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How to Create a Product Management Dashboard in Looker

Cody Schneider•

A great product management dashboard does more than just display metrics, it tells a clear story about your users and how they interact with your product. Building one from scratch in a powerful tool like Looker can feel intimidating, but it comes down to a simple process: deciding what story you need to tell, and then choosing the right visuals to tell it. This guide will walk you through defining the most important product KPIs and then provide a step-by-step process for building your product dashboard in Looker.

Before You Build: Planning Your Product Dashboard

Jumping straight into Looker without a plan is like starting a road trip without a map. Before you write a single line of LookML or create a chart, you need to define the purpose of your dashboard. A clear plan ensures you build something that provides real value, not just a collection of vanity metrics.

1. Identify Your Audience

Who is this dashboard for? The metrics and layout that are valuable to a CEO are very different from what a UX designer or a marketing manager needs. Tailor your dashboard to its primary audience:

  • Executive Team: Needs a high-level overview. Focus on core business success metrics like Monthly Recurring Revenue (MRR), user growth, overall customer retention, and high-level product engagement rates.

  • Product Team: Needs granular, actionable data. Focus on feature adoption rates, user funnels for new features, DAU/MAU ratios, session duration, and retention cohorts.

  • Engineering Team: Needs performance and quality data. Focus on key metrics like application performance, error rates, page load times, and API uptime.

  • Marketing Team: Needs to understand user acquisition and activation. Focus on metrics like conversion rates by channel, user activation funnels, and the journey from first touch to becoming a power user.

For this tutorial, we will focus on building a dashboard for the product team, one that helps them understand user behavior and make informed decisions.

2. Define Key Product Management KPIs

Instead of tracking everything, focus on metrics that align with the core product user journey: Adoption, Engagement, Retention, and Task Success. This framework helps you create a dashboard that tells a coherent story.

Adoption & Activation Metrics

These metrics tell you how successful you are at getting new users to use your product and experience its core value.

  • User Sign-ups: Tracks the total number of new user registrations over time. It’s your top-of-funnel for product growth.

  • Activation Rate: The percentage of new users who complete a key “aha moment” action within a specific timeframe (e.g., inviting a teammate, creating their first project, publishing their first post). This is far more meaningful than just tracking sign-ups.

  • New Feature Adoption Rate: Measures what percentage of your active users have tried a new feature you recently launched.

Engagement Metrics

Engagement metrics show how often and how deeply users are interacting with your product.

  • DAU/MAU Ratio (Daily Active Users / Monthly Active Users): This is a powerful indicator of product "stickiness." It shows what percentage of your monthly users engage with the product on a daily basis. A ratio above 20% is generally considered good for SaaS products.

  • Session Duration & Frequency: How long do users spend in the product per session, and how many sessions does a typical user have per week or month? This helps you understand if users are finding continuous value.

  • Core Feature Usage: Tracks interactions with the most important features of your product. For example, if you have a project management tool, you'd want to track "tasks created" or "projects completed."

Retention Metrics

Acquiring a new user is expensive, keeping them is profitable. Retention metrics tell you if your product continues to deliver value over time.

  • User Retention Rate: The percentage of users who return to your product over a specific period. You’ll want to view this by cohort (e.g., of all the users who signed up in January, what percentage are still active in February, March, etc.?).

  • User Churn Rate: The opposite of retention. This is the percentage of users who stop using your product during a given period.

Step-by-Step: Creating Your Product Dashboard in Looker

Once you've defined your KPIs, it's time to build the dashboard. The process in Looker generally involves connecting your data, creating individual charts and reports (called "Looks"), and then arranging those Looks on a dashboard.

Step 1: Get Access to Your Data & Understand LookML

Looker sits on top of your existing database (like BigQuery, Snowflake, or Redshift). Your data team will need to connect Looker to the database where your product event data lives (often from tools like Segment, Mixpanel, or Amplitude). They will then use LookML to create a semantic layer.

Don’t let the term “LookML” scare you. Think of it as a central dictionary for your company's data. It defines all your dimensions (like User Sign-up Date, Customer Plan Type) and measures (like Count of Active Users, Total Revenue) in one place. This ensures that when one person on your team talks about "active users," they are pulling the exact same data as everyone else.

