Google Analytics 4's standard, aggregated reports provide a comprehensive understanding of the key metrics that are crucial to your business operations. These reports offer extensive flexibility, allowing you to manipulate data through plotting, sorting, and filtering various metric and dimension values to extract meaningful insights that can drive informed decision-making. However, when you need to delve deeper into your data analysis with more sophisticated queries that demand reports combining specific metrics, dimensions, and segments not readily available within GA4's standard reporting interface, the explorations feature becomes an invaluable tool in your analytics arsenal.

GA4's explorations, which can be accessed through the Explore panel in the platform, represents one of the most powerful analytical capabilities within the tool. However, it's important to note that this feature can become quite complex and potentially overwhelming, particularly for users who aren't well-versed in navigating its interface, creating and customizing reports, or utilizing its various sharing functionalities. Additionally, explorations come with certain limitations that could potentially lead to confusion or unexpected results if you're not aware of them beforehand. Understanding these constraints is crucial for effective utilization of the feature. Let's take a systematic approach to understanding explorations and discover how to create and share reports that can uncover valuable insights for both you and your team members, ultimately enhancing your data analysis capabilities.

Create Custom Reports Through to Explorations Interface

The Explorations feature can be easily accessed through the Explore panel, conveniently located in the left-hand navigation menu of GA4's interface. This placement ensures quick access to this powerful analytical tool whenever you need it. Upon accessing the Explorations section, you'll be presented with an array of report templates, along with a comprehensive list of any previously created explorations within your GA4 property. This organization helps you quickly find both new templates and existing reports you've created.

In this guide, we'll start with a blank Exploration to thoroughly demonstrate how to build a report from the ground up. While Google provides numerous pre-configured templates, both in the main selection and the template gallery, which come populated with specific dimensions, metrics, and sometimes segments, these can be valuable time-savers if you have a clear report objective in mind. However, based on our extensive experience, we've found that users typically need to modify these templates significantly to match their specific requirements anyway. Therefore, starting with a blank slate can often be equally efficient and helps avoid unnecessary clutter in your workspace. This approach also provides a better understanding of how to build reports from scratch.

To begin your exploration journey from scratch, simply select the Blank option from the available templates. Upon selection, you'll be transported to the comprehensive explorations interface, where you can start building your custom report.

Understand the Explorations Interface

To begin your exploration journey from scratch, simply select the Blank option from the available templates. Upon selection, you'll be transported to the comprehensive explorations interface, where you can start building your custom report.

Moving from left to right across the interface, you'll first encounter the Variables (1) panel. This essential component serves multiple purposes: it allows you to assign a meaningful name to your exploration, specify the date range for your data analysis, and incorporate the necessary segments, dimensions, and metric fields that will form the foundation of your reports. It's crucial to choose a descriptive and representative name for your exploration to facilitate easy reference in the future. Additionally, carefully consider and set your intended date range, keeping in mind that users with whom you share the report won't be able to modify this range - only the report owner has this capability.

It's important to understand that the fields you add in this panel aren't automatically incorporated into your reports. Instead, consider this panel as a curated inventory of potentially useful fields that you might need during your analysis. While it's beneficial to maintain an organized panel, you can use it as a testing ground to add various fields and experiment with different combinations to determine which ones best serve your analytical needs. For instance, you might want to include metrics with different scoping to observe how they impact the overall report structure and insights.

The Settings panel (2) serves as the central configuration hub where you can meticulously customize and fine-tune your reports to meet your specific analytical needs. At its core, this panel operates on a straightforward principle: the fields you've previously added in the Variables panel can be seamlessly integrated into various selection boxes through either manual addition or an intuitive drag-and-drop interface. This flexibility allows you to construct reports that perfectly align with your data analysis objectives. The panel offers comprehensive control over multiple aspects of your report, including advanced technique settings, visualization options, detailed row and column configurations, and sophisticated filtering capabilities. These controls collectively determine how your selected fields are presented and how your data is ultimately visualized, enabling you to create reports that are both informative and visually compelling.

