On this page, you'll find detailed tables showcasing the metrics and dimensions of each data set in our Google Analytics 3 / Universal Analytics exports. These tables are your guide to understanding what data is available in the Excel files and how it can be leveraged to fulfill your analytical objectives. Note, the data set in BigQuery are the same as those exported to Excel.
All datasets are aggregated data, not raw data. This means that metrics are summed up or averages for a certain group of dimensions and dates. Data sets are exported for different time frames as displayed below and may have different time granularity (monthly, yearly). Please note, as the the Google API does not offer the export of raw data, there is also no way to download really all data for a property (also not with other tools or methods). Therefore we provide and extensive set of reports build by our analytics experts which cover the vast majority of marketing analytics use cases.
To cater to various reporting requirements, we've incorporated three distinct time frames into our reports
All Time: This offers a panoramic view of your data, spanning the entirety of your data collection period. It's ideal for long-term trend analysis and strategic planning.
12 Months: Focusing on the most recent year, this view provides a detailed month-by-month analysis, perfect for understanding recent trends and making timely adjustments.
Retention Period: Some detailed data is only available in Google Analytics for a limited time. This depends on the Data Retention setting in your Google Analytics account. We detect this time range and export data accordingly.
The time ranges are indicated as part of the file name.
We add the '_smp' sampling flag to the file name. This flag indicates whether the data in the file has been sampled, ensuring you're always aware of the data's representativeness and can make informed decisions accordingly. Sampling is an artifact of the Google Analytics API. It will happen automatically if the raw data set is too large. Sampling can only occur in the detailed reports which are subject to the above mentioned retention Period.
Some dimensions may see a lot of different values (so called high cardinality dimensions). In this case exports will be truncated after 1 million rows. The 1 million rows are subset of all rows leading to similar effects as explained in the "understanding sampling" section.
Also, in rare cases we see the Google Analytics API return too little data for some time ranges. We can't unfortunately influence this behavior so we recommend to check data for completeness after downloading your reports.
The 'overview_scores_12months' dataset is a collection of key performance indicators designed to provide an overview of user engagement and website performance. This concise array of metrics gives you a clear view of user activity on your site, including total users and how they engage with your content. By examining trends in new users, session counts, pageviews, and more, you can gain a deeper understanding of your audience's behavior over an annual cycle.
Time granularity = None (sum of last 12 months)
Time range = Last 12 months
The 'overview_scores_12months_comp' dataset present the number for the last period of 12 months.
In other words, is a benchmark for overview_scores_12months.
Time granularity = None (sum of previous 12 months)
Time range = Previous 12 months
The 'overview_scores_allTime' dataset present the numbers for all time available on the property.
Time granularity = None (sum of all time)
Time range = All time available on the property
The dataset 'overview_kpi_allTime' is designed to analyze key performance indicators (KPIs) related to website analytics over the entire duration of data collection. Unlike the previous datasets, this one includes 'dimensions' that suggest a focus on time-based analysis. The dimensions 'year' and 'yearMonth' indicate that the data can be broken down by both annual and monthly intervals, providing a more granular view of user behavior and website performance over time. The metrics, which include users, new users, sessions, pageviews, session duration, and bounces, offer insights into user engagement and interaction with the website.
Time granularity = Month
Time range = All time available on the property
The dataset 'overview_kpi_yearly_allTime' is tailored to provide a yearly overview of key performance indicators (KPIs) relevant to website analytics, spanning the entire period of data collection. It contains a single dimension, 'year', which implies that the data is segmented on an annual basis. This allows for a year-over-year comparison and trend analysis of website interactions and user behavior. The metrics included in this dataset are 'newUsers', 'sessions', 'pageviews', 'sessionDuration', 'bounces', and 'users'. These metrics collectively offer insights into new and returning user engagement, site usage patterns, and overall website performance from a yearly perspective.
