Budget app development: envelopes, forecasts, goals

Development of a Mobile Budget Planning App

Let's note: when a user opens a budgeting app, they expect more than just a log of "what I spent yesterday." They need to understand: "Can I afford an apartment in 3 years?" "Will I exceed my limit before payday?" This changes the architecture: instead of simple transaction tracking, you need a budgeting model with forecasts, goals, and rules. We specialize in such projects — over the years, we've launched 8 financial apps, averaging a 4.7 store rating. The architecture must support a flexible accounting model, forecasting, and cross-device sync.

Which budgeting methodology to choose?

Three main schemes, each with its own data model:

Envelope budgeting. Each category is an "envelope" with a monthly allocation. Spend, and the envelope shrinks. Overspend can be covered from another envelope. YNAB works this way. Technically: an Envelope entity with allocated, spent, available. A transaction decreases available in the envelope, not just recorded in history. More about the method on Wikipedia.

Zero-based budgeting. Every dollar of income must be assigned to a category. income - sum(allocations) = 0. Requires active allocation at month start — suits disciplined users but may deter casual ones.

Percentage-based (50/30/20). Automatic split: 50% needs, 30% wants, 20% savings. Implemented as a rules engine on top of transactions with auto-categorization. Less user effort — higher retention.

It's crucial to support multiple methodologies or clearly define one from the start — reworking the data model can nullify a month of work. In our experience, the envelope method yields 30% more active users due to its visual clarity — it's the best start for audience retention.

Forecasting and savings goals

"Save 150,000 for a vacation by August" is a SavingsGoal with targetAmount, targetDate, currentAmount. When income is added, the system suggests directing part to the goal. A progress bar with a date: if the contribution pace is insufficient, a warning shows the required monthly amount.

Expense forecasting based on history — a simple 3-month moving average with seasonal adjustment (December is always anomalous). Implemented client-side without ML — just SQL aggregation with GROUP BY month. For accuracy, we use a weighted average with a 0.6 coefficient for the last month.

Recurring transactions

Recurring payments (subscriptions, rent, loans) — RecurringTransaction with fields amount, frequency (RRULE or enum), nextDueDate, categoryId. A background task checks daily if nextDueDate <= today and creates transactions automatically. On iOS — BGProcessingTask, on Android — WorkManager with PeriodicWorkRequest(1, TimeUnit.DAYS). More on WorkManager in the official documentation.

Pitfall: daylight saving time. If a recurring transaction should be created on "the 1st of every month," you can't store just a second interval — you need a Calendar API with locale awareness. An error here leads to duplicates or skips, undermining trust.

Comparison of platforms for implementing recurring transactions

What's included in our work (deliverables)

• Accounting model design — methodology selection, ER diagrams, edge cases (28-day months, year transition, zero income).
• Design and prototyping — Figma with loading, error, and empty states.
• Development — Swift/SwiftUI (iOS), Kotlin/Jetpack Compose (Android), Flutter/Dart (cross-platform).
• Integrations — App Store Connect, Google Play Console, TestFlight, Firebase, analytics (Amplitude/Mixpanel).
• Testing — unit tests (XCTest, JUnit), UI tests (XCUITest, Espresso), load testing for sync.
• Documentation — architecture diagram, API spec (OpenAPI), maintenance guide.
• Warranty — 12 months free bug fixes, SLA up to 4 hours for critical issues.

YNAB recommends a similar approach for user retention.

Why trust us with development?

We don't just write code — we build financial systems. Our experience includes bank integrations via OpenAPI, implementing StoreKit 2 and Billing 6 for subscriptions, compliance with App Store Review Guidelines (section 5.1 — privacy). Over the years in the market, we've released 8 apps in the "Finance" category with minimal churn (<5% after 3 months). Every project is accompanied by documentation and client team training.

Process

1. Analytics — user interviews, competitive analysis, methodology definition.
2. Design — data model, prototype, user stories.
3. Development — 2-week sprints, code review, CI/CD.
4. Testing — manual + automated, beta test via TestFlight / Firebase Distribution.
5. Deployment — store submission, monitoring setup (Crashlytics, Sentry).
6. Support — warranty maintenance, on-request enhancements.

Time estimates

Cost is calculated individually — depends on team composition, integration complexity, and design. Contact us for a project estimate — we'll prepare a commercial proposal with a week-by-week plan within 2 business days. Order development — get a consultation from an engineer on your future app's architecture.

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.