Guide to Mobile Analytics: Benefits and Best Practices (2025)

Mobile analytics is the go-to tool that helps you see how people find, use, and return to your app. With these data points in hand, you can remove friction from the user experience, ship features that better connect with users, and grow revenue without guessing. 

In this guide, you’ll get clear definitions, the key metrics you should be tracking, and how to set up event tracking the right way. We’ll touch on important privacy must-knows for 2025 and provide a step-by-step plan to launch a reliable analytics practice. 

Plus, we’ll also discuss some of the common pitfalls and how to dodge them, and close with how to run the whole loop in Domo.

What is mobile analytics?

Mobile analytics is the practice of measuring how people use your app. It tracks what actions they take (“events”), how often they come back (“retention”), and whether the experience performs well (like crashes, slow screens, or battery drain). You’ll often pair these usage signals with marketing and attribution data to understand which campaigns attract the best users. 

On iOS and Android, privacy frameworks shape what you can track and how you measure ad performance; we’ll call out the big ones—Apple’s App Tracking Transparency (ATT) and SKAdNetwork, plus Android’s Privacy Sandbox Attribution Reporting—to help you stay compliant as you grow.

Benefits you can expect

Before we get into the details, let’s align on what you can achieve. When done well, mobile analytics delivers:

• Faster product decisions. Clear data helps show you where use stalls, so you can fix the right screens first.
• Higher retention. You’ll see which behaviors predict stickiness (like finishing onboarding or turning on notifications) and guide more people to those moments.
• Better performance. Crash-free sessions, cold-start time, and key metrics (“core vitals”) correlate with app ratings and store discoverability, especially on Android.
• Smarter spend. Attribution shows which channels bring in active users, not just those who install an app. On iOS, that means leaning on SKAdNetwork; on Android, the Privacy Sandbox’s Attribution Reporting API is maturing. 

The quick glossary (so the rest makes sense)

• Event: An action taken by the user (like opening an app, viewing a product, or starting the checkout process).
• Parameter: Details attached to an event, like product_id or price. In Google Analytics for Firebase, using recommended events and parameters helps you get richer reporting. 
• Session: A focused period of user activity; the definitions can vary by tool.
• DAU/WAU/MAU: Daily, weekly, or monthly active users. Use them to understand reach and engagement.
• Retention cohort: Users grouped by “start date” or first action and tracked over a period of time (e.g., day 1, day 7, day 30).
• Attribution: Assigning credit for app installs and user actions, with a focus on privacy for modern iOS and Android devices (more below).

Core metrics that matter (and what “good” looks like)

Acquisition & activation

Start by checking whether new users make it past “hello.” Two simple handoff points will tell you a lot: How many installs become first-time opens, and how many first-time opens lead to a key action (like creating an account, finishing a tutorial, or adding an item). 

If many people install an app but never open it, you need to improve your store page or the post-install experience. If users open the app but don’t complete the key action, then onboarding is the bottleneck. 

To get a quick sense of overall stickiness, use the DAU/MAU or WAU/MAU ratio, which shows how many monthly users return daily or weekly. New apps often begin with a low ratio but tend to climb as onboarding gets cleaner and the “next step” becomes more obvious on the home screen.

Engagement & retention

Once people activate, the question becomes: Do they come back? And how soon do they see value each time? Track time to first value (the first moment the app proves its worth for a new user) and watch cohorts for day-1, day-7, and day-30 retention. 

In a healthy pattern, those curves drop at first and then flatten, meaning a stable core sticks around. If the lines keep sliding toward zero, you’re likely masking friction (slow screens, confusing navigation) or missing a clear reason to return (no reminders, no fresh content).

Monetization

If your app sells, measure whether engaged users convert and keep spending in sensible ways. Follow the journey from trial to paid subscriber or from first purchase to repeat purchase, then look at frequency and average order value for a complete picture. 

Over time, combine these into a simple lifetime value view so you can compare cohorts and see whether changes in onboarding or performance actually translate into revenue, not just nicer charts.