The canvas (3), positioned on the right-hand side of the interface, functions as your real-time preview window, displaying your currently selected report with immediate responsiveness to any modifications made in the Settingspanel. By default, the canvas presents a single tab, providing a focused environment for configuring your initial report. However, the interface's versatility allows you to expand your analysis by adding additional tabs, each capable of housing separate reports within the same exploration. This multi-tab functionality enables you to leverage the fields available in the Variables panel across multiple reports, maximizing the utility of your selected variables. Furthermore, you have the flexibility to apply different techniques to each report, facilitating comprehensive data analysis through multiple analytical approaches within a single exploration.

Choose a Technique and Visualisation

Before diving into the detailed configuration of fields and report parameters, it's essential to establish the fundamental framework by selecting an appropriate technique for your analysis. The technique selection essentially determines the type of exploration report you'll be working with. Among the available options, the free form technique stands out as the most versatile and adaptable choice. This technique provides unparalleled flexibility, allowing you to incorporate fields according to your specific requirements, with their display being governed by your chosen visualization method.

The visualization selection, which is specifically applicable to the freeform technique, plays a crucial role in determining how your data is presented visually. Similar to the technique selection, changing the visualization automatically adjusts the Settings panel to accommodate the specific requirements of your chosen display method. Tables offer a structured approach to highlighting relationships between your selected metrics and dimensions, presenting data in an organized, easy-to-read format. Donut charts excel at illustrating part-to-whole relationships within your dataset, making complex proportional relationships immediately apparent. Line charts are particularly effective at demonstrating temporal trends, showing how your data evolves over time. Explore provides an extensive array of visualization options, each designed to accommodate specific analytical needs and use cases.

However, the free form technique isn't always the optimal choice for every analytical scenario. Explore offers several specialized techniques designed for specific types of analysis. The funnel exploration technique, for instance, is specifically engineered for analyzing user journeys and tracking the progression of users through defined action sequences. Configuring a funnel exploration follows a similar pattern to freeform setup, allowing you to select relevant dimensions and segments. The Settings panel provides specialized controls for adjusting funnel steps through an interface that mirrors segment configuration. This allows you to break down and segment your funnel using any dimension or segment available in your Variables panel, providing deep insights into user behavior patterns.

A particularly useful feature in funnel analysis is the make open funnel toggle switch in the Settings panel. This control allows you to alternate between closed and open funnel views. When this option is disabled, the funnel chart displays step counts exclusively for users who have completed all previous steps in sequence. Enabling the option expands the view to include both new funnel entries and continuing progressions, revealing users who entered the funnel at later stages without completing earlier steps. For analyzing event or page sequences, the path exploration technique proves invaluable. This specialized technique excels at examining the progression of events or pages, whether leading up to or following specific events or pages of interest.

Path explorations contain comprehensive node structures that detail the complete journey sequence, incorporating the specific contents of each analytical step, all of which are preselected for immediate analysis. These preselected elements include several crucial identifiers: the event name which precisely tracks specific user interactions, the page title and screen name combination that provides detailed context about where users are engaging, the page title and screen class pairing that offers technical and contextual information about the interface, and the page path and screen class which maps the complete user navigation journey while maintaining technical context.

The start over functionality, prominently positioned at the top of the report canvas, serves as a complete reset mechanism for your exploration, effectively clearing all current configurations and allowing you to build a fresh analysis framework. This reset enables you to meticulously configure your own starting point or ending point, strategically add nodes to map out specific journey paths, and precisely specify the content and parameters each node should contain. Similar to funnel explorations, path explorations offer robust breakdown capabilities through dimensions and support metric selection from your available metric list. However, it's important to note that these capabilities are somewhat constrained in path explorations due to inherent dimension and metric compatibility restrictions, which we'll examine in detail in the following sections.

