Mobile expense tracker with fast input and widgets

The main problem of any expense tracker is input speed. A user abandons the app if logging a transaction requires more than two taps. Everything else — analytics, limits, export — is secondary. Our team has 5+ years of experience in financial app development and has delivered over 50 mobile projects. We have developed an approach: replacing the system keyboard with a custom numpad reduces input time by 200–300 ms. With 50 entries per day, this saves 5–7 minutes per month. Users report saving up to $200 per month with consistent use.

Why input speed is the key metric

User retention directly depends on how quickly they can log an expense. We ensure that logging a transaction takes no more than two seconds. On iOS, we use a Home Screen widget via WidgetKit with AppIntent — it opens the input sheet without fully launching the app. A custom Shortcut allows voice input through Siri. Live Activity in Dynamic Island shows the daily balance without unlocking the phone. On Android, a Tile in Quick Settings via TileService: one swipe and tap, the app doesn't open fully. A floating widget via WindowManager is a controversial solution but offers maximum speed. We employ asynchronous data processing and lazy loading to maintain sub-second response times under concurrent user operations.

A custom numpad keyboard for the amount replaces the system keyboard, which adds 200–300 ms of delay. Auto-focus on the amount field, default category as 'last used', geolocation-based suggestion (coffee shop nearby → 'Food' category). The custom numpad is 3 times faster than the system keyboard.

Designing categories and budget limits

Categories with icons and colors, nesting at most one level (subcategories). Set budget limits per category — stored as a separate BudgetLimit entity with categoryId, amount, month. When 80% of the limit is exceeded, a local notification fires. Progress calculation — only when the screen is opened plus a background recalculation once per hour.

Analytics and export: key features

Charts: pie chart by category for a period, bar chart by day. On Flutter — fl_chart, on iOS natively — Swift Charts. We don't drag in heavy libraries for two simple charts. Support for CSV and PDF export. CSV is trivial. PDF via PDFKit (iOS) or PdfDocument (Android) — we render an expense table with totals. The user can select a period and category set before export.

How to ensure cross-device synchronization?

If sync is needed, we use Firebase (Realtime Database or Firestore) with merge logic. The main pitfall: conflicts when writing simultaneously from two devices. Strategy: last-write-wins by updatedAt timestamp for simple cases, or CRDT for complex ones. For most trackers, last-write-wins is sufficient because the probability of conflict is low.

During development, we follow App Store Review Guidelines 5.1.1 for data protection.

Deliverables

• Architecture documentation and data model
• Source code repository (Git)
• Configured CI/CD (GitHub Actions, Fastlane)
• Publication to App Store and Google Play
• Access to test builds (TestFlight, Firebase App Distribution)
• Training for the client's team (2–3 hour webinar)
• 30-day warranty on bug fixes after release

Process

1. Analytics and requirements gathering — clarify scenarios, platforms, need for sync, import from statements.
2. Design — data model, navigation prototypes, UX validation for quick input.
3. Implementation — backend (if needed), client side, widget integration, push notifications.
4. Testing — functional, load (up to 10,000 records), testing on real devices.
5. Deployment — store publication, monitoring setup (Crashlytics, Google Analytics).

Performance testing

We conduct load testing with 10,000 records in the database. Check that the expense list screen responds within 200 ms. For widgets and Tile, we test on devices with iOS 16+ and Android 12+.

Time estimates and cost

Cost is calculated individually. Contact us for an engineer consultation before starting work. Order the development of an expense tracker — get a prototype in 2 weeks.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.