Quality & performance

Technical quality shapes every metric above. Keep an eye on crash-free sessions and users, ANR (“app not responding”) on Android, and cold-start times. If these slide, funnels and retention will slide with them. 

Performance also has a second-order effect. On Android, core vitals influence Play Store visibility; on iOS, users reward smooth apps with better ratings. Treat these as leading indicators alongside activation and retention, not as separate “engineering-only” numbers.

How to use this process 

Read the loop from left to right: acquisition → activation → engagement → monetization, with quality supporting all of it. When something dips, move one step back to find the cause. Improve the earliest weak link you can change quickly, then come back to see if cohorts flatten and revenue follows. This iterative process is how these metrics work together instead of competing for attention.

Privacy & attribution in 2025

Mobile privacy has real teeth now, so plan with it and not around it.

• iOS ATT: If your app shares data with other companies for cross-app tracking, you must ask the user for permission using Apple’s AppTrackingTransparency framework and respect their choice. 
• iOS SKAdNetwork (SKAN): Apple’s privacy-preserving install and campaign measurement. It limits user-level data but lets you see campaign outcomes with delayed, aggregated signals. SKAN 4 (iOS 16.1+) brought multiple postbacks and coarse conversion values for longer windows. 
• Android Privacy Sandbox (Attribution Reporting): An API that measures ads without cross-party IDs (i.e., less reliance on GAID), sending aggregated, privacy-safe reports. Expect continued evolution through 2025. 
• Store telemetry: Android vitals surfaces performance metrics that influence user experience and ranking; watch them like a KPI.

Bottom line: You’ll still measure what works; you’ll just do it with more aggregation, modeling, and careful event design.

Instrumentation: How to set yourself up right

Before you add SDKs, agree on three things: What to measure, how to name it, and what “good enough” means for quality.

1. Define a short event taxonomy. Start with 12 to 20 events that map to core tasks (like open, sign_up, view_item, add_to_cart, start_checkout, or purchase). Use recommended events or parameters where supported to get better reports. 
2. Add parameters that answer “which/why.” For example, view_item with item_id, category, price.
3. Set user properties sparingly. Keep to durable traits (e.g., plan_tier) and avoid PII.
4. Version your schema. Changing event names later is expensive. Add new events now; deprecate old ones with a sunset date.
5. QA before release. Use debug modes and test devices; validate events and parameters in pre-release builds and staging environments.

Reading the charts: Funnels, paths, and cohorts

Once your events are in place, treat the three views as your core toolkit and use each with a purpose. Start with a funnel to understand where momentum dies. Read it from left to right and ask: Which step combines a big drop in users but has a lot of traffic? That’s where you should focus on improving first. 

When you want to see how people actually move—not just how you wish they’d move—open a path-style view. Compare the routes of users who convert with those who don’t, paying special attention to the two steps before success; that’s where small tweaks often pay off. 

Finally, check cohorts to see whether your changes stick. Group users by the week they first opened the app and compare their day-7 and day-30 retention; if cohorts after a change hold on to more users, you’ve earned a win you can trust.

Best practices for onboarding

Onboarding is where first impressions form and habits begin. Keep choices light on the first run, guide people directly to the action that proves your value, and explain only what helps them take that next step. A clear path to “first value” matters more than a perfect tour. 

Next, instrument each moment so you can see exactly where people stall. When you make a change like shorter sign-up, clearer copy, or fewer fields, go back to the funnel and cohort views. If the largest drop shrinks and later cohorts retain better, you’re moving in the right direction.

Technical quality is part of analytics

Crashes, freezes, and slow screens aren’t just engineering issues; they’re analytics issues because they shape behavior. Track crash-free sessions and users, watch for “app not responding” events on Android, and keep an eye on cold-start time and any lag. 

If technical quality dips, your funnel will look worse, not because the design changed, but because the experience did. Treat performance metrics as leading indicators and review them alongside activation and retention every week.

Measuring campaigns without breaking privacy

Marketing still needs a scoreboard, even as platforms tighten privacy. On iOS, that means designing around SKAdNetwork’s aggregated, delayed signals and choosing conversion values that reflect real progress, like onboarding completed or first purchase. 