Time granularity = Year
Time range = All time available on the property
The 'overview_channel_allTime' dataset offers an in-depth analysis of user engagement and website performance metrics, with no specified time limit, implying a comprehensive all-time overview. It incorporates dimensions that break down the data by time (year, yearMonth) and user acquisition channels (channelGrouping), enabling a detailed understanding of performance trends over time and across different marketing channels. The metrics included in this dataset are diverse, ranging from session information to user behavior metrics like pageviews, session durations, bounce rates, and time spent on page. This dataset is particularly valuable for long-term performance analysis, understanding the effectiveness of different channels, and for identifying changes in user behavior over extended periods.
Time granularity = Month
Time range = All time available on the property
The dataset 'overview_sourcemedium_allTime' is an all-encompassing dataset focusing on user engagement metrics across various time granularities and source mediums. This dataset spans an indefinite range, as implied by the term "allTime," providing a longitudinal view of user interactions. It includes dimensions like 'year', 'yearMonth', and 'sourceMedium', which allow for a detailed analysis of user engagement trends over time and across different traffic sources or mediums. The metrics included offer a thorough insight into session details, pageviews, user engagement efficiency, and bounce rates. This dataset is crucial for understanding not only the volume of interactions but also the quality and behavior of users over an extended period, segmented by when and how they access the site.
Time granularity = Month
Time range = All time available on the property
The 'overview_deviceCategory_allTime' table presents a detailed breakdown of user interactions with your site, categorized by the type of device they use. It includes dimensions like 'year', 'yearMonth', and 'deviceCategory', which allow you to track and analyze traffic patterns over time and across different device types. The metrics provided, such as 'sessions', 'pageviews', and 'bounceRate', offer insights into user engagement and behavior. This table is an invaluable resource for understanding the comprehensive performance of your site across various devices throughout the entirety of your data history.
Time granularity = Month
Time range = All time available on the property
The 'overview_country_allTime' dataset offers a detailed analysis of user interaction metrics without any specific time limit, possibly encompassing the entire duration of the data collection. The inclusion of dimensions such as 'year', 'yearMonth', and 'country' allows for a granular examination of data across different time frames and geographical locations. This level of detail facilitates in-depth analysis of trends and patterns over time and across various regions. The comprehensive set of metrics included provides insights into session data, pageviews, user engagement efficiency, duration of sessions, and bounce rates. Such a dataset is crucial for understanding long-term user behavior and website performance across different countries, and for making informed decisions about global strategies and localization efforts.
Time granularity = Month
Time range = All time available on the property
The dataset 'audience_location_retentionPeriod' is structured to analyze user engagement and site performance metrics based on geographical and temporal dimensions. The dimensions include 'year', 'yearMonth', 'country', 'city', 'region', and 'language', providing a detailed breakdown of user data by location and time. These dimensions allow for an in-depth analysis of audience distribution and behavior over different periods (yearly and monthly) and across various geographic locations. Metrics such as 'users', 'sessions', 'pageviews', 'sessionDuration', 'bounces', 'goalCompletionsAll', 'transactions', and 'transactionRevenue' offer insights into user engagement, site interaction, and economic performance. This dataset is particularly valuable for understanding audience retention and engagement patterns in relation to specific locations and timeframes.
Time granularity = Month
Time range = Retention period
The dataset 'audience_device_retentionPeriod' provides a detailed analysis of user interactions with a focus on device usage and retention over time. It incorporates dimensions that capture temporal aspects (year, yearMonth) and user device preferences (deviceCategory, browser, operatingSystem), allowing for a granular understanding of user behavior across different time periods and technological platforms. Metrics such as user counts, session data, pageviews, session duration, bounces, and various goal completions and transaction data offer insights into user engagement, website effectiveness, and economic performance. This dataset is particularly valuable for analyzing how different devices and operating systems impact user behavior and retention, as well as for understanding temporal trends in user interactions and revenue generation.
