How to Create a Mobile App Dashboard in Looker with AI

Building a successful mobile app is one thing, but understanding how people actually use it is a completely different challenge. A well-designed dashboard is your command center for tracking performance, measuring growth, and making smarter decisions, and a powerful tool like Looker can help you build it. This article will walk you through the essential metrics for any mobile app dashboard and show you how to streamline the building process using AI.

Why You Need a Dedicated Mobile App Dashboard

Jumping between different analytics platforms to check user numbers, then over to another tool for crash reports, and a third for revenue data is inefficient. A centralized dashboard brings all your critical data into a single view, allowing you to see the relationships between different metrics at a glance. Instead of just seeing raw numbers, you get a narrative of your app's performance.

This unified view helps you:

• Make Data-Driven Decisions: Stop guessing what users want. See exactly which features are popular and where users are getting stuck so you can prioritize updates and improvements effectively.

• Understand User Behavior: Track the entire user journey, from the ad that brought them in to their first session, to the actions they take within the app.

• Measure Marketing ROI: Connect acquisition sources to in-app behavior. Did that new ad campaign bring in high-value users, or just a flood of quick uninstalls? Your dashboard will tell you.

• Monitor App Health: Keep an eye on technical performance metrics like crash rates and load times to ensure a smooth user experience.

The Most Important Metrics for Your Mobile App Dashboard

A great dashboard homes in on the metrics that matter most. While every app is different, most performance indicators fall into four main categories. Here are the key performance indicators (KPIs) you should consider adding to your Looker dashboard.

1. User Engagement Metrics

These metrics tell you how often and how deeply people are interacting with your app. High engagement is a strong indicator of a healthy, valuable product.

• Daily Active Users (DAU) & Monthly Active Users (MAU): These are baseline metrics showing how many unique users open your app within a day or a month. Tracking DAU and MAU over time shows your overall user base growth.

• DAU/MAU Ratio (Stickiness): This percentage is your app's "stickiness factor." It shows what proportion of your monthly users engage with the app on a daily basis. A ratio of 20% or more is generally considered good, though this varies by industry.

• Session Length: This measures the average amount of time a user spends in your app per session. A longer session length often suggests users find your app engaging and useful.

• Screen Flow & Funnel Analysis: This isn't a single metric, but a visualization that shows the paths users take through your app. It’s perfect for identifying where users drop off, such as during an onboarding process or a checkout flow.

2. Acquisition Metrics

Acquisition metrics track how you are attracting new users. They help you understand which marketing channels are working and how much it costs to acquire a new user.

• Downloads/Installs by Channel: Track how many new installs come from different sources like the App Store (organic), Facebook Ads (paid social), Google search results (organic), or referrals. This is critical for optimizing your marketing spend.

• Cost Per Install (CPI): Calculate the average cost to acquire one new user from a specific paid campaign. By comparing CPI across channels, you can allocate your budget to the most efficient ones.

• App Store Conversion Rate: This is the percentage of people who install your app after visiting its page in the App Store or Google Play Store. A low conversion rate might indicate your app description, screenshots, or reviews need improvement.

3. Performance Metrics

Even the most feature-rich app will fail if it’s slow, buggy, or constantly crashes. Performance metrics help you monitor the technical health of your app.

• Crash Rate: This measures how often the app crashes per session or per user. A high crash rate is a major red flag that leads to frustrated users and negative reviews. You should monitor this by app version, device type, and operating system.

• App Load Time: How long does it take for your app to become interactive after a user taps the icon? Slow load times are a major cause of user churn.

• API Latency: If your app relies on data from a server, this tracks how long it takes for the app to receive that data. Slow API responses can make your app feel sluggish and unresponsive.

4. Revenue Metrics

For most apps, the ultimate goal is to generate revenue. These metrics track your app's financial performance.

• Average Revenue Per User (ARPU): This is the total revenue generated divided by the number of active users over a specific period. It gives you a clear picture of the value each user brings to your business.

