An interactive dashboard that showcases the City of Austin Open Data Portal (data.austintexas.gov) web traffic and search-term performance metrics. *City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj‐cccq

Web Analytics Market size was valued at USD 6.16 Billion in 2024 and is projected to reach USD 24.07 Billion by 2032, growing at a CAGR of 18.58% during the forecast period 2026-2032.Global Web Analytics Market DriversThe digital landscape is in constant flux, and at its core, understanding user behavior is paramount for any business aiming to thrive. This imperative fuels the robust expansion of the Web Analytics Market, driven by a confluence of technological advancements, evolving business needs, and shifting consumer behaviors. Let's delve into the major forces propelling this vital industry forward.Digitalization and the Explosive Growth of Online Presence: The most fundamental driver is the relentless march of digitalization. Businesses across every sector are establishing, expanding, and optimizing their online presence, whether through sophisticated e-commerce platforms, informative corporate websites, or engaging mobile applications. As more operations, customer interactions, and commerce migrate to the digital realm, the sheer volume of online activity creates an insatiable demand for tools that can decipher user journeys, measure website performance, and identify areas for improvement. This foundational shift necessitates web analytics to transform raw digital interactions into actionable insights, making it indispensable for strategic decision-making in the modern business environment.The Imperative for Data-Driven Decision Making: In today's competitive landscape, gut feelings and anecdotal evidence are no longer sufficient. Businesses are increasingly recognizing the critical importance of basing their strategies on empirical data. Web analytics provides this crucial foundation, offering deep insights into customer behavior, site usage patterns, conversion funnels, and potential drop-off points. From optimizing marketing spend to refining product offerings and enhancing user experience, data-driven decision-making, powered by comprehensive web analytics, allows companies to minimize risks, maximize opportunities, and achieve measurable growth, thereby solidifying its position as a core business intelligence tool.Proliferation of Mobile Devices and Mobile Web Traffic: The smartphone revolution has profoundly reshaped how users interact with the internet. With billions of people globally accessing the web predominantly via mobile devices and tablets, understanding mobile-specific behaviors has become a paramount concern. Web analytics tools are evolving rapidly to effectively capture and analyze interactions across a myriad of devices, operating systems, and browser types. This includes tracking mobile app usage, responsive website performance, and ensuring a seamless cross-device user experience. The pervasive nature of mobile traffic means that robust mobile analytics capabilities are no longer a luxury but a necessity for any comprehensive web analytics solution.

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Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

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By Deployment Insights

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

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting, integra

The Web Analytics Market in Retail and CPG is experiencing robust growth, projected to reach $1.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing need for data-driven decision-making within retail and CPG companies is paramount. Businesses are leveraging web analytics to gain deeper insights into customer behavior, optimize marketing campaigns, and personalize the shopping experience. The rise of e-commerce and omnichannel strategies further intensifies the demand for sophisticated web analytics solutions. Specifically, the ability to track customer journeys across multiple touchpoints, analyze real-time data, and measure the effectiveness of online marketing initiatives are crucial factors driving market growth. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more predictive analytics, empowering businesses to anticipate customer needs and proactively address potential challenges. Competitive pressures are also pushing companies to adopt advanced web analytics technologies to gain a competitive edge and improve operational efficiency. Segmentation reveals a strong demand across both SMEs and large enterprises, with significant application in search engine optimization (SEO), online marketing automation, customer profiling, application performance management, and social media management. Major players like Google, IBM, Meta, and Salesforce are strategically positioned to capitalize on this expanding market. The market's growth trajectory is expected to be consistent throughout the forecast period, driven by continued digital transformation within the retail and CPG sectors. While challenges such as data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive. The North American market is anticipated to hold a significant share, given the region's advanced digital infrastructure and high adoption of web analytics technologies. However, other regions, particularly Asia Pacific, are expected to show significant growth due to the rapid expansion of e-commerce and increasing internet penetration. The market's future success hinges on the continued development of innovative analytics solutions that address the specific needs of retail and CPG companies, providing actionable insights that drive revenue growth, customer loyalty, and operational efficiency. Recent developments include: April 2024 - IBM Consulting and Microsoft have unveiled the opening of the IBM-Microsoft Experience Zone in Bangalore, India. The Experience Zone is designed as an exclusive venue where clients can delve into the potential of generative AI, hybrid cloud solutions, and other advanced Microsoft offerings. The goal is to expedite their business transformations and secure a competitive edge., January 2024 - Microsoft Corp. announced a suite of generative AI and data solutions tailored for retailers. These solutions cover every touchpoint of the retail shopper journey, from crafting personalized shopping experiences and empowering store associates to harness and consolidating retail data, ultimately aiding brands in better connecting with their target audiences. Microsoft's initiatives include introducing copilot templates on Azure OpenAI Service, enhancing retailers' ability to craft personalized shopping experiences, and streamlining store operations. Microsoft Fabric hosts advanced retail data solutions, while Microsoft Dynamics 365 Customer Insights boasts new copilot features. Microsoft also rolled out the Retail Media Creative Studio within the Microsoft Retail Media Platform. These advancements collectively bolster Microsoft Cloud for Retail, providing retailers with diverse tools to integrate copilot experiences across the entire shopper journey seamlessly.. Key drivers for this market are: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Potential restraints include: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Notable trends are: Search Engine Optimization and Ranking Sector Significantly Driving the Market Growth.

