How to Do Trend Analysis in Looker

Spotting trends in your data is less about predicting the future and more about understanding the present so you can make smarter decisions tomorrow. While Looker is an incredibly powerful platform for centralizing your business data, getting it to reveal those trends can sometimes feel like you need a secret decoder ring. This guide will walk you through the most practical methods for conducting trend analysis in Looker, from simple line charts to more advanced period-over-period calculations.

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What is Trend Analysis, Really?

Before jumping into Looker, let's quickly align on what we mean by "trend analysis." In a nutshell, it’s the process of looking at historical data points over a specific period of time to identify patterns, momentum, and shifts in direction. It helps you answer critical business questions like:

• Are our sales growing, declining, or flatlining?
• Which marketing channels are becoming more effective over time?
• Is customer churn increasing quarter-over-quarter?
• Are there seasonal peaks or dips in website traffic we should plan for?

Looker is a great environment for this because it connects directly to your "single source of truth" - your data warehouse. This means everyone is looking at the same, up-to-date information, which is essential for accurate analysis.

Method 1: The Go-To Method - Time Series Line Charts

The most straightforward way to spot a trend is to visualize it. The simple line chart is your best friend here. It’s perfect for plotting a single metric over time to get an immediate visual sense of its trajectory.

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How to Build It:

1. Start in an Explore: Navigate to the relevant Explore in Looker that contains the data you want to analyze (e.g., an Orders or Web Analytics Explore).
2. Select a Time Dimension: In the field picker on the left, find your time-based dimension. This is often something like "Order Date," "Created Date," or "Event Timestamp." Click on it and select the appropriate timeframe to group by - Day, Week, Month, or Quarter. For high-level trends, starting with the Month is usually a safe bet.
3. Select a Metric: Next, choose the measure you want to track over time. This could be "Total Sales," "Count of Users," "Average Order Value," or any other key performance indicator (KPI).
4. Run the Query: Click the "Run" button to pull the data. You'll see a data table with your time periods in one column and your metric values in another.
5. Visualize It: In the Visualization pane, select the "Line" chart option. Looker will automatically plot your time dimension on the x-axis and your measure on the y-axis. Instantly, you can see if the line is trending up, down, or staying relatively flat.

Pro Tip: Take advantage of Looker’s dimension_group in your LookML model. A well-defined dimension_group for dates will automatically provide all the common timeframes (date, week, month, year) right in the field-picker, making it effortless to zoom in or out of your trend analysis.

Method 2: Smoothing Out the Noise with Moving Averages

Daily or weekly data can often be "noisy," with lots of spikes and dips that make it hard to see the underlying trend. A sudden one-day sales spike can be distracting. This is where a moving average comes in handy - it helps smooth out short-term fluctuations to reveal the longer-term direction.

How to Add a Moving Average Trend Line:

1. Follow the steps above to create your basic line chart.
2. In the Visualization pane, click the Edit gear icon to open the settings.
3. Navigate to the Y tab (for the Y-axis).
4. Scroll down and find the Trend Line section.
5. Toggle it on and select Moving Average from the "Type" dropdown menu.
6. Set the Period: This is the key setting. It determines how many data points are included in each average calculation.

You’ll now see a second, smoother line overlaid on your original chart. This line represents the moving average and often provides a much clearer picture of your business momentum, free from the noise of daily volatility.

Method 3: Getting Specific with Period-over-Period Growth Calculations

Sometimes, a visual trend isn't enough. You need the hard numbers: "What was our month-over-month revenue growth in March?" or "How did week-over-week user signups change?" To get these precise figures, you’ll turn to one of Looker's most powerful features: Table Calculations.

Table Calculations work a lot like formulas in Excel or Google Sheets, but they operate directly on the data returned by your query. Let’s build a Month-over-Month (MoM) Growth calculation.

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How to Calculate MoM Growth:

1. Start with a data table, not a visualization. Set up your Explore with a time dimension (e.g., Order Month) and a metric (e.g., Total Sales). Run the query.
2. In the Data section, on the far right of the green headers, click the + Add calculation button.
3. A pop-up will appear. Give your calculation a clear name, like "MoM Sales Growth."
4. In the formula box, you'll use Looker's functions to compare the value of one row to the value of the previous row. The most common function for this is offset().
5. Enter the following formula:

((${orders.total_sale_price} - offset(${orders.total_sale_price}, -1)) / offset(${orders.total_sale_price}, -1))

Let's break that down:

• ${orders.total_sale_price} refers to the sales value in the current row. Replace this with the actual name of your measure field.
• offset(${orders.total_sale_price}, -1) grabs the sales value from the previous row (one row back).
• The formula simply calculates: (Current Month - Previous Month) / Previous Month.

1. Change the Format dropdown to "Percent (2)" to display the result as a clean percentage.
2. Click Save.

A new column will appear in your data table showing the precise percentage growth (or decline) for each month. Now you can visualize this new "MoM Sales Growth" column using a bar or line chart to see the trend of your growth rate itself.

Best Practices for Meaningful Trend Analysis

Creating the chart is only half the battle. To extract real insights, keep these best practices in mind:

1. Choose the Right Timeframe

Analyzing daily traffic for a one-year period will likely result in a chaotic, unreadable chart. Conversely, looking at yearly revenue for a three-year-old business won’t give you enough data points. Match your time aggregation to the scope of your question. Use months or quarters for strategic overviews and days or weeks for tactical performance checks.

2. Segment Your Data for Deeper Insights

An overall upward trend in sales is great, but what’s driving it? Before you celebrate, add a dimension to pivot your data. Segmenting your trends by product category, marketing channel, geographic region, or customer persona can reveal the true story. You may find that one channel is driving all the growth while another is in decline - an insight that’s completely hidden in the aggregate view.

3. Compare Related Metrics

A trend in one metric becomes much more powerful when viewed in the context of another. Instead of just plotting "New Users," plot it alongside "Marketing Spend." This helps you understand efficiency and correlation. Are you spending more to acquire the same number of users? Or is your user growth outpacing your spending? Analyzing trends in pairs tells a much richer story.

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4. Annotate and Explain the "Why"

Your trend chart shows a huge spike in traffic in May. Why? Was it a successful marketing campaign? A viral blog post? A new product launch? Use Looker dashboards to add text tiles or notes that add context to an event. An annotated chart that explains the "why" behind the numbers transforms a simple data visualization into a valuable piece of business intelligence.

Final Thoughts

Performing trend analysis in Looker, whether through line charts, moving averages, or detailed table calculations, is a vital skill for anyone looking to make data-driven decisions. By moving beyond simple numbers and looking at the momentum over time, you can better understand your business performance, anticipate future needs, and identify opportunities long before they become obvious.

While Looker provides the building blocks, putting them all together can be manual and requires an understanding of its interface and functions. At Graphed, we've focused on automating that whole process. Instead of clicking through menus and handwriting formulas, we let you connect your data and then simply ask for what you need in plain English - like, "Show me my Shopify sales trend by month for the last year" or "Create a dashboard with month-over-month growth for my Facebook Ads campaigns." We turn hours of report building into a 30-second conversation, so you can focus on the insights, not the setup.