AI Conversion Prediction Implementation in Mobile Applications
Conversion in a mobile app is a fuzzy term. Registration, first purchase, subscription upgrade, target action completion — all are conversions, and the model must be built for a specific one. Predicting "probability of purchase within 7 days" and "probability of onboarding completion" are fundamentally different tasks with different features and different practical value.
Defining the Conversion Goal
Before building the model, we fix exactly what we're predicting:
- Free-to-paid conversion in subscription app
- First purchase in e-commerce or in-app shop
- Onboarding flow completion (often predicts long-term retention better than direct purchases)
- Return to abandoned cart / unfinished form
Each goal needs its own time horizon (7 days, 30 days) and its own labeling in training data.
Features That Actually Work
From practical experience building conversion prediction models:
Behavioral patterns in first sessions work best. User who opened the app 3+ times in first 48 hours and reached the premium features screen converts with significantly above-average probability. First 48 hours is the critical window.
Feature depth: did they reach paywall, click "Learn more", add something to favorites. These are binary features, cheap to implement and powerful for the model.
Attribution source: users from organic search convert differently than from paid ads. SKAdNetwork (iOS) / Install Referrer API (Android) give attribution — add to features.
Device characteristics: iPhone 14 Pro+ owners convert statistically differently than budget Android users. Not discrimination, this is correlation with purchase power.
Model Architecture
Binary classification: LightGBM or XGBoost for tabular data, trained on historical cohorts. Sample: users registered in last 6–12 months, labeled "converted within N days" (Y=1) or not (Y=0).
Main pitfall — data leakage. Can't include in features events that happened after prediction point. If predicting conversion on day 3, features must only be from days 0–3. Sounds obvious — in practice people get it wrong often.
Model updates: retrain monthly or on drift — when distribution of features in production starts diverging from training sample. PSI (Population Stability Index) for monitoring drift.
Using Prediction on Client
Scoring is server-side, batch. Daily or real-time on new session (latency < 200ms via Redis result cache).
What we do with result on mobile:
Personalized paywall. High-propensity user sees extended trial (14 days instead of 7) or social proof. Low-propensity — more aggressive discount. A/B test mandatory to validate hypotheses.
Timing of push notifications. User with high predicted conversion probability gets onboarding reminder at peak engagement moment — usually evening in user's timezone. Firebase Functions + FCM for implementation.
Feature gating. User with conversion score > 0.6 temporarily unlocks premium feature — let them "try it". Config managed via Firebase Remote Config, mobile client checks flag on session start.
Measuring Results
Model must be validated not just by offline metrics (AUC, precision/recall) but by business metrics in production. A/B test: group A gets personalization based on model, group B gets default flow. Look at conversion rate, ARPU over 30 days.
Work Process
Analytics audit and event tracking → define target conversion and horizon → build feature pipeline → train and validate model → integrate scoring → configure personalization on client → A/B test → monitoring.
Timeline Guidelines
Basic model with personalized paywall and A/B test — 3–5 weeks with existing data. Full system with real-time scoring, feature gating and monitoring dashboard — 8–12 weeks. Pricing is calculated individually.