 billion
Value-based CAGR (2025 to 2035)	14.3%

Semi-Annual Market Update

Particular	Value CAGR
H1	13.6% (2024 to 2034)
H2	14.2% (2024 to 2034)
H1	14.0% (2025 to 2035)
H2	14.6% (2025 to 2035)

Country-wise Insights

Countries	CAGR from 2025 to 2035
India	16.6%
China	15.4%
Germany	14.1%
KSA	12.6%
United States	14.6%

The Landing Page Builder Software market has experienced significant growth in recent years, emerging as a crucial tool for businesses seeking to optimize their online presence. These software solutions are designed to simplify the creation of landing pages?dedicated web pages optimized for specific marketing campai

In this table, we’re looking at whether adding video content (including links to your video hosting platforms) could help you boost your engagement metrics, primarily the average click-th rough and click-to-open rates.

This reference provides significant summary information about health expenditures and the Centers for Medicare & Medicaid Services' (CMS) programs. The information presented was the most current available at the time of publication. Significant time lags may occur between the end of a data year and aggregation of data for that year.

This dataset reports downloads of metadata records and documents from the Development Experience Clearinghouse (DEC).

The ckanext-ds-stats extension for CKAN provides analytics and statistics capabilities by integrating with Google Analytics. It leverages dga-stats and ga-report to pull data from Google Analytics and display relevant statistics within the CKAN interface, primarily focusing on package resource downloads. Facilitating data-driven decisions about dataset usage and popularity, this extension helps CKAN administrators understand how users interact with their data catalog. It also supports cross-domain tracking using Google's site linking feature. Key Features: Google Analytics Integration: Utilizes the Google Analytics API to retrieve website usage data, providing insights into dataset and resource access patterns. Download Tracking: Tracks and displays the number of downloads for individual resources on package pages, providing immediate feedback on resource popularity. Bounce Rate Tracking: Records bounce rate information for a specified page (typically the home page), enabling assessment of landing page effectiveness. Cross-Domain Tracking: Supports cross-domain tracking to consolidate analytics data from multiple related domains into a single Google Analytics property. Event Tracking: (Potentially for CKAN 1.x) Enables tracking of events beyond resource downloads, providing a more holistic view of user interactions with the CKAN instance. Configurable Analytics Settings: Offers several configuration options, including resource_prefix to easily filter resource downloads in Google Analytics, and domain settings to specify the tracking domain.

East Baton Rouge Parish Library computer usage statistics are organized by branch, year, and month. This dataset only includes the count for library patrons who have logged in to the Library’s public computers, located at any of the 14 locations.

A historical tabulation of selected quarterly transient lodging tax return statistics by accommodation type. Taxable sales, state transient lodging tax liability, and the number of lodging providers with tax liability are reported for each accommodation type. See "data limitations" below.

Simulated Citizens Broadband Radio Service device deployments, calculated federal incumbent protection move lists, and calculated aggregate interference statistics. This data is associated with publication, "3.5 GHz Federal Incumbent Protection Algorithms," M. R. Souryal, T. T. Nguyen, and N. J. LaSorte, in Proc. IEEE DySPAN 2018, Oct. 2018.

A file containing all Min/Max Baseline Reports for 2005-2023 in their original format is available in the Attachments section below. A second file includes a separate set of reports, made available from 2002-2017, that did not include OLDMEDLINE records.

MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

The monthly card payment statistics provide data in relation to credit and debit card transactions undertaken by Irish resident households. The data includes the monthly value and volume of transactions across both credit and debit cards by Irish households. The data is collected from issuers of credit and debit cards and specifically from reporting agents that are resident in Ireland (including established foreign branches). The aggregate data is further broken down into, remote and non-remote card spending; contactless and mobile wallet card spending; sectoral card spending; domestic and non-domestic card spending; regional card spending in Ireland; and cash withdrawals. A breakdown of the number of credit & debit cards currently issued to Irish residents is also provided. Note, only Personal Cards are in scope for this reporting, business cards and cards issued to non-Irish residents are not included. Additionally, data files uploaded here follow the SDMX –ML format where Series Key are the primary identifier for a reporting period (Date for which the data is reported is represented in the Reporting Period field). For example : PCI.M.IE.W2.PCS_ALL.11.PN is the series key and each element/dimension between the delimiter “.” is expanded with a description in subsequent columns ending with the subscript “DESC” to understand the meaning of each element/dimension. The Observation_free column represents the value (€ EUR) or Volume (PN) of transactions depending on the last element/dimension, EUR or PN. For further information on the Payment Statistics Monthly, the reporting instructions in the Landing page link has additional details about the table and the column names used in this data collection.

This child item describes a machine learning model that was developed to estimate public-supply water use by water service area (WSA) boundary and 12-digit hydrologic unit code (HUC12) for the conterminous United States. This model was used to develop an annual and monthly reanalysis of public supply water use for the period 2000-2020. This data release contains model input feature datasets, python codes used to develop and train the water use machine learning model, and output water use predictions by HUC12 and WSA. Public supply water use estimates and statistics files for HUC12s are available on this child item landing page. Public supply water use estimates and statistics for WSAs are available in public_water_use_model.zip. This page includes the following files: PS_HUC12_Tot_2000_2020.csv - a csv file with estimated monthly public supply total water use from 2000-2020 by HUC12, in million gallons per day PS_HUC12_GW_2000_2020.csv - a csv file with estimated monthly public supply groundwater use for 2000-2020 by HUC12, in million gallons per day PS_HUC12_SW_2000_2020.csv - a csv file with estimated monthly public supply surface water use for 2000-2020 by HUC12, in million gallons per day Note: 1) Groundwater and surface water fractions were determined using source counts as described in the 'R code that determines groundwater and surface water source fractions for public-supply water service areas, counties, and 12-digit hydrologic units' child item. 2) Some HUC12s have estimated water use of zero because no public-supply water service areas were modeled within the HUC. STAT_PS_HUC12_Tot_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply total water use from 2000-2020 STAT_PS_HUC12_GW_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply groundwater use for 2000-2020 STAT_PS_HUC12_SW_2000_2020.csv - a csv file with statistics by HUC12 for the estimated monthly public supply surface water use for 2000-2020 public_water_use_model.zip - a zip file containing input datasets, scripts, and output datasets for the public supply water use machine learning model version_history_MLmodel.txt - a txt file describing changes in this version

This information covers fires, false and other incidents attended by firecrews, and the statistics include the numbers of incidents, fires, fatalities and casualties as well as information on response times to fires. The Home Office has responsibility for fire services in England. In the past the Department for Communities and Local Government (who previously had responsibility for fire services in England) produced data tables for Great Britain and at times the UK. Similar information for devolved administrations are available at Scotland: Fire and Rescue Statistics, Wales: Community safety and Northern Ireland: Fire and Rescue Statistics (see Landing Page for links).

This information covers data on the workforce. The Home Office has responsibility for fire services in England. In the past the Department for Communities and Local Government (who previously had responsibility for fire services in England) produced data tables for Great Britain and at times the UK. Similar information for devolved administrations are available at Scotland: Fire and Rescue Statistics, Wales: Community safety and Northern Ireland: Fire and Rescue Statistics (see Landing page for links).