Hello! In our last lesson, we categorized the raw materials of analytics: the automatically collected, recommended, and custom events that capture member actions. You now have a solid understanding of the data you can collect.
Today, we'll learn how to use those raw materials to get answers. The goal of this lesson is to translate a general question about your community's growth into a precise specification that Google Analytics can understand. This is a crucial skill that bridges the gap between your strategic goals and the data on your screen. By the end of this session, you'll be able to break down any community-growth question into four key components: a metric, a dimension, a segment, and a time-range.
The Anatomy of an Analytics Question
Imagine you're managing a community and you ask: "Are new members from the US more engaged on mobile than on desktop?"
This is a great strategic question, but you can't just type it into Google Analytics. To get an answer, you need to translate it into the language of analytics. This involves breaking the question down into four essential parts:
- Metric: What, specifically, are you measuring? (e.g., number of active users, engagement rate)
- Dimension: How are you breaking down that measurement? (e.g., by device, by country)
- Segment: What specific subset of your data are you looking at? (e.g., only new members, only users from the US)
- Time-Range: Over what period are you analyzing? (e.g., the last 30 days)
Let's look at these four components one by one.
Metrics and Dimensions: The 'What' and the 'How'
At the heart of any report are two things: what you're counting (metrics) and how you're grouping those counts (dimensions).
- Metrics are the numbers. They are the quantitative measurements of user behavior. Examples include
Total Users,Sessions,Engaged sessions, and theEvent countfor a specific action likepost_topic. - Dimensions are the attributes of your data. They are the non-numerical labels you use to slice, dice, and categorize your metrics. They answer the "by what?" part of your question. Examples include
Country,Device category, andSession source / medium.
The MeasureSchool channel provides a fantastic visual explanation of this distinction within the GA4 Exploration interface. Pay close attention to how they describe dimensions as "the aspect of your business you want to analyze" and metrics as what "help us measure."
How to Use GA4 Explorations | Free-Form Exploration (Lesson 5)
Watch this segment to see the building blocks of a GA4 report explained.
Focus on the description of Dimensions and Metrics starting from this point. Notice how they are color-coded in the interface (green for dimensions, blue for metrics), which is a helpful mental shortcut.
So, in our question "Are new members from the US more engaged on mobile than on desktop?", our primary metric might be Engagement Rate, and we'd need to break it down by the Device Category dimension.
Segments and Comparisons: The 'Who'
Your question isn't about all users; it's about a specific group: "new members from the US." To isolate this group, you need to apply a filter or create a segment. In GA4, there are several ways to slice your data, each with a different purpose. The most common ones you'll encounter are Comparisons, Segments, and Audiences.
For now, let's focus on Comparisons, which are a straightforward way to analyze subsets of your data side-by-side in GA4's standard reports. They are perfect for answering questions like "mobile vs. desktop."
This article from Analytics Mania gives a great introduction to what Comparisons are and why you'd use them.
Google Analytics 4 Comparisons - How to use them? - Analytics Mania
This article will show you how to use Comparisons to quickly analyze different data subsets.
Read the introduction, What are comparisons, to understand their purpose. Then, review the section on creating comparisons to see how you select a dimension (like Device category) and its values (like mobile and desktop) to build your comparison.
As you just read, Comparisons are a powerful tool for quick analysis within standard reports. For more complex analysis in the "Explore" section of GA4 (which we'll cover later), you'll use Segments. To help you understand the difference, the following resource provides a handy comparison table.
This guide contains a clear table distinguishing the different ways to subset your data in GA4.
Focus on the table titled Comparisons vs. Segments vs. Audiences. For this course, we are most interested in Comparisons (for quick analysis in standard reports) and Segments (for deeper analysis in Explorations). Don't worry too much about Audiences for now; their primary use is for advertising.
So, to analyze "new members from the US," you would create a comparison (or segment) that includes two conditions:
- User attribute
Country= "United States" - User attribute
New / established= "new"
Time-Range: The 'When'
The final piece of the puzzle is the time-range. Are you interested in the last week, the last month, or the entire year? Specifying the date range is fundamental. Often, you'll also want to compare this period to a previous one to understand trends. For example, you might compare this month's performance to last month's, or to the same month last year.
The official Google Analytics channel has a short video that demonstrates how to set a date range and a comparison period in a standard report.
3.1 Answering Business Questions with Standard Reports - Analytics Academy on Skillshop
This video shows how to manipulate the date range in a standard GA4 report.
Watch the section from 00:40 to 00:48 to see how a comparison period is added. This simple action allows you to see percentage changes over time directly in your reports.
From Question to Specification: A Framework
Let's put it all together. When you have a question, you can use this framework to translate it.
| Question Component | Analytics Specification | GA4 Element | Example for "Where are our most active new members coming from?" |
|---|---|---|---|
| The "what" | What are you counting? | Metric | Active users |
| The "how" | How do you want to break it down? | Dimension | First user source / medium |
| The "who" | Which specific group are you analyzing? | Segment/Comparison | New / established is "new" |
| The "when" | What period are you interested in? | Time Range | Last 30 days |
By systematically breaking your question down into these four components, you create a precise recipe for GA4 to follow. You're no longer just asking a vague question; you're giving the system a specific set of instructions:
"Show me the count of Active users (Metric) broken down by First user source / medium (Dimension) for the Last 30 days (Time-Range), but only include users where New / established is "new" (Segment)."
This structured approach is the key to moving from curiosity to concrete insights.
Conclusion
In this lesson, we've established a critical process for data analysis. You've learned how to deconstruct any business or community-growth question into a clear, actionable analytics query.
Let's summarize the key takeaways:
- Every analytics query is composed of four parts: metrics (what you count), dimensions (how you group the counts), segments (who you're analyzing), and a time-range (when).
- Comparisons are a simple way to analyze subsets of data (like mobile vs. desktop) in standard GA4 reports.
- Segments are used for more advanced analysis in GA4's Exploration reports.
- Translating your questions into this four-part structure is the first and most important step in getting meaningful answers from your data.
In our next lesson, we will formalize this process by learning how to define a community Key Performance Indicator (KPI), complete with a formula, its decision-making purpose, and a cadence for reviewing it.