On Android, build with Privacy Sandbox Attribution Reporting in mind and combine it with in-app behavior to tell a fuller story. The point isn’t to chase user-level trails; it’s to connect campaigns to meaningful outcomes in your app so product and marketing work from the same reality.

A simple data model you won’t outgrow

Keep your pipeline clean by separating concerns. Land raw events exactly as the SDKs emit them so you can audit any oddities. Transform into a clean layer with consistent names and types that analysts and dashboards rely on. 

From there, publish a curated layer with the tables people actually use like daily actives, retention by cohort, or funnel step summaries. If you explore predictions later, add a feature layer and document it. This structure makes change safer, allowing you to evolve the raw and clean layers without breaking the curated views teams depend on.

A/B testing that respects your users

When you test a change, keep it honest and simple. 

• Write a one-sentence hypothesis that ties a visible change to a single metric you care about (“If we shorten sign-up, day-7 retention will rise by two points”). 
• Guard the experience with a quality metric; no win is worth a spike in crashes. 
• Run long enough for a fair read, decide, and document the outcome in a few lines the team can skim later. 
• Then confirm with cohorts that the lift shows up beyond the first week.

A one-week starter plan that actually ships

Give yourself five days to build momentum. 

• Day one, choose a single flow—onboarding or checkout—and write the three questions you need answered. 
• Day two, finalize a short event schema that maps to those questions and agree on names. 
• Day three, instrument and validate on test devices until every event appears as expected. 
• Day four, build one funnel, one cohort view, and a small quality panel that tracks crash-free sessions and cold-start time. 
• Day five, ship one change aimed at the biggest drop you found and set an alert on your primary metric so you’ll know if it’s moving. 

End the week with a short note about what you changed, what moved, and what you’ll try next. That rhythm is how analytics becomes practice, not a project.

Avoiding the traps that slow teams down

Most problems are predictable. Event taxonomies that sprawl make analysis slow and brittle, so keep your schema short and versioned. Chasing installs without watching activation leads to hollow wins; always measure the path to “first value.”

Ignoring quality creates dips you’ll misread as product issues. Keep performance on the dashboard next to your funnels. Don’t rely on one-time heroics; if you can’t replay your transformations and tests, reliability won’t scale when you grow.

Choosing tools in 2025

Pick tools that make the basics easy. Recommended event templates will save you time and unlock richer reports. Privacy-aware attribution on both platforms keeps you compliant while still giving marketing a fair read. Built-in performance panels help you watch crashes, stalls, and cold starts without wiring extra dashboards. Most importantly, make sure you can export your raw and processed data into your warehouse so product, marketing, and analytics can all work from the same source of truth.

Proving ROI in plain language

You don’t need a complex model to show value. If a cleaner onboarding flow nudges day-30 retention from 12 to 14 percent on a 100,000-install cohort, that’s 2,000 more active users at day 30. If an average active user contributes three dollars in margin per month, you’ve created six thousand dollars of monthly value from a single cohort—and the effect compounds as new cohorts roll in. 

If smoothing the checkout path lifts completion by one point on fifty thousand monthly starts at a forty-dollar average order value, that’s twenty thousand dollars in additional revenue each month. When quality work drops crash rate materially, watch the ripple in ratings, retention, and support tickets. 

Write each win the same way: what changed, how much it moved, and what you’ll try next.

Bringing it together in Domo

You can easily run this entire loop in Domo without a long setup. Connect your app data and a few key marketing sources, and you’re ready to bring your events into Domo. Using Magic ETL and DataFlows, you can standardize your event schema and publish clean, reusable tables that everyone can rely on. 

You can build a mobile performance page that puts your funnel, your cohort view, and your quality panel side by side so product and engineering teams can make decisions from the same page. Set alerts on the metrics that matter like activation rates, purchase completions, and crash-free sessions. You can also share updates easily through campaigns or app-style pages so fixes turn into repeatable workflows. 

Start small with about a dozen core events, one funnel, one cohort, and a single change aimed at the biggest drop. In a week, you’ll have a clear starting point and proof that the practice works.