Time granularity = Month
Time range = Retention period
The dataset 'audience_demography_retentionPeriod' provides an insightful analysis of user demographics and engagement metrics, with a focus on retention over an unspecified period. This dataset includes dimensions such as 'yearMonth', 'userGender', and 'userAgeBracket', indicating its capability to segment data based on time, gender, and age groups. Such segmentation is crucial for understanding the behavior and preferences of different demographic groups. The metrics in this dataset, including user counts, session details, pageview statistics, and economic measures like transactions and revenue, offer a comprehensive view of user engagement and economic performance. This dataset is especially valuable for evaluating the effectiveness of targeting strategies and the economic impact of various demographic groups.
Time granularity = Month
Time range = Retention period
The 'audience_interest_retentionPeriod' dataset focuses on user engagement and conversion metrics, segmented by time and interest categories over a specific retention period. The inclusion of the 'yearMonth' dimension indicates that the data is structured to facilitate a monthly analysis, providing insights into how user behavior and engagement trends evolve over time. The 'interestInMarketCategory' dimension adds a layer of specificity, allowing for the examination of user interactions based on their market interests. Key metrics such as user count, session details, pageviews, session duration, bounce rates, goal completions, transactions, and transaction revenue offer a comprehensive view of user engagement and website performance. This dataset is particularly valuable for understanding how different market interest groups behave and convert over time, enabling targeted marketing and optimization strategies.
Time granularity = Month
Time range = Retention period
The dataset 'audience_behaviour_timings_retentionPeriod' provides a detailed analysis of user behavior and engagement, with a focus on timing and retention aspects. The dimensions included in this dataset such as 'year', 'yearMonth', 'sessionDurationBucket', 'sessionCount', 'daysSinceLastSession', and 'userType' offer a multi-layered perspective on how users interact with the service over different time frames and conditions. The temporal dimensions ('year' and 'yearMonth') allow for an analysis of trends and patterns over specific time periods. 'SessionDurationBucket' and 'SessionCount' provide insights into the length and frequency of user sessions, while 'DaysSinceLastSession' and 'userType' offer data on user retention and the type of users (new vs. returning). The metrics included, such as 'sessions', 'pageviews', 'sessionDuration', 'bounces', 'goalCompletionsAll', 'transactions', and 'transactionRevenue', offer quantitative measures of user engagement, site performance, and commercial outcomes. This dataset is particularly useful for understanding how different types of users behave over time and how their interactions translate into tangible outcomes like goal completions and revenue.
Time granularity = Month
Time range = Retention period
The dataset 'audience_content_retentionPeriod' is structured to provide an in-depth analysis of audience engagement and content performance, with a focus on retention over various periods. The dimensions included in this dataset—such as 'year', 'yearMonth', 'pagePath', 'pageTitle', and various content groups—suggest its capability to offer detailed insights into how content is consumed over time and how it performs in attracting and retaining users. The time granularity, indicated by 'year' and 'yearMonth', allows for both annual and monthly performance reviews. Metrics like 'sessions', 'pageviews', 'timeOnPage', and 'transactionRevenue' further enrich this analysis, providing quantitative data on user engagement, content effectiveness, and financial outcomes. This dataset is particularly useful for content strategists and marketers looking to optimize content for better audience retention and increased revenue.
Time granularity = Month
Time range = Retention period
The 'audience_behaviour_search_retentionPeriod' dataset provides a detailed analysis of user search behavior over a specified retention period. The dimensions included suggest a focus on temporal trends (year and month), as well as various aspects of the search experience, such as the starting page of the search, keywords used, search categories, and the destination page after the search. The time granularity, indicated by the inclusion of 'yearMonth', allows for a monthly analysis of search patterns. Metrics in this dataset cover a wide range of search-related activities, including the total number of sessions involving searches, unique searches, depth of searches, views of search results, exits during a search, and the duration of search sessions. This dataset is pivotal for understanding how users interact with the search function, providing insights into user preferences, search efficiency, and overall engagement with the search feature on a platform.
