How to Create a Mobile App Dashboard in Google Sheets with AI
Tracking your mobile app’s performance doesn’t require expensive, complex business intelligence tools. You can build a powerful, custom dashboard right inside Google Sheets, giving you a centralized view of your app's health. This guide will walk you through exactly how to define your core metrics, pull in the data, and use built-in features and AI to create a dashboard that turns raw data into clear, actionable insights.
Choose Your Core App Metrics
Before you build anything, you need to decide what to measure. A cluttered dashboard is an ignored dashboard. Focus on a handful of key performance indicators (KPIs) that align with your app’s goals. Group them into categories to tell a complete story about your user journey.
User Acquisition Metrics
These metrics tell you how people are discovering and installing your app.
Installs & Downloads: The total number of times your app has been downloaded from an app store. You'll want to track this over time to see trends.
Traffic Source & Medium: Where are your users coming from? (e.g., organic search, paid ads on Facebook, referrals). This helps you understand which marketing channels are most effective.
Cost Per Install (CPI): If you're running paid campaigns, this is essential. Calculate it by dividing your total ad spend by the number of installs from that campaign.
User Engagement Metrics
Once users install your app, what do they do? Engagement metrics reveal how valuable they find it.
Daily Active Users (DAU) & Monthly Active Users (MAU): The number of unique users who open your app on a given day or within a 30-day period.
The DAU/MAU Ratio (Stickiness): An excellent indicator of how consistently users return. Divide your DAU by your MAU. A ratio above 20% is generally good, and 50%+ is world-class.
Session Length: The average amount of time a user spends in your app during a single session. This helps you understand if users are deeply engaged or just popping in and out.
Key Event Conversions: Track actions inside your app that signal value, like completing onboarding, playing a level, or using a core feature.
Retention & Churn Metrics
Getting users is one thing, keeping them is another. Retention is a key predictor of long-term success.
Retention Rate: What percentage of users come back to your app after Day 1, Day 7, and Day 30? This shows you if your app has staying power.
Churn Rate: The percentage of users who stop using your app over a given period. It's the inverse of retention and highlights potential friction points that drive users away.
Monetization Metrics
If your app generates revenue, these metrics are your North Star.
Average Revenue Per User (ARPU): The total revenue divided by the number of users. It gives you a high-level view of your app's revenue-generating efficiency.
Lifetime Value (LTV): A prediction of the total revenue a single user will generate throughout their entire time using your app. Aim for an LTV that's at least 3x your CPI.
Conversion Rate: The percentage of users who make an in-app purchase, subscribe, or complete another monetization event.
Gathering and Syncing Your App Data to Google Sheets
After you’ve defined your metrics, you need to get the data into Google Sheets. You have a few options, ranging from manual and tedious to automated and efficient.
Option 1: The Manual Approach (Export/Import CSVs)
This is the most straightforward but time-consuming method. You can manually export reports as CSV files from your various platforms:
App Stores: Google Play Console and Apple's App Store Connect for download and revenue data.
Analytics Tools: Firebase or Google Analytics 4 for engagement and event data.
Attribution Platforms: AppsFlyer, Adjust, or Branch for channel performance.
Ad Networks: Meta Ads and Google Ads for campaign spend and CPI.
While this works for a one-off report, it's not sustainable for an ongoing dashboard. You'll spend half your week just downloading and cleaning files.
Option 2: The Automated Approach (Connectors)
This is where you save hours of work. Use a third-party connector to automatically pull data from your app platforms directly into Google Sheets on a schedule you set.
Google Sheets Add-ons: Tools like Supermetrics, Coefficient, or Power My Analytics connect directly to the APIs of common platforms (like Firebase, Facebook Ads, etc.) and pipe the data into your sheet. It's a low-code way to automate the most painful part of the process.
Zapier or Make.com: These no-code automation platforms can trigger actions in Google Sheets based on events in your other apps. For example, you could create a "workflow" that adds a new row to a Google Sheet every time a new in-app purchase is logged in Stripe.
Google Apps Script: For those with development skills, you can write custom scripts to fetch data from any service with an API. This offers the most flexibility but requires coding knowledge.