Web Analytics Platform Market Outlook

According to our latest research, the global web analytics platform market size reached USD 7.6 billion in 2024 and is anticipated to grow at a robust CAGR of 17.8% from 2025 to 2033. By the end of the forecast period, the market is projected to achieve a value of USD 25.4 billion by 2033. The primary growth factor driving this expansion is the increasing demand for actionable insights from digital channels, which has encouraged organizations across industries to invest heavily in data-driven marketing and customer engagement strategies.

A significant catalyst for the web analytics platform market’s growth is the rapid adoption of digital transformation initiatives among enterprises. As businesses strive to enhance their online presence and improve customer experiences, the need to understand user behavior across websites, mobile apps, and social media platforms has become paramount. Web analytics platforms offer comprehensive tools for tracking, analyzing, and interpreting user interactions, enabling organizations to optimize content, personalize marketing efforts, and maximize conversion rates. The integration of advanced technologies such as artificial intelligence and machine learning further amplifies the capability of these platforms, allowing for predictive analytics and more precise targeting. This technological evolution has made web analytics indispensable for organizations seeking to maintain a competitive edge in the digital economy.

Another key growth driver is the exponential rise in e-commerce and digital advertising expenditures worldwide. Retailers and brands increasingly rely on web analytics platforms to monitor campaign performance, evaluate customer journeys, and allocate budgets more efficiently. The proliferation of multichannel marketing strategies, encompassing email, social media, search engines, and display advertising, has added complexity to digital marketing efforts. Web analytics platforms address this complexity by offering unified dashboards and cross-channel attribution models, which help marketers gain a holistic view of their campaigns’ effectiveness. Furthermore, the growing emphasis on data privacy and compliance has prompted vendors to enhance their solutions with robust security features, ensuring organizations can leverage analytics without compromising regulatory obligations.

The surge in remote work and digital collaboration, particularly following the global pandemic, has accelerated the adoption of cloud-based web analytics platforms. Organizations are increasingly favoring solutions that offer scalability, flexibility, and seamless integration with existing business systems. Cloud deployment models facilitate real-time data access and collaboration across geographically dispersed teams, supporting agile decision-making and faster response to market trends. Additionally, the democratization of analytics—through user-friendly interfaces and self-service features—has empowered non-technical users to derive insights independently, further broadening the market’s addressable base. These trends collectively underscore the sustained momentum of the web analytics platform market.

From a regional perspective, North America continues to dominate the web analytics platform market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major technology vendors, high digital adoption rates, and mature online retail ecosystems contribute to North America’s leadership. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by burgeoning internet penetration, rapid e-commerce expansion, and increasing investments in digital infrastructure across emerging economies such as China and India. Europe maintains a strong position due to stringent data privacy regulations, which have spurred demand for compliant analytics solutions. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as businesses in these regions accelerate their digital transformation journeys.

Component Analysis

The web analytics platform market by component is primarily segmented into software and services. The software segment dominates the market, reflecting the critical role of analytics engines, dashboards, and data visualization tools in extracting actionable insights from vast digital datasets. Modern web analytics software is equipped with advanced features suc

This dataset contains statistics related to searches performed in websites of the Publications Office. The data included corresponds exclusively to searches performed in the websites that created a click on a search result (e.g., publication, legal document).