Before you build, explore the available dimensions and measures your data team has created. This is done in the "Explore" section of Looker.

Step 2: Create "Looks" for Each KPI

A "Look" is a single visualization - a chart, a graph, or a table - that answers a specific question. You will create one Look for each of our key product KPIs.

Let’s walk through creating a visualization for the DAU/MAU Ratio:

  1. Navigate to an Explore: Start in an Explore that contains your user activity data.

  2. Select Your Dimensions: From the field picker on the left, filter for and select a time-based dimension. For this, you would typically choose something like Activity Date and set it to a "Month" timeframe.

  3. Select Your Measures: You'll need two core measures: a distinct count of daily active users and a distinct count of monthly active users. Your LookML model might have these as "Daily Active Users" and "Monthly Active Users". Add both to your report.

  4. Create a Table Calculation: Now it’s time to calculate the ratio. Click the "Calculations" button. You can create a new field directly in the Explore using a simple formula. In the expression box, you'd type something like:

  5. Format the Calculation: Give your new calculation a clear name like "DAU/MAU Ratio" and set the format to "Percent."

  6. Choose Your Visualization: Click on the "Visualization" pane. For a trend over time, a Line Chart is a perfect choice. Configure the chart to display the months on the X-axis and your new DAU/MAU Ratio on the Y-axis.

  7. Save as a Look: Once your chart looks right, click the gear icon in the top right and select "Save as a Look." Give it a descriptive name like "Product KPI: DAU/MAU Ratio Trend" and save it.

Repeat this process for your other essential KPIs, like User Retention by Cohort (using a Table visualization), New Feature Adoption (Bar Chart), and an overview of Total Active Users (Single Value visualization).

Step 3: Arrange Your Looks on a Dashboard

Now that you have your ingredients (your Looks), it's time to assemble them into a dashboard.

  1. Go to the folder where you saved your Looks and click "New Dashboard."

  2. Give your dashboard a name, such as "Product Management Performance Dashboard."

  3. Click "Add Tile" and choose to add from an existing Look you just created. Find your saved Look (e.g., "Product KPI: DAU/MAU Ratio Trend") and add it to the dashboard.

  4. Continue adding all the Looks you created. Drag and drop the tiles to arrange them logically. A good practice is to place your most important, high-level metrics (like MAU and Active User Growth) at the top, with more detailed charts for engagement and retention below.

Step 4: Add Filters for Interactivity

A static dashboard is helpful, but an interactive one is empowering. Filters allow you and your team to slice and dice the data without needing to create new reports.

  • At the top of your dashboard, click "Filters" → "Add Filter." The most common and useful filter for any product dashboard is a Date Range filter. This allows anyone viewing the dashboard to change the time period from "Last 30 Days" to "Last 90 Days" or a custom range.

  • You will also want to link this filter to the date field within each tile on your dashboard. You might also add filters for User Segment, Device Type, or Customer Plan to allow for deeper analysis.

Dashboard Design Best Practices

Finally, apply some simple design principles to make your dashboard easy to understand at a glance.

  • Keep it Clean: Don't cram too many charts onto one dashboard. If it gets too cluttered, consider creating a second, more detailed dashboard for deep-dive analysis.

  • Go Left-to-Right, Top-to-Bottom: Place your most important summary KPIs in the top-left corner, as this is where a user's eye naturally falls first.

  • Use Consistent Colors: Use color to mean something. For example, always use blue for user count and green for revenue across all your charts.

  • Tell a Story: Organize your dashboard logically. You could group visuals by Adoption, Engagement, and Retention to guide the viewer through the entire user journey.

Final Thoughts

Building an effective product management dashboard in Looker is about two things: disciplined strategic planning upfront and methodical execution within the tool. By first defining your audience and KPIs centered around the user journey, you create a clear blueprint that makes the actual building process in Looker much simpler and more impactful.

While Looker is an incredibly powerful platform for enterprise data analysis, its reliance on custom LookML and its steep learning curve can be a barrier for teams who need answers quickly. At Graphed, we're focused on solving this by allowing anyone to build dashboards by simply describing what they need in plain English. Instead of learning a complex new tool, you connect your data sources and ask questions like, "Create a dashboard showing our DAU/MAU ratio and user retention broken down by sign-up month," and get a live, interactive dashboard built for you in seconds.