Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Sharing my Sample Data (Bellabet) for my Google Analytics capstone course.

With this i was able to get a strong overview of the inner workings of the organization

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

This dataset is a custom reference of Google Analytics field definitions.

It was specifically compiled to enhance datasets like the Google Analytics 360 data from the Google Merchandise Store, which lacks field descriptions in its original BigQuery schema. By providing detailed definitions for each field, this reference aims to improve the interpretability of the data—especially when used by language models or analytics tools that rely on contextual understanding to process and answer queries effectively.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

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Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
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• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

In the way of my journey to earn the google data analytics certificate I will practice real world example by following the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Picking the Bellabeat example.

Dataset

This dataset was created by Tignangshu Chatterjee

Contents

Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

The City uses Google Analytics to track data about use of the City's website.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Ongoing analysis of over 650,000 websites with webbkoll. The front page for Italy-related domain names has been accessed through HTTPS or HTTP to gather data about third-party requests, cookies and other privacy-invasive features. Over 80 % of the websites in the sample appear to contain Google Analytics.

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

This dataset shows the number of page views each day of 2016 for data.edmonton.ca. This data is pulled from our Google Analytics and updated monthly.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Snapshot img

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.
Request Free Sample

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)

.

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

HR analytics, also referred to as people analytics, workforce analytics, or talent analytics, involves gathering together, analyzing, and reporting HR data. It is the collection and application of talent data to improve critical talent and business outcomes. It enables your organization to measure the impact of a range of HR metrics on overall business performance and make decisions based on data. They are primarily responsible for interpreting and analyzing vast datasets.

Download the data CSV files here ; https://drive.google.com/drive/folders/18mQalCEyZypeV8TJeP3SME_R6qsCS2Og

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

This data set is made available through the Google Analytics Coursera course. This data set is a part of a case study example, meant to showcase skills learned throughout the course.

Version update: The originally uploaded versions of the CSV files in this dataset included an extra column, "Unnamed: 0," which is not RAMP data and was an artifact of the process used to export the data to CSV format. This column has been removed from the revised dataset. The data are otherwise the same as in the first version.

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 2020. For a description of the data collection, processing, and output methods, please see the "methods" section below.

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 2020-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2020.

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 2020-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2020.

References

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

Weekly data from the Google Analytics tag for the Open Data Portal at OpenData.fcgov.com.

Analytics shown are presumed to be non-City-employees, as these data come from computers external to the City network. Each day starting at the first day for which there are data is included, and the URL is either a specific page or "all", specifying that every page in the domain is included. Specific-page URLs are filtered to the main Portal page or data assets, so "all" may capture more pages than specified individually.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This is replication data for the journal article "Sanctions, Sales, and Stigma: Intermediary Online Firms’ Market Role in Sustaining Trade", forthcoming in the Journal of International Economics. In the wake of Russia’s full-scale invasion of Ukraine in February 2022, online intermediaries enabled brands to retain a presence in the Russian market as many global companies—presumably or actually—withdrew due to legal and reputational concerns. Our paper examines how sales by intermediaries responded to international sanctions. Using novel data on customer transactions of 95 global brands from 1,761 web shops, we show that sales to Russia dropped significantly after the invasion, especially among shops from countries enacting export restrictions. This drop was substantial yet not absolute. Guided by a stylized conceptual framework, we explore which intermediary shops helped sustain sales to Russia, linking their actions to economic incentives and the brand-specific legal and reputational concerns. Overall, we demonstrate how market structure shapes shops’ compliance with sanctions and highlight how economic incentives undermine compliance.

Our analysis uses proprietary, high-quality first-party data from Grips Intelligence, a German business analytics firm. The data is collected via direct data-sharing agreements with partners and captures websites' performance data (e.g., user sessions, transactions, conversions) using web analytics services. This approach avoids recall biases and provides accurate, unsampled sales data.

The data's high accuracy is reflected in its widespread use among industrial customers for predicting company revenues, brand sales, and overall e-commerce performance, including six of the ten largest quantitative hedge funds, all tier-one global management consultancies, and major marketing research companies. The data has a global coverage, excluding China, and represents a significant percentage of total e-commerce revenue in various countries.

Our transaction data relies solely on online purchases, collecting all transactions registered by web shops through Google Analytics. Our data is a convenience sample of online intermediaries that use Google Analytics and opt-in into data sharing agreements with Grips Intelligence and its partners.

Additionally, we encode the presence of export bans on consumer products based on regulatory information. To identify global brands' exit from the Russian market, we complement our dataset with the Yale CELI List of Companies Leaving and Staying in Russia. We identify brands with special Russian demand based on Regulation No.~1532 by the Russian Federal Ministry of Industry and Trade, dated April 19, 2022.

Our data spans individual online transactions from January 2, 2021, to March 29, 2023. The replication data is aggregated by week, online intermediary, brand, and buyer country.

This was a exiting case study for the Google Data Analytics Certification 2023. I choose to do the Case Study 2, the goal was as a business analyst for a small health tracker company how can we use the data from Fitbit users to inform a decision for growth when comparing it to one of Bellabeat's products. I included apple watch users since the data did appear limited in the sample size being 33 participants and with the apple watch users the sample size went up to 59 participants.

I have included my notes from data cleaning process and a power point on my findings and recommendation.

Datasets were not my own and belong to Datasets - ‘FitBit Fitness Tracker Data’ by Mobius, 2022, https://www.kaggle.com/datasets/arashnic/fitbit License: CC0: Public Domain, sources: https://zenodo.org/record/53894#.X9oeh3Uzaao - ‘Apple Watch and Fitbit data’ by Alejandro Espinosa, 2022, https://www.kaggle.com/datasets/aleespinosa/apple-watch-and-fitbit-data, License: CC0: Public Domain, sources: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZS2Z2J