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The Web Analytics Market In Retail And CPG report segments the industry into By Offering (Solution, Services), By Organization Size (SMEs, Large Enterprises), By Application (Search Engine Optimization And Ranking, Online Marketing & Marketing Automation, Customer Profiling And Feedback, Application Performance Management, Social Media Management, Others), and Geography (North America, Europe, Asia, and more).

Explore the dynamic Search Engine Marketing (SEM) market: drivers, trends, restraints, and forecasts. Discover key segments like PPC and web analytics, and leading companies shaping the future of online advertising.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

This dataset contains trace data describing user interactions with the Inter-university Consortium for Political and Social Research website (ICPSR). We gathered site usage data from Google Analytics. We focused our analysis on user sessions, which are groups of interactions with resources (e.g., website pages) and events initiated by users. ICPSR tracks a subset of user interactions (i.e., other than page views) through event triggers. We analyzed sequences of interactions with resources, including the ICPSR data catalog, variable index, data citations collected in the ICPSR Bibliography of Data-related Literature, and topical information about project archives. As part of our analysis, we calculated the total number of unique sessions and page views in the study period. Data in our study period fell between September 1, 2012, and 2016. ICPSR's website was updated and relaunched in September 2012 with new search functionality, including a Social Science Variables Database (SSVD) tool. ICPSR then reorganized its website and changed its analytics collection procedures in 2016, marking this as the cutoff date for our analysis. Data are relevant for two reasons. First, updates to the ICPSR website during the study period focused only on front-end design rather than the website's search functionality. Second, the core features of the website over the period we examined (e.g., faceted and variable search, standardized metadata, the use of controlled vocabularies, and restricted data applications) are shared with other major data archives, making it likely that the trends in user behavior we report are generalizable.

This asset is a filter (derived view of a dataset) based on the system dataset, 'Site Analytics: Catalog Search Terms' which is automatically generated by the City of Austin Open Data Portal (data.austintexas.gov). It provides data on the words and phrases entered by site users of in search bars that look through the data catalog for relevant information. Catalog searches using the Discovery API are not included.

Each row in the dataset indicates the number of catalog searches made using the search term from the specified user segment during the noted hour.

Data are segmented into the following user types: • site member: users who have logged in and have been granted a role on the domain • community user: users who have logged in but do not have a role on the domain • anonymous: users who have not logged in to the domain

Data are updated by a system process at least once a day, if there is new data to record.

Data provided by: Tyler Technologies Creation date of data source: January 31, 2020

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

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The Catalog Search Terms dataset captures the words and phrases input by users in search bars that look through the data catalog for relevant information. Data can also be categorized by user segments.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.

The record will be revised periodically to make new data available through the remainder of 2021.

Methods

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above. Also as noted above, daily data are downloaded for each IR in two sets which cannot be combined. One dataset includes the URLs of items that appear in SERP. The second dataset is aggregated by combination of the country from which a search was conducted and the device used.

As a result, two CSV datasets are provided for each month of published data:

page-clicks:

The data in these CSV files correspond to the page-level data, and include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “page-clicks”. For example, the file named 2021-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2021.

country-device-info:

The data in these CSV files correspond to the data aggregated by country from which a search was conducted and the device used. These include the following fields:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
index: The Elasticsearch index corresponding to country and device access information data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “country-device-info”. For example, the file named 2021-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2021.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

This dataset includes data on the words and phrases input by users in search bars that look through the data catalog for relevant information. Catalog searches using the Discovery API are not included.

Each row in the dataset indicates the number of catalog searches made using the search term from the specified user segment during the noted hour.

• site member: users who have logged in and have been granted a role on the domain
• community user: users who have logged in but do not have a role on the domain
• anonymous: users who have not logged in to the domain

Data are updated by a system process at least once a day.

Please see Site Analytics: Catalog Search Terms for more detail.

Considering dataset description

The user wants an improved description of the "online_shoppers_intention" dataset. I'll base the rewrite on metadata like 12,330 sessions, collected over a year, and incorporate details about the dataset’s purpose, data components, and possible uses. I could consider writing it as a cohesive paragraph or bullet points. I'll need to include citations too—let's search for further details on OpenML. Once I find that, I'll craft a clearer, more thorough description.

Thought for 5 seconds

Overview The Online Shoppers Purchasing Intention dataset captures 12,330 distinct web‐session records collected over a one‐year span from an e-commerce site, with each session belonging to a different visitor to prevent user‐ or campaign-specific biases. Originally published in 2017 and licensed under CC BY 4.0, it was curated by Sakar et al. for benchmarking classifiers on independent and identically distributed tabular data.