Time granularity = Month
Time range = Retention period
The 'audience_behaviour_weekday_hour_allTime' dataset provides an in-depth analysis of user behavior and engagement across various dimensions and metrics, without a specified time limit, implying coverage from the beginning of data collection to the current date. It incorporates time granularity at the level of years, months, hours, and days of the week, offering a detailed view of how user interactions vary over different timescales. The inclusion of dimensions such as 'year', 'yearMonth', 'hour', and 'dayOfWeekName' allows for sophisticated temporal analysis, facilitating insights into patterns and trends at specific times of the day and week, as well as monthly and yearly trends. Metrics extend beyond basic engagement indicators to include transactional data, providing a holistic view of not just how users interact with the platform but also their economic impact through 'transactions' and 'transactionRevenue'. This dataset is particularly valuable for identifying temporal engagement trends and their correlation with revenue-generating activities.
Time granularity = Month
Time range = All time available on the property
The dataset 'acquisition_channels_retentionPeriod' is designed to provide insights into user acquisition and behavior over different retention periods. It features a blend of time-based dimensions such as 'year' and 'yearMonth', which allows for analyzing data over various time granularities, from annual to monthly. Additional dimensions like 'channelGrouping', 'sourceMedium', 'campaign', 'keyword', and 'adContent' offer a detailed breakdown of how different marketing channels and strategies contribute to user acquisition and engagement. The metrics in this dataset, including sessions, new users, pageviews, session duration, bounces, goal completions, transactions, and transaction revenue, provide a comprehensive view of user engagement and conversion effectiveness. This dataset is particularly useful for evaluating the performance of marketing campaigns and strategies over different time frames and through various acquisition channels.
Time granularity = Month
Time range = Retention period
The dataset 'acquisition_adcost_retentionPeriod' is focused on acquisition metrics, ad costs, and user retention data, with a time granularity that includes annual and monthly breakdowns. The dimensions 'year' and 'yearMonth' indicate that the data can be analyzed both annually and on a monthly basis. Additional dimensions like 'sourceMedium' and 'campaign' provide insights into the performance of various marketing channels and specific campaigns. The metrics in this dataset cover a broad range of user engagement and financial performance indicators, including session data, user acquisition details, pageview statistics, session duration, bounce rates, goal completions, transactions, revenue from transactions, and advertising costs. This dataset is particularly valuable for analyzing the effectiveness and ROI of marketing campaigns and strategies over different time periods.
Time granularity = Month
Time range = Retention period
The dataset 'acquisition_landing_retentionPeriod' focuses on acquisition metrics with an emphasis on the retention period, showcasing user engagement and financial performance data. The dimensions included—'year', 'yearMonth', and 'landingPagePath'—indicate that the dataset provides temporal insights on a yearly and monthly basis, as well as analysis based on specific landing pages. This granularity allows for a detailed examination of how different landing pages perform over time. Metrics such as sessions, new users, pageviews, session duration, bounces, goal completions, transactions, and transaction revenue offer a comprehensive view of user interaction and financial success. This dataset is particularly useful for understanding the effectiveness of different landing pages in terms of attracting and retaining users, as well as their contribution to overall business goals and revenue generation.
Time granularity = Month
Time range = Retention period
The dataset 'acquisition_adwords_retentionPeriod' is designed to analyze AdWords acquisition performance over a specific retention period. This dataset includes dimensions that allow for a detailed breakdown of data by time (year and year-month), as well as AdWords-specific elements such as customer ID, source and medium of the traffic, campaign details, keywords, and ad content. The inclusion of time dimensions like 'year' and 'yearMonth' suggests the ability to drill down into performance data on both an annual and monthly basis.The metrics provided offer a comprehensive view of the effectiveness of AdWords campaigns. They include impressions, clicks, sessions, pageviews, session duration, bounces, goal completions, transactions, transaction revenue, and ad cost. This dataset is particularly valuable for assessing the ROI of AdWords campaigns, tracking user engagement, and understanding the overall impact of advertising efforts on website performance.