Add Metrics and Dimensions

After establishing your preferred analytical technique and selecting an appropriate visualization method, the next crucial step involves populating your report with relevant metrics and dimensions to generate meaningful insights. The process of adding metrics and dimensions is streamlined through the intuitive + icons located within the Variablespanel. This interface provides access to a comprehensive selection of metrics and dimensions, complete with a powerful search functionality that enables quick location of specific fields. To enhance efficiency, the interface supports multi-select functionality, allowing you to choose multiple fields simultaneously before finalizing your selection with a single Confirm action, streamlining the report configuration process.

The potential combinations of metrics and dimensions are remarkably extensive, offering countless analytical possibilities that can be tailored to various business intelligence needs. However, it's crucial to understand that not all metric and dimension combinations are viable due to technical and logical constraints. The primary consideration here is scope compatibility – different metrics and dimensions operate at various levels of data granularity, which can create conflicts when combined inappropriately. To assist users in avoiding incompatible combinations, Google has implemented an intelligent system that automatically grays out fields that aren't compatible with your current selections, preventing potential analytical errors.

We strongly advocate for experimental approaches in field combination, as different arrangements can reveal unique perspectives and insights within your data that might not be immediately apparent. Each unique combination has the potential to illuminate different aspects of your user behavior and business performance. However, this experimentation should be guided by clear objectives and analytical goals. The sheer volume of available fields, combined with the complexities of scope compatibility, can be overwhelming without a clear direction. Therefore, we recommend developing a thorough understanding of your team's insight requirements and analytical objectives before embarking on detailed exploration creation.

Configure Segments and Filters

Segments, which are managed through the Variables panel, represent a powerful feature that enables you to segmentand analyze specific subsets of your users, sessions, or events based on precisely defined conditions. These segments can be applied comprehensively across entire reports, effectively filtering and focusing your data analysis on specific user groups or behaviors of interest.

When you click the + icon adjacent to Segments, you're presented with two primary options for segment creation. You can either create a custom segment, which provides complete control over condition definition and configuration settings, or select a reference segment. Reference segments offer a collection of pre-configured options, ranging from general-purpose segments to specialized segments designed for specific use cases such as e-commerce and shopping analysis.

To further assist in segment creation, Google provides a variety of templates that include pre-configured conditions, streamlining the setup process for common analytical scenarios. Additionally, the platform offers innovative predictive segments that leverage machine learning to identify users likely to perform specific actions within designated timeframes.

To configure a segment, begin by entering a descriptive segment name that clearly identifies its purpose, then navigate to the add new condition section to define your segmentation criteria. The condition builder interface provides extensive flexibility in creating complex logical relationships between multiple conditions. You can utilize both or and and operators to establish sophisticated condition combinations, allowing for precise audience definition. The oroperator enables you to include users who meet any of the specified conditions, while the and operator ensures users meet all defined criteria simultaneously. For more advanced segmentation needs, you can incorporate additional condition groups, which function as separate logical units. Furthermore, the sequence feature allows you to specify the chronological order in which conditions must be met, providing granular control over user qualification criteria. This sequential capability is particularly valuable when analyzing specific user journeys or behavior patterns across sessions, users, or events.

Filters, conveniently located at the bottom of the Settings panel, serve as powerful tools for refining your data analysis by enabling precise inclusion and exclusion of data points based on specific metric and dimension conditions. While filters can only be applied to metrics and dimensions that are available within the Variables panel, it's important to note that these elements don't necessarily need to be actively used within your current report visualization. This flexibility allows for sophisticated data filtering without cluttering your report's visual presentation. For instance, you can implement a filter to focus exclusively on organic search traffic by applying a channel grouping filter, even if the channel grouping dimension isn't directly displayed in your report's structure. This capability enables clean, focused analysis while maintaining comprehensive filtering capabilities behind the scenes.