Features

• Numerical (10):

  • Administrative, Informational, ProductRelated (counts of pages visited) and their corresponding _Duration fields (total time in seconds spent on those pages).
  • BounceRates, ExitRates (average session‐level bounce and exit rates) and PageValues (average monetary value of pages preceding a purchase).
  • SpecialDay (normalized [0 – 1] indicator of how close the visit was to major shopping holidays, e.g. Valentine’s Day).

• Categorical (7):

  • Month (Aug – Sep), OperatingSystems (8 codes), Browser (13 codes), Region (9 codes), TrafficType (20 codes), VisitorType (“New_Visitor,” “Returning_Visitor,” “Other”), and Weekend (True/False).

Target and Class Distribution

• Revenue (False/True) denotes whether the session ended in a purchase.
• Of the 12,330 sessions, 84.5 % (10,422) did not result in revenue, while 15.5 % (1,908) did.

Intended Use This dataset is ideal for developing and comparing binary classification models—ranging from multilayer perceptrons and LSTM networks to tree-based methods—to predict online purchasing intention in a controlled, time-invariant setting.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2017-01_RAMP_all.csv. Using this example, the file 2017-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2017.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

Search and Content Analytics Market Outlook

The global search and content analytics market size was estimated to be USD 5.9 billion in 2023 and is projected to reach USD 16.5 billion by 2032, growing at a CAGR of 12.1% over the forecast period. This substantial growth is primarily driven by the increasing demand for data-driven insights across various industries that aim to enhance their decision-making processes. The expansion of digital content and the need for effective content management and optimization are significant factors contributing to this upward trajectory. With businesses striving to improve their online presence and engagement, the emphasis on advanced analytics tools continues to rise, thereby fuelling the market's expansion.

One of the core growth factors propelling the search and content analytics market is the exponential growth of data generated through digital channels. Businesses today are inundated with vast quantities of unstructured data derived from social media, web pages, online forums, and other digital environments. The ability to transform this raw data into actionable insights is increasingly becoming a competitive necessity. Organizations are leveraging search and content analytics to navigate this complex data landscape, enabling them to understand consumer behavior, optimize marketing strategies, and improve content delivery. This growing reliance on data analytics to derive meaningful insights from voluminous data sets is a crucial driver of market growth.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are further accelerating the adoption of search and content analytics tools. These technologies enhance the capabilities of analytics software, enabling it to process and analyze large data sets with greater speed and accuracy. AI-powered analytics solutions offer features like natural language processing for more precise sentiment analysis, predictive analytics for forecasting trends, and automated recommendations for content optimization. The integration of AI and ML in analytics solutions not only streamlines operations but also enhances the precision and reliability of the insights generated, thus boosting the market growth.

The increasing focus on personalized customer experiences is another significant factor driving the search and content analytics market. As businesses seek to offer more personalized interactions, the need for understanding customer preferences and behaviors becomes paramount. Search and content analytics tools facilitate deeper audience insights, allowing companies to tailor their content and marketing strategies accordingly. This trend is particularly prevalent in sectors like retail, e-commerce, and media, where customer engagement and satisfaction are crucial. By leveraging analytics solutions, companies can refine their content strategies to better align with consumer expectations, thereby enhancing customer loyalty and driving revenue growth.

Regionally, North America is expected to lead the market, driven by the presence of major technology companies and early adoption of advanced analytics solutions. The region's strong technological infrastructure, coupled with a high concentration of digital businesses, facilitates the widespread implementation of search and content analytics tools. Europe follows closely, with increasing investments in digital transformation initiatives driving market expansion. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth rate, spurred by the rapid digitalization and growing e-commerce industry in countries like China and India. These regional dynamics illustrate the global reach and potential of the search and content analytics market.

Component Analysis

The search and content analytics market is segmented into software and services components, each playing a crucial role in the ecosystem of data-driven insights. Software solutions form the backbone of analytics applications, offering platforms for data collection, processing, and analysis. These solutions are critical for businesses seeking to harness the power of big data to drive strategic decisions. The software segment is witnessing robust growth, fueled by continuous innovations and enhancements in analytics capabilities. Cloud-based analytics solutions, in particular, are gaining traction due to their scalability, cost-effectiveness, and ease of deployment. As businesses increasingly migrate towards cloud infrastructures, the demand for cloud-based analytics software is expected to soar.

Within the services component, a broad spectrum of