• Customer Lifetime Value (CLV): This metric predicts the total revenue a business can expect from a single customer account throughout their time using the app. It's a powerful metric for making long-term strategic decisions.

• Churn Rate: The percentage of users who stop using your app (or cancel their subscription) over a given period. Reducing churn is one of the most effective ways to grow revenue.

• Conversion Rate to Paid: If your app uses a freemium model or subscription, this measures the percentage of free users who convert into paying customers.

Building Your Dashboard in Looker: A Traditional Approach

Looker is a robust business intelligence platform that specializes in letting businesses define their own data models once, then allowing anyone to explore that data. Creating a dashboard typically involves a few key stages.

Step 1: Connect & Model Your Data

Before you can visualize anything, Looker needs access to your data. This data might live in Google Analytics 4 (via Firebase), BigQuery, a PostgreSQL database, or another data-warehouse solution. Looker's real strength lies in LookML, its modeling language. Your data team will use LookML to define dimensions (like 'User Country' or 'Acquisition Channel') and measures (like 'Total Installs' or 'Average Session Length'). This modeling layer ensures everyone in the company is using the same definitions for key metrics, creating a single source of truth.

Step 2: Build Your Visualizations (Looks)

Once your data is modeled, you can start building individual charts and tables in Looker’s “Explore” interface. These individual visualizations are called “Looks.” You’ll select dimensions and measures, choose a chart type (like a line chart to show DAU over time), and apply any necessary filters. For a complete mobile dashboard, you’ll repeat this process for each of the key metrics you identified earlier.

Step 3: Assemble Your Dashboard

With your individual Looks created, the final step is to assemble them into a cohesive dashboard. You can drag and drop your charts, resize them, and arrange them in a logical flow. The idea is to create a story - perhaps starting with high-level acquisition metrics at the top, followed by deeper engagement and retention visualizations below.

Enter AI: The Faster Way to Build Dashboards

The traditional BI process works, but it can be slow and requires specialized knowledge. Learning LookML takes time, and even once it's set up, building visually effective dashboards requires a lot of clicking, dragging, and thinking about chart configurations. This is where AI-driven analytics is changing the game.

Instead of acting as a manual "chart builder," next-generation tools function like a data analyst you can talk to. The process is completely different:

1. Connect Your Sources: Just like with Looker, you start by connecting your app analytics tools, ad platforms, and databases.

2. Ask Questions in Plain English: This is the revolutionary step. Instead of navigating menus to build a report, you simply type what you want to see. For example:

   • "Show me daily active users for the last 30 days as a line chart."

   • "What was our cost per install on Facebook vs. Google in the last quarter? Show it as a bar chart."

   • "Create a funnel showing user progression from app install to completing the onboarding."

The AI understands your request, queries the connected data sources, and generates the visualization instantly. There’s no need to manually select dimensions, measures, or chart types - you just describe the final report you want.

This conversational approach radically reduces the time it takes to get from a question to an insight. You don't have to become proficient in a complex BI tool, if you can ask a question, you can analyze your data. It empowers everyone on your team, from marketers to product managers, to get answers themselves without waiting on a data analyst to build a new report. As one chart reveals an interesting trend, you can immediately ask a follow-up question to dig deeper, creating a seamless and rapid data exploration process.

Final Thoughts

A comprehensive mobile app dashboard is essential for navigating the competitive app landscape. By focusing on the right metrics across engagement, acquisition, performance, and revenue, you can gain a clear, holistic view of your app's health and discover actionable opportunities for growth, moving beyond educated guesses to truly data-informed strategies.

We built Graphed to remove the friction from this process entirely. Instead of spending hours learning tools and manually building reports, our platform allows you to connect all your data sources — like Google Analytics, Shopify, Facebook Ads, and Salesforce — in one place. From there, you just use simple, natural language to ask questions and instantly create live, real-time dashboards. Answering "which marketing channels drive the users with the highest lifetime value?" takes 30 seconds, not half a day wrestling with tables and charts.