Build Your Mobile App Dashboard in 5 Steps
With your data flowing into Google Sheets, it's time to build the dashboard itself. The key is to keep your raw data separate from your visualizations.
Step 1: Structure Your Spreadsheet
Create separate tabs for each data source (e.g., "App Store Data," "Firebase Raw," "Ad Spend"). This keeps your workspace clean. Then, create one primary tab called "Dashboard." This is where all your charts and summary tables will live.
Step 2: Create Summary Tables with Formulas
On a "calculation" tab or within your dashboard tab, create small tables that aggregate your raw data into the KPIs you need. These tables will power your charts. Use formulas to do the heavy lifting:
SUMIFS & COUNTIFS: Perfect for adding up numbers based on specific criteria. For example, you can count installs from a certain campaign.
QUERY: This is Google Sheets' most powerful function. It lets you use SQL-like commands to select, filter, and organize your data. For example, you can pull total revenue by country from your sales data.
VLOOKUP or XLOOKUP: Use these to combine data from different tabs. For example, you could combine campaign cost data from your ad spend sheet with install data from your analytics sheet to calculate CPI for each campaign.
Step 3: Visualize Your Data with Charts
Now, turn your summary tables into visuals. Select your summary data, go to Insert > Chart, and choose a visualization that tells the clearest story.
Line Charts: Ideal for tracking metrics over time (e.g., DAU, Retention Rate).
Bar/Column Charts: Great for comparing categories (e.g., Installs by Source, Revenue by Country).
Scorecard Charts: Use these single-number displays for your most critical KPIs (e.g., Total Installs This Month, Current ARPU).
Pie Charts: Use sparingly, but they can be effective for showing the composition of a total (e.g., user breakdown by device type).
Arrange your charts logically on the "Dashboard" tab. Group related metrics together so you can get a quick overview of acquisition, engagement, and monetization at a glance.
Using AI Features in Google Sheets to Speed Up Analysis
This is where you can truly accelerate your workflow and uncover insights you might otherwise miss. Google Sheets has AI features that make data analysis more accessible, even if you’re not a formula whiz.
The "Explore" Button (Built-in AI)
Google’s Explore feature automates the initial steps of data analysis.
Select your raw data table.
Click the Explore icon in the bottom-right corner (it looks like a firework or sparkle).
A pane will open with AI-generated suggestions, including formatted tables, charts summarizing trends, and answers to natural language questions. You can ask it things like "monthly average session length" or "top traffic sources."
This is a fantastic way to quickly test hypotheses or create a starting set of visualizations for your dashboard without writing a single formula.
AI-Powered Add-ons
The marketplace has numerous add-ons that bring the power of large language models like GPT-4 directly into your spreadsheet cells.
Generate Formulas with Words: Instead of struggling with syntax, you can install an AI add-on and simply type a prompt like: "Write me a formula that calculates the DAU/MAU ratio using the data in cells C2 and D2." The add-on will generate the correct formula for you.
Summarize Data Instantly: Highlight a table of user feedback or survey responses and ask an AI tool to "summarize the main themes and sentiment." This turns tedious qualitative analysis into a seconds-long task.
Clean and Format Data: Use prompts to standardize messy data. You could ask it to "extract all the ISO country codes from the user location in Column B" or "remove all duplicate rows based on user ID."
These AI tools act like a data analyst sitting next to you. They remove the technical barrier, letting you focus on the questions you want to ask your data rather than the technical steps required to get the answers.
Final Thoughts
Building a mobile app dashboard in Google Sheets provides a free, flexible, and surprisingly powerful way to consolidate your most important metrics. By starting with clear KPIs, automating your data flows, and leveraging AI tools to speed up formula writing and analysis, you can create a dynamic command center for your app's growth.
We built Graphed to take this process one step further. When you outgrow the manual setup of spreadsheet connectors and charts, our tool lets your entire team talk to your data. Just connect your app analytics sources like Google Analytics, your ad accounts, and transaction data, then ask questions in plain English like, "create a dashboard showing installs and CPI by campaign for the last 30 days." Graphed instantly builds live, interactive dashboards, so you can spend your time finding insights, not wrestling with formulas.