When evaluating whether to implement a segment or a filter for your analysis, it's crucial to understand their respective strengths and use cases. Segments offer superior versatility and control in defining and restricting your dataset. They provide a comprehensive toolkit for creating complex condition combinations, incorporating sequential patterns, and implementing various advanced settings with minimal effort. This robust functionality allows for highly sophisticated data segmentation through a single, unified interface. In contrast, filters operate on a more straightforward principle, offering quick and direct data refinement capabilities. While you can achieve similar results by combining multiple filters, this approach often requires more manual configuration and can become unwieldy for complex analyses. However, filters excel in scenarios where you need to quickly apply simple data restrictions or make rapid adjustments to your analysis parameters. Their accessibility through the filters menu makes them particularly useful for on-the-fly data refinement during exploratory analysis.

Sharing and Exporting a Report

The collaboration features in GA4 Explorations facilitate seamless sharing of analytical insights across your organization. To share an Exploration with other users who have access to your GA4 property, simply click the share exploration button located in the top-right corner of the interface. This action makes your report visible to all authorized users within the explorations list, enabling collaborative analysis and insight sharing. While there are certain limitations to consider when sharing Explorations (which we'll discuss in detail below), the ability to distribute and collaborate on analytical reports among team members represents a significant advantage for organizations seeking to maintain consistent analysis approaches and share valuable insights.

For situations requiring external distribution or offline analysis, GA4 provides multiple export options. You can export your report data in various formats, including TSV, CSV, or directly to Google Sheets for further manipulation and analysis. Additionally, the PDF export option enables you to create polished, presentation-ready versions of your reports suitable for distribution to stakeholders.

Considerations and Limitations

While Explorations represent a powerful analytical tool within GA4, users should be aware of certain limitations and restrictions that may impact their analysis capabilities. These considerations become particularly relevant when working with complex queries or managing larger properties with substantial traffic volumes.

Data and Field Limitations

Google has implemented specific limits to manage system resources and ensure optimal performance. Each user is restricted to creating 200 individual explorations, while properties are limited to 500 shared explorations. Additional constraints apply to the number of dimensions, metrics, and segments that can be utilized simultaneously: each exploration can incorporate up to 20 dimensions and 20 metrics, while being limited to 10 segments and 10 filters at any given time.

It's important to recognize that GA4's standard reports and explorations serve distinct analytical purposes, resulting in differences in both granularity levels and available metrics and dimensions. Consequently, certain dimensions and metrics that are accessible in standard reports may not be available within explorations, and vice versa.

Date Ranges

Unlike GA4's aggregated reporting, the data accessibility within explorations is fundamentally constrained by the data retention settings configured for your GA4 property. These retention periods are specifically defined, with standard GA4 properties allowing data storage for up to 14 months, while GA4 360 properties extend this capability to 50 months. This temporal limitation directly impacts the historical depth and breadth of data available for analysis within your explorations, making it crucial to align your data retention settings with your analytical requirements and business objectives.

Sampling, Filtering and Processing Differences

When comparing GA4's aggregated reporting to explorations, several key technical distinctions emerge in data handling mechanisms. The filtering functionality operates on different principles, as do segments and comparisons, with notable variations in case sensitivity implementations and field support capabilities. User count calculations employ distinct methodologies, while behavioral modeling algorithms may process data differently. Additionally, varying processing times can lead to sampling effects or data discrepancies between standard reports and explorations. These technical nuances can result in slight variations in metrics and insights when comparing data across different reporting interfaces, making it essential to maintain consistency in your analytical approach.

Sharing and Collaboration

While GA4's sharing and collaboration features for Explorations offer valuable team analysis capabilities, several important limitations warrant consideration. A primary constraint is the absence of real-time collaborative functionality - when users make changes to shared Explorations, these modifications aren't instantly visible to other team members, potentially leading to conflicting or redundant edits. The platform's role-based permission system introduces additional complexities, as certain user-specific configurations, including custom filters and segments, may not function as expected across different user accounts. Furthermore, users assigned the Viewer role within a property face restricted editing capabilities - they cannot directly modify shared Explorations and must instead create separate copies to implement changes, which can impact workflow efficiency and version control.