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How to Create a Startup Dashboard in Looker

Cody Schneider•

Creating a startup dashboard in Looker gives you a powerful, centralized view of your business performance. Instead of guessing how your marketing campaigns, sales funnel, and product usage are trending, you get a real-time command center loaded with the metrics that matter most. This guide will walk you through planning your key metrics and then building a functional and insightful dashboard in Looker, step by step.

Why Your Startup Needs a Central Dashboard

In a startup, speed and focus are everything. A well-designed dashboard isn't just a collection of pretty charts, it's a strategic tool that aligns your entire team around the same goals and data. It replaces chaotic, time-consuming reporting - where you manually download CSVs from a half-dozen platforms - with a single source of truth that updates automatically.

A good dashboard helps you:

  • Make Faster Decisions: Spot trends, opportunities, and problems as they happen, not a week later when you finally get the report built.

  • Drive Team Alignment: When everyone from marketing to sales to product is looking at the same numbers, conversations become more productive and focused.

  • Measure What Matters: It forces you to define your Key Performance Indicators (KPIs) and track your progress toward tangible goals.

  • Answer Stakeholder Questions: Easily provide investors and advisors with clear, data-backed updates on the health of the business.

Planning Your Dashboard: Key Metrics for an Early-Stage Startup

Before you even open Looker, the most critical step is deciding what to measure. A cluttered dashboard is an ignored dashboard. Start simple and focus on the handful of metrics that truly reflect the health and growth of your business. Below are some foundational KPIs, but remember to tailor them to your specific business model and current stage of growth.

Marketing & Acquisition Metrics

These metrics tell you how effective you are at getting in front of the right audience and bringing them into your funnel.

  • Website Traffic by Source: Where are your visitors coming from (e.g., Organic Search, Paid Social, Direct)? This helps you double down on what works.

  • Customer Acquisition Cost (CAC): How much does it cost to acquire a new paying customer? Calculate this by dividing your total sales and marketing spend over a period by the number of new customers acquired in that period.

  • Conversion Rate (Visitor-to-Lead): What percentage of your website visitors convert into a lead (e.g., sign up for a newsletter, request a demo)?

  • Cost Per Lead (CPL): How much are you paying for each new lead? This is vital for managing your ad spend efficiently.

Sales & Revenue Metrics

For SaaS and e-commerce startups, these metrics track the money coming in the door.

  • Monthly Recurring Revenue (MRR) / Annual Recurring Revenue (ARR): The lifeblood of any subscription business. Track new MRR, expansion MRR, and churned MRR.

  • Sales Funnel Conversion Rates: Track the percentage of leads that move from one stage to the next (e.g., MQL to SQL, Demo to Closed-Won). This helps you pinpoint bottlenecks in your sales process.

  • Average Revenue Per User (ARPU): The average amount of revenue you generate from a single customer each month or year.

  • Churn Rate: The percentage of customers who cancel their subscriptions in a given period. Keeping this low is crucial for sustainable growth.

  • Customer Lifetime Value (LTV): The total revenue you expect to generate from a single customer over their entire relationship with you. A healthy business needs an LTV that is significantly higher than its CAC (a common benchmark is an LTV:CAC ratio of 3:1 or higher).

Product & Engagement Metrics

Are people actually using and loving your product? These metrics tell the story.

  • Daily/Monthly Active Users (DAU/MAU): A classic indicator of product stickiness. The DAU/MAU ratio helps you understand how frequent engagement is.

  • User Retention Rate: What percentage of users who sign up in a given week or month are still active one, seven, or thirty days later? This is often visualized in a cohort analysis chart.

  • Key Feature Adoption Rate: What percentage of your active users are engaging with the core, value-driving features of your product?

Getting Your Data Ready for Looker

This is where Looker differs significantly from simpler dashboard tools. Looker does not directly connect to apps like Salesforce or Google Analytics. Instead, it sits on top of a data warehouse (like Google BigQuery, Snowflake, or Amazon Redshift) and uses a powerful modeling language called LookML to define your business logic.

For a non-technical startup team, this part can be the main hurdle. Here’s a simplified overview of the required setup:

  1. Consolidate Your Data: You first need to get all your data from sources like Stripe, HubSpot, Google Analytics, and your product database into a central data warehouse. This is typically done using an ETL (Extract, Transform, Load) tool like Fivetran or Stitch.

