How to Create a Digital Marketing Dashboard in Looker

Building a great digital marketing dashboard in Looker means you can finally see all your crucial metrics in one place, helping you make smart, data-driven decisions instead of just guessing. This guide will walk you through the essential steps to create a powerful dashboard, from planning your metrics and connecting your data all the way to building your first charts.

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Start with a Plan: What Story Should Your Dashboard Tell?

Before you even open Looker, the most important step is to plan your dashboard. A dashboard without a clear goal is just a collection of random charts. To avoid this, ask yourself a few key questions:

1. What is the dashboard's primary goal?

You can't track everything, so you need to be specific. Are you trying to measure the success of a specific product launch, understand your overall lead generation funnel, or prove the ROI of your content marketing? This goal will guide every choice you make.

• Example Goal 1: Track the performance of our latest Facebook Ads campaign.
• Example Goal 2: Get a high-level overview of website traffic and user engagement.
• Example Goal 3: Monitor the sales pipeline from initial lead to closed deal.

2. Who is the audience?

A dashboard for your company's CEO should look very different from one for your Paid Ads specialist. The CEO needs a high-level summary of performance and ROI, while the specialist needs granular data on individual campaigns, ad sets, and creatives. Tailoring the dashboard to its audience ensures it will actually get used.

3. What are your Key Performance Indicators (KPIs)?

Once you know your goal and audience, you can define the specific KPIs you need to track. Here are some of the most common digital marketing KPIs to consider:

• Traffic & Engagement:
• Paid Advertising:
• Lead Generation & Sales:

Choose just a handful of the most important KPIs that directly relate to your goal. A crowded dashboard is an ineffective dashboard.

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Connecting Your Data Sources to Looker

This is where things can get a bit technical, but it's manageable once you understand the basic concept. Looker doesn't usually connect directly to your marketing apps like Google Analytics or HubSpot. Instead, it connects to a database where your data is stored.

For most marketing teams, this means you'll need to follow a two-step process:

1. Get your marketing data into a centralized database (a data warehouse).
2. Connect Looker to that database.

Step 1: Get Your Marketing Data into a Data Warehouse

Your marketing data is scattered across platforms like Google Analytics, Facebook Ads, Shopify, Salesforce, and a half-dozen others. To analyze it all together, you need to pull it into one central location. This central location is called a data warehouse. Some popular options include Google BigQuery, Snowflake, and Amazon Redshift.

To get data from your disparate sources into the data warehouse, teams typically use an ELT (Extract, Load, Transform) tool like Fivetran or Stitch. These services have pre-built connectors that automatically pull data from your marketing platforms and load it into your warehouse on a regular schedule.

Step 2: Connect Looker to Your Data Warehouse

Once your data is flowing into the warehouse, you can connect Looker to it. This happens in the Admin section of Looker:

• Navigate to Admin > Database > Connections.
• Click Add Connection.
• Fill in your database connection details (host, port, username, password, etc.). This information will come from your data warehouse provider (e.g., BigQuery).
• Test the connection to make sure it's working, then save it.

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Understanding LookML: The Engine Behind Looker

With your data connection established, the next layer is Looker’s modeling language, LookML. Think of LookML as the instruction manual that tells Looker what your data means and how it's all related. This is where you define your metrics - known as "dimensions" and "measures" - so your business users can build reports without writing any code themselves.

• Dimensions: These are the attributes or "group-by" fields in your data. Things like Date, Campaign Name, Country, or Traffic Source.
• Measures: These are the numbers you want to calculate or aggregate. Things like Total Spend, Number of Clicks, or Average Order Value.

Building a LookML model is usually a job for a data analyst or developer, as it requires a grasp of both your business logic and a bit of code. Here is a simplified LookML example for a Facebook Ads dataset:

view: facebook_ads {
  dimension: campaign_name {
    type: string
    sql: ${TABLE}.campaign_name ,,
  }

  dimension_group: date {
    type: time
    timeframes: [raw, time, date, week, month, quarter, year]
    sql: ${TABLE}.date ,,
  }

  measure: total_spend {
    type: sum
    sql: ${TABLE}.spend ,,
    value_format_name: usd
  }

  measure: total_clicks {
    type: sum
    sql: ${TABLE}.clicks ,,
  }
}

This code defines the dimensions for "campaign name" and "date" and creates aggregated measures for "total spend" and "total clicks." Once this model is set up, a non-technical marketer can easily explore the data just by clicking on these pre-defined fields.

Building Your First Marketing Visualizations

Now for the fun part! With the data connected and the LookML model built, you can start creating charts and graphs. In Looker, you build these individual report components from what's called an "Explore."

Step 1: Choose Your Data in an Explore

An "Explore" is simply a user-friendly starting point for querying a specific dataset. For example, your data team might create Explores named google_analytics_traffic or facebook_ads_performance.

Let's build a simple chart to show ROAS (Return On Ad Spend) by campaign from Facebook Ads:

1. Navigate to the appropriate Explore (e.g., facebook_ads_performance).
2. In the left-hand panel, you’ll see all the available dimensions and measures.
3. From Dimensions, select Campaign Name.
4. From Measures, select Total Spend and Total Conversions Value. You might also have a pre-built measure for ROAS, if not, you can create one with a table calculation.
5. Click Run.

Looker will immediately generate a data table with your results.

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Step 2: Choose a Visualization Type

Above the data table, you'll see a visualization pane with options for different chart types. A bar chart is perfect for comparing campaign performance:

1. Click on the Bar Chart icon.
2. Looker will automatically turn your table into a bar chart.
3. Use the Edit menu to customize your chart's appearance, like changing colors, labels, axis names, and more.

Step 3: Save Your Chart to the Dashboard

Once you're happy with your chart, you can save it. In the top-right corner, click the gear icon and select Save & Add to Dashboard. Give your visualization a name (a saved visualization is called a "Look" in Looker) and select the marketing dashboard you want to add it to. Repeat this process for all your key marketing KPIs!

Putting It All Together: Designing an Effective Dashboard

As you add Looks to your dashboard, think about the layout. A good dashboard presents information in a logical flow that tells a story. Here are a few final tips for designing an effective layout:

• Place Key Metrics at the Top: Put your most important summary numbers (e.g., total ad spend, new leads, overall ROAS) in large, clear visualization cards at the top for an at-a-glance view.
• Arrange and Group Logically: Group related charts together. For instance, put all your website traffic charts (sessions, bounce rate, etc.) in one section and your paid ad metrics in another.
• Add Interactive Filters: The real power of a Looker dashboard comes from its interactivity. Add filters at the top of your dashboard - especially a Date Range filter - so users can easily drill down into the data and answer their own questions without needing to create new reports.

Final Thoughts

Creating a digital marketing dashboard in Looker is a process that involves thoughtful planning, some technical data preparation, and a creative eye for visualization. While it's more involved than some simpler platforms, the result is a robust, interactive, and centralized hub for all your marketing intelligence and a single source of truth for your team.

The biggest hurdle for most teams is the need to pipe all their data into a warehouse and then build LookML models before they can even create a single chart. We created Graphed to solve this very problem. We connect directly to your marketing and sales tools - like Google Analytics, Facebook Ads, Shopify, and dozens more - letting you use natural language to build real-time dashboards in seconds, without any need for data engineers or complex setup.