Time granularity = Month
Time range = Retention period
The dataset 'overview_ecommerce_allTime' offers an in-depth look into the ecommerce activities across all recorded time, emphasizing both the temporal distribution of data and financial performance metrics. With dimensions such as 'year' and 'yearMonth', the dataset provides a granular view that allows for detailed time series analysis, ranging from annual summaries to month-by-month breakdowns. The metrics included—sessions, transactions, transaction revenue, transactions per session, and revenue per transaction—offer a comprehensive overview of ecommerce efficiency, customer engagement, and financial outcomes. This dataset is crucial for evaluating the long-term success of ecommerce strategies, understanding seasonal trends, and identifying opportunities for growth and optimization.
Time granularity = Month
Time range = All time available on the property
The dataset 'conversion_ecommerce_retentionPeriod_smp11' is specifically designed to analyze e-commerce conversion and retention over time, with a particular focus on how different factors influence these metrics. The dimensions included in this dataset—year, yearMonth, sourceMedium, campaign, deviceCategory, and userType—allow for a granular analysis of user behavior and transaction patterns over specific periods (year and month) and across various segments. This level of detail supports in-depth analysis of how different marketing sources, campaigns, devices, and types of users (new vs. returning) contribute to overall sessions, transactions, and transaction revenue. This dataset is particularly valuable for understanding the effectiveness of marketing efforts, user engagement across devices, and purchasing behavior over time.
Time granularity = Month
Time range = Retention period
The dataset 'conversion_ecommerce_timing_retentionPeriod_smp11' delves into the intricate dynamics of e-commerce transactions, focusing on the timing and retention aspects over varying periods. This dataset is structured to capture both temporal dimensions—spanning years and specific months within those years—and transaction-related dimensions that measure the number of sessions leading up to a transaction and the days taken to reach a transaction. By including metrics such as the total number of transactions and the revenue generated from these transactions, it offers a detailed look into the effectiveness of conversion strategies and customer retention efforts. This dataset is particularly useful for analyzing how long it takes for user engagement to convert into financial outcomes, providing insights into customer journey efficiency and the impact of marketing strategies over time.
Time granularity = Month
Time range = Retention period
The dataset 'conversion_ecommerce_checkout_retentionPeriod' captures key data related to user sessions in the context of ecommerce checkout processes, with a specific focus on the retention period. The dimensions included in this dataset—'year', 'yearMonth', and 'shoppingStage'—allow for a detailed temporal analysis, enabling stakeholders to observe trends and patterns both annually and monthly. Additionally, the 'shoppingStage' dimension offers insights into the checkout process, potentially highlighting stages where users drop off or proceed. The singular metric, 'sessions', quantifies user interactions, providing a clear indicator of engagement and effectiveness of the ecommerce platform over time. This dataset is crucial for understanding how changes in the ecommerce checkout process impact user retention and session activity across different time periods.
Time granularity = Month
Time range = Retention period
The dataset 'conversion_ecommerce_products_retentionPeriod' offers detailed insights into product-level performance within an e-commerce context, with a focus on product retention over specific periods. The dimensions included—year, yearMonth, productSku, productName, and productCategoryHierarchy—allow for a granular analysis down to individual product performance over time, facilitating trend analysis and performance review at both macro (year, month) and micro (product-specific) levels. The metrics, including views, cart additions, checkouts, revenue, quantity, refunds, and refund amounts, provide a comprehensive look at product interaction from interest to purchase and potential post-purchase dissatisfaction. This dataset is particularly valuable for understanding product life cycles, consumer interest, and the effectiveness of product placement and promotion strategies over time.
Time granularity = Month
Time range = Retention period
The dataset 'conversion_goals_retentionPeriod' is designed to evaluate conversion goals over a specified retention period. This dataset uniquely combines temporal dimensions with source, campaign, and user-related dimensions to provide a granular view of conversion metrics. The time-related dimensions, such as 'year' and 'yearMonth', suggest an analysis capability ranging from annual to monthly granularity. Additional dimensions like 'sourceMedium', 'campaign', 'deviceCategory', and 'userType' offer insights into the effectiveness of various marketing channels, campaign performances, device usage patterns, and user behaviors. Metrics in this dataset focus on sessions and specific goals, tracking completions, values, and starts for two distinct goals. This rich combination of dimensions and metrics makes it a valuable tool for understanding the effectiveness of conversion strategies over time and across different segments.
