How to Make a Time Series Plot in Looker with AI

A time series plot is one of the most effective ways to see how your business is performing, letting you spot trends, understand seasonality, and track progress toward your goals. This guide explains how to create a time series plot in Looker, starting with the classic method and then exploring how new AI-driven approaches are making the process much faster.

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What is a Time Series Plot (and Why Should You Care)?

In simple terms, a time series plot (or time series graph) is a line chart that shows data points connected in chronological order. It has two axes: the horizontal x-axis always represents time (e.g., days, weeks, months), and the vertical y-axis represents the metric you're measuring (e.g., revenue, website sessions, new user signups).

So, why is this type of chart so important for marketing and sales?

• It reveals trends. Are your sales steadily increasing over the last quarter? Is your website traffic slowly declining? A time series plot makes upward and downward trends immediately obvious.
• It highlights seasonality. You can see if your business has predictable busy and slow periods. For an e-commerce store, that might mean seeing a huge spike in sales around Black Friday every November. For a B2B SaaS company, you might notice dips during major holidays.
• It shows the impact of events. Did that big marketing campaign you launched on Tuesday cause a spike in user signups on Wednesday? A time series plot connects actions to outcomes, helping you measure the direct impact of your efforts.
• It helps with forecasting. By understanding historical patterns, you can make more educated guesses about future performance and set realistic goals for your team.

Essentially, it turns a spreadsheet full of dated numbers into a clear story about your business's performance over time.

How to Make a Time Series Plot in Looker: The Standard Method

Looker is a powerful BI tool, and creating a time series plot is a fundamental skill. Before you begin, you need to make sure your data is connected to Looker and that it contains at least one dimension with time-based data (like a date, timestamp, or week) and one measure you want to track (like a count of users or a sum of revenue).

Here’s the step-by-step process:

Step 1: Start from an Explore

Your journey begins in the "Explore" section. An Explore is a starting point for a query in Looker, designed by your data team to allow business users to safely query data without needing to know SQL. Navigate to the Explore that contains the data you want to analyze, such as "Sales Data" or "Website Analytics."

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Step 2: Select Your Time Dimension and Your Measure

On the left-hand side, you'll see a list of available fields (dimensions and measures). Dimensions are attributes of your data (like date, country, or product name), while measures are quantitative values (like total sales, user count, or pageviews).

• First, find your time dimension. This will likely be named something like "Order Date," "Created Date," or "Event Timestamp." Click on it to add it to your query.
• Next, find the measure you want to plot over time. This could be "Total Revenue," "Number of Sessions," or "Active Users." Click on it to add it as well.

You should now see both the date dimension and the measure selected in the "Data" pane.

Step 3: Filter Your Data (If Needed)

Your explore likely contains all your historical data, but you might only want to look at a specific period. This is where filters come in.

Click the "Filters" section. Select the date field you are using, and specify the time range you're interested in, such as "in the past 90 days," "in the past 12 months," or a custom date range. You can also filter by other dimensions, like "Country" or "Campaign," if you want a more granular view.

Step 4: Run the Query

With your dimension, measure, and filters selected, click the "Run" button in the upper right corner. Looker will now query your database and retrieve the data, displaying it in a table below a visualization placeholder.

Step 5: Choose and Customize Your Visualization

Looker will often default to a table, a bar chart, or even a line chart. If it’s not already a line chart, navigate to the "Visualization" tab and click the line chart icon.

This is where you bring your chart to life. The basic line chart will appear, but you can refine it using the settings panel (usually opened by clicking the gear icon).

• Plot Settings: You can change the line interpolation (linear, monotone, step) and manage series types if you have multiple measures.
• Series Settings: Add a splash of color! Customize the colors of your lines, change the labels for clarity, and choose how you want points to be displayed (or not at all).
• X-Axis & Y-Axis Settings: This is crucial for readability. Give your axes clear, descriptive names (e.g., "Date" for the x-axis and "Daily Revenue ($)" for the y-axis). You can also adjust the scale, gridlines, and value formatting. For example, you can format your y-axis as currency or a percentage.

Once you’re happy with the look, you can save your plot as a "Look" or add it to a Dashboard to monitor regularly.

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The Challenge with the Manual Approach

The process above is functional, but it comes with a built-in learning curve. For managers and marketers who aren't in Looker all day, it's easy to get lost. Which Explore do I use? What's the right dimension name? Is it created_at or submit_date?

This manual process can feel slow, especially when one question naturally leads to another. You build your revenue chart for the last 90 days, and you immediately wonder, "Okay, but how does this compare to revenue from our US traffic only?" Answering that follow-up question means going back into the settings, adding a new filter, and running the query again. Each new question requires more clicks, more settings panels, and more time staring at a loading screen.

This friction often discourages deeper exploration and keeps people from finding valuable insights that are hidden just one or two questions away.

Using AI and Natural Language to Create Reports

This is where AI is changing the game for business intelligence. Instead of clicking through menus and settings, you can now use plain English to tell your tool what you want to see. This approach, often called Natural Language Query (NLQ), removes the technical barrier between you and your data.

The core idea is simple: if you can ask a question, you should be able to get an answer - instantly, in the form of a chart. You don't have to learn a specific piece of software, the software learns to understand you.

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How AI turns requests into visualizations

While the traditional method involves knowing how to build the chart, the AI method focuses on what you want to see. For example, instead of clicking through six different steps, you could just type:

"Show me daily sales from Shopify for the last 30 days as a line chart"

An AI data analyst breaks down this request:

• Metric: "sales"
• Data Source: "Shopify"
• Time Granularity: "daily"
• Time Frame: "last 30 days"
• Visualization: "line chart"

It then instantly generates the precise time series plot you asked for, without you needing to manually select a single field or configure an axis label. If you have a follow-up question, you just ask it:

"Now compare that to the 30 days before"

The system understands the context and adds a second line to the same chart for comparison. This conversational flow turns data analysis from a tedious task into a quick conversation, allowing you to follow your curiosity and dig deeper into your data without friction.

Tips for Better Time Series Plots (No Matter How You Make Them)

Once your chart is created, the analysis begins. Here are a few things to look for:

• Zoom In and Out: Don't just stick to one timeframe. Look at your data daily, weekly, and monthly. A scary dip in daily sales might just be normal weekly variation when you zoom out to a monthly view.
• Add Annotations: If you know why a spike or dip occurred (e.g., "Summer sale started" or "Website was down"), add an annotation to the chart. This provides critical context for anyone else who might view the report.
• Use Comparison Lines: Comparing current performance to a previous period (like the same month last year) is one of the most powerful uses of a time series plot. It tells you if you're growing, stagnating, or declining.
• Look for Correlations: Plot two different metrics on the same graph. For example, plot marketing spending and new user signups. Do you see a relationship where signups increase when spending does? This can help you understand cause and effect.

Final Thoughts

Whether you build them manually in Looker or create them instantly with a prompt, time series plots are a marketer’s best friend. They provide the clearest view of your performance over time, helping you make smarter, data-driven decisions about where to focus your energy and budget.

At our core, we built Graphed to eliminate the friction between having a question and getting an answer. Instead of requiring you to learn a complex tool, we let you connect data sources like Google Analytics, Shopify, and Salesforce, and simply ask for what you need in plain English. Your time series plots - and entire dashboards - are ready in seconds, with live data that updates automatically, helping you move from insight to action faster than ever before.