  2. Connect Looker to Your Warehouse: Once your data is in the warehouse, you connect Looker to that database.

  3. Build a LookML Model: A developer or data analyst on your team will write LookML code. This code defines your data relationships, dimensions (attributes like 'User Sign Up Date' or 'Campaign Name'), and measures (calculations like 'Total Revenue' or 'Average Session Duration'). This is Looker's "secret sauce" - it creates a reliable and reusable model so everyone is querying data the same way.

If you don't have a data engineer, this setup can be complex. However, once the model is built, building dashboards becomes much easier for the rest of the team.

Step-by-Step: Building Your Dashboard in Looker

Once your LookML model is set up, you and your team can move on to the fun part: creating the visuals and assembling your dashboard.

Step 1: Start with an Explore

An "Explore" is the starting point for any visualization in Looker. It's a user-friendly interface that lets you query the data defined in your LookML model. Your data analyst will have set up Explores for different business areas, like "Users," "Orders," or "Website Sessions." Navigate to the Explore section and choose the one that contains the data you want to visualize.

Step 2: Create a Visualization (a "Look")

Inside the Explore interface, you’ll see all the available dimensions and measures in a panel on the left. Let’s say you want to build a line chart showing daily active users over the last 30 days.

  • Select Your Fields: From the left panel, you’d select the 'Sign Up Date' dimension and the 'User Count' measure.

  • Apply a Filter: Use the filter bar at the top to set the 'Sign Up Date' to "is in the past 30 days."

  • Run the Query: Click the "Run" button. Looker will show you your data in a table.

  • Choose a Visualization: Above the data table, click on the visualization tab. Looker offers many chart types - bar, column, line, scatter-plot, map, etc. Select "Line Chart."

You’ve just created your first tile, which Looker calls a "Look." Customize it with labels and colors, then click the gear icon in the top right and select "Save to Dashboard."

Step 3: Assemble Your Dashboard

A pop-up will appear prompting you to choose an existing dashboard or create a new one. Give your new startup dashboard a name like "Company Health Dashboard | [Q2 2024]" and save your Look there.

Your dashboard is now a blank canvas where you can add your visualizations. Repeat Step 2 for each of your key metrics. Arrange the tiles on the dashboard by dragging and dropping them into place. A good practice is to put your most important, high-level KPIs (like MRR or Active Users) in large, single-value number charts at the very top for at-a-glance visibility.

Step 4: Add Interactive Dashboard Filters

A static dashboard is useful, but an interactive one is even better. Looker allows you to add filters that apply to all the tiles on your dashboard at once.

  • From your dashboard in Edit Mode, click "Filters" in the top menu and select "Add Filter."

  • The most common and useful filter is a date range. Create a "Date" filter and configure it to apply to the relevant date fields in each of your looks.

Now, any team member can visit the dashboard and instantly filter the entire view to show performance for "last week," "last quarter," or any custom date range without having to edit individual charts.

Best Practices for a Dashboard That Gets Used

  • Start with Questions: Design each chart to answer a specific business question, like "Which marketing channel is driving the most conversions?" not just to display data.

  • Prioritize Ruthlessly: Limit your dashboard to 8-12 key visualizations. If you try to show everything, you'll communicate nothing.

  • Tell a Story: A great dashboard flows logically. Start with high-level outcome metrics (like revenue) at the top, then drill down into the performance drivers (like traffic and conversion rates) below.

  • Make it Actionable: When looking at a chart, the next step should be obvious. If sales are down, a supporting chart might show which sales rep is underperforming or which stage of the funnel has a bottleneck.

Final Thoughts

Building a Looker dashboard for your startup is an investment of time, especially during the initial data warehousing and LookML modeling phase. However, the payoff is immense: a single source of truth that empowers your entire team to make smarter, faster decisions and stay aligned on the metrics that truly drive growth.

We know that for many startups, the process of setting up a data warehouse and learning LookML can be a significant roadblock. That's why we created Graphed. Our platform connects directly to your data sources like Google Analytics, HubSpot, and Shopify in just a few clicks. You can then use simple, natural language to ask questions and build the exact real-time dashboards you need in seconds - no data engineers, no complex setup, and no SQL or LookML required.