Time granularity = Month
Time range = Retention period
Metrics = The number of metrics will depend on the number of Goal of the property (Max 20 Goals)
The dataset 'conversion_goals_goalCompletionLocation_allTime' is designed to provide an in-depth analysis of goal completions across various locations, without a specific end date, indicating a comprehensive accumulation of data over time. It integrates time granularity through dimensions such as 'year', 'yearMonth', and 'goalCompletionLocation', enabling a detailed temporal and spatial breakdown of goal achievements. This dataset focuses on tracking the performance of up to 20 distinct conversion goals (though not all sequential numbers are present), offering insights into the effectiveness of various user engagement and conversion strategies over an indefinite period. Such detailed temporal and location-based segmentation is invaluable for understanding the dynamics of user interactions and the success of targeted goals across different time frames and site locations.
Time granularity = Month
Time range = All time available on the property
Metrics = The number of metrics will depend on the number of Goal of the property (Max 20)
The dataset 'conversion_goals_sourceMedium_allTime' focuses on tracking conversion goal completions from various sources and mediums over an indefinite period, emphasizing the all-time performance. It incorporates dimensions that detail the timing of these conversions (by 'year' and 'yearMonth') and the 'sourceMedium' through which the conversions were achieved. This structure allows for an in-depth analysis of conversion trends over time and the effectiveness of different sources and mediums in achieving up to three specified conversion goals. The dataset is particularly valuable for marketing and analytics teams looking to evaluate and optimize their strategies for user acquisition and conversion across different channels and periods.
Time granularity = Month
Time range = All time available on the property
Metrics = The number of metrics will depend on the number of Goal of the property (Max 20)
The dataset 'conversion_goals_campaign_allTime' focuses on tracking the performance of conversion goals across different campaigns over an indefinite time period. This dataset incorporates dimensions that allow for the analysis of data by year, month (as 'yearMonth'), and specific campaigns, offering a nuanced view of performance over time and across different marketing efforts. The metrics included, such as 'goal1Completions' up to 'goa20Completions', specifically measure the success rates of various predefined goals, providing insights into the effectiveness of different campaigns in achieving their objectives. This dataset is particularly useful for evaluating long-term trends in conversion rates and optimizing campaign strategies accordingly.
Time granularity = Month
Time range = All time available on the property
Metrics = The number of metrics will depend on the number of Goal of the property (Max 20)
The 'conversion_goals_country_allTime' dataset offers a detailed analysis of goal completions segmented by country over an all-time period. It features both time-based dimensions (year and yearMonth) and geographical segmentation (country), enabling a granular analysis of conversion goals across different time frames and regions. This setup allows for the tracking of specific objectives, such as form submissions, product purchases, or any other actions defined as goals, within a global context. The inclusion of multiple goal completion metrics (goal1Completions through goal20Completions) suggests a comprehensive approach to measuring various conversion actions. This dataset is crucial for understanding the effectiveness of different strategies and initiatives on a country-by-country basis over an extended period.
Time granularity = Month
Time range = All time available on the property
Metrics = The number of metrics will depend on the number of Goal of the property (Max 20)
The 'events_retentionPeriod' dataset is tailored to analyze event-based data with a focus on retention over time. It features a set of dimensions and metrics that allow for a detailed examination of event interactions within a specified period. The dimensions include 'year', 'yearMonth', 'eventCategory', and 'eventAction', providing a granular view that facilitates analysis both on an annual and monthly basis. The inclusion of event categories and actions further enables the segmentation and detailed investigation of specific types of user interactions. Metrics such as 'totalEvents', 'uniqueEvents', and 'eventValue' offer quantitative insights into the frequency and value of these events. This dataset is particularly valuable for understanding how users engage with different event types over time and assessing the long-term impact of such interactions.
Time granularity = Month
Time range = Retention period
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