What is a Metric in Looker Studio?

A metric in Looker Studio is any number you can measure - the counts, sums, averages, and ratios that quantify your business performance. Understanding how to use them is the first step toward building reports that actually tell a story. This article will show you exactly what metrics are, how they work with dimensions, and how to use them to visualize your data.

Metrics vs. Dimensions: The Foundation of Looker Studio

In every data tool, including Looker Studio, data is organized into two primary types: metrics and dimensions. Getting this distinction right is essential, as it dictates how you can build every chart, table, and report. Think of them as the "what" and the "how much" of your data.

What Is a Metric?

A metric is a quantitative measurement. It's a number that can be counted, added, averaged, or otherwise aggregated. Metrics answer questions like "how many?", "how much?", or "how long?". In the Looker Studio data panel, metrics are typically highlighted in green and represented with numbers (e.g., 123).

Common examples of metrics include:

• From Google Analytics: Users, Sessions, Pageviews, Bounce Rate, Session Duration, Conversions.
• From Google Ads: Clicks, Impressions, Cost, Conversions, Cost per Click (CPC).
• From E-commerce (like Shopify): Total Sales, Orders, Average Order Value, Quantity Sold.
• From CRM (like Salesforce): Number of Deals, Deal Value, Win Rate.

Metrics are the key figures you want to track to measure performance.

What Is a Dimension?

A dimension is a descriptive attribute or a category used to group and filter your data. It provides the context for your metrics. Dimensions answer questions like "who?", "what?", "where?", "when?", and "how?". They are the labels for your numbers. In Looker Studio, dimensions usually appear in blue.

Common examples of dimensions include:

• Date & Time: Date, Year, Month, Hour.
• Geography: Country, Region, City.
• User Behavior: Traffic Source, Medium, Campaign, Landing Page.
• User Attributes: Device Category, Browser, Operating System.
• Product/Sales: Product Name, Deal Stage, Sales Rep.

How They Work Together: A Simple Analogy

The relationship between metrics and dimensions is what brings your data to life. A metric on its own is just a number, a dimension gives it meaning.

Imagine a simple spreadsheet tracking daily sales:

Here, Date and Product are dimensions. They label and categorize the data. You can group your data by Date or by Product.

Sales is the metric. It's the number you want to analyze. You can sum the sales to see your total revenue for October 26th ($300) or check the total sales for the Espresso Machine ($750).

You can't "sum" the Product column, but you can use it to filter or break down the Sales metric. This is the core concept: you use dimensions to segment and describe your metrics.

Creating Your Own Metrics with Calculated Fields

Looker Studio comes packed with default metrics from your connected data sources. Google Analytics provides classics like Users and Sessions automatically. Often, however, the most valuable insights come from creating your own custom metrics tailored to your specific business goals. This is done using "calculated fields."

A calculated field lets you apply mathematical formulas, use logic, and manipulate your data to create entirely new metrics. This is how you calculate things like conversion rates, profitability, or efficiency ratios that your data source might not provide out of the box.

Example 1: Calculating Website Conversion Rate

Google Analytics gives you Sessions (how many visits) and Conversions (how many completed a goal), but what if you want to see the relationship between them - your overall conversion rate?

Here’s how to create that metric:

1. Select a chart on your report or navigate to your data source and click Add a Field.
2. Give your new metric a clear name, like "Overall Conversion Rate."
3. In the formula box, enter the calculation:

SUM(Conversions) / SUM(Sessions)

1. Under Type, go to Number > Percent to make sure your result displays correctly as a percentage.
2. Click Save. You now have a reusable "Overall Conversion Rate" metric you can drag into scorecards, tables, and time series charts.

Why SUM()? We use the SUM() function because you need to aggregate all conversions and all sessions before performing the division. This ensures the calculation happens on the total dataset for the chart, not on a row-by-row basis.

Example 2: Calculating Return On Ad Spend (ROAS)

If you're running ads, you need to know if you're making more money than you're spending. Return On Ad Spend (ROAS) is the classic metric for this, calculated by dividing revenue by cost.

Assuming your data source (like Google Ads) contains a Cost metric and a Conversion Value (or Revenue) metric, here's how you'd build ROAS:

1. Following the same process, create a new calculated field.
2. Name it "Return on Ad Spend (ROAS)."
3. Enter the formula:

SUM(Ad Conv. Value) / SUM(Cost)

1. Set the Type to Number > Percent or Number > Currency, depending on how you prefer to visualize it. Displaying it as a percentage is very common (for instance, 300% ROAS means you made $3 for every $1 spent).
2. Click Save. Now you can use this metric to see which campaigns, keywords, or ads are the most profitable.

Putting Metrics to Work in Your Report Visuals

Once you have your metrics, the next step is to visualize them effectively. The type of chart you choose depends entirely on what you want to communicate about your metric.

Choosing the Right Chart for a Metric

• Scorecards: Perfect for displaying a single, high-level metric or KPI. Use a scorecard when you want someone to know a key number at a glance, like Total Revenue this Month or Total Users Today. You can also add a comparison to a previous period to add context.
• Time Series Charts: The best choice for tracking how a metric changes over time. Your dimension will almost always be a date field (Date, Month, Week). Use it to answer questions like, "How did our website traffic (Sessions metric) trend over the last quarter?"
• Bar or Column Charts: Ideal for comparing a metric across different categories (dimensions). Use a bar chart to see your Total Sales (metric) by Product Name (dimension) or your Pageviews (metric) by Page Title (dimension).
• Tables: Tables are for when you need to show detailed, granular information with multiple dimensions and metrics side-by-side. For instance, a table could show Campaign Name, Source, Clicks, Cost, and Conversions all in one view.

Practical Tips for Working With Metrics

1. Give Your Metrics Clear Names. In your data source, the default name might be ga:users or c_installs. Change these to plain English like "All Users" or "App Installs." When you create calculated fields, be descriptive. "Conv Rate" is okay, but "New Customer Conversion Rate" is better. Your colleagues will thank you.
2. Always Add Context. A number by itself is rarely useful. "We had 5,000 visitors" could be amazing or terrible depending on your normal traffic. Use comparison date ranges ("vs. previous period") to show growth or decline. Combine metrics with dimensions to show where the performance is coming from (e.g., show traffic by channel).
3. Be Mindful of Aggregation. Aggregation is how Looker Studio summarizes your numbers (SUM, AVERAGE, COUNT, MIN, MAX). Looker Studio often guesses correctly, but you should always check it. Some common mistakes include summarizing Bounce Rate (it should almost always be averaged) or showing a table with Average Order Value as a SUM (which creates a meaningless number).

Final Thoughts

At their core, metrics are the countable figures that measure your business, while dimensions provide the all-important context that turns those numbers into meaningful insights. Mastering this relationship, and learning to create your own bespoke metrics with calculated fields, is how you move from building basic charts to creating truly powerful, interactive dashboards in Looker Studio.

Creating nuanced dashboards and digging into layers of your data in a tool like Looker Studio can take time. From understanding syntax to setting up data blends, there’s often a steep learning curve. At Graphed , we found ourselves spending more time fighting with tool configurations than actually analyzing results. That’s why we take a conversational approach, allowing you to connect your marketing and sales data, then simply ask for the dashboards and answers you need in plain English - no calculated field formulas necessary.