+24% Weekly Retained Users

Optimized a consumer mobile app for faster personalization and stronger repeat usage.

Enhanced core recommendation and usage loops to grow engagement in a utility-focused app.

Project: SuperCook

MobilePersonalizationConsumer

Executive Summary

A quick leadership snapshot of platform scope, delivery approach, and measurable outcomes.

Industry

Marketplace / On-demand

Platform

Android

Tech Stack

Kotlin, Node.js, Python

Result

+24% Weekly Retained Users

Timeline

11 weeks

Service Category

Mobile Apps

Type

Client Project

Live Product

Explore the live project surfaces across web and app platforms.

Visit Play Store

Problem

Client Background

SuperCook required a more responsive and personalized app experience to improve recurring value.

Critical Risk Area

Users were not consistently receiving relevant value in early sessions.

Low repeat usage after initial installs
Slow recommendation interactions
Weak personalization depth

Solution

Delivery Outcome

We improved recommendation pipelines, interaction flow speed, and retention touchpoints for high-frequency utility usage.

Why this approach

Utility apps retain users when they deliver immediate and relevant value with minimal friction.

Recommendation flow improvements

Faster interaction loop

Behavior-based prompts

Retention analytics

Process

How we made key decisions, handled technical complexity, and applied engineering expertise to deliver measurable outcomes.

Product & Architecture Decisions

Separated recommendation logic from UI interaction layer
Added local caching for faster repeat sessions

Technology Selection Reasoning

Android-native optimization for performance
ML-assisted ranking for relevance improvement

Complexity Managed

Reduced latency in recommendation display
Improved session continuity across usage cycles

System Design Approach

Rolled out speed and relevance improvements incrementally to protect engagement stability during updates.

Engineering Highlights

Key technical decisions that enabled production-grade reliability, maintainability, and system scale.

Backend Architecture Design

Separated recommendation logic from UI interaction layer

API Integrations

Implemented robust API integrations with explicit contracts, retries, and monitoring hooks.

Performance Optimization

Reduced latency in recommendation display

Scalability Considerations

Weekly Retained: 50.8%

Data Processing Workflows

Retention analytics

Tech Stack

A modern technology stack selected to maximize performance, scalability, and delivery speed.

Our stack is selected for reliability, maintainability, and production scale.

Core Stack

Kotlin

Node.js

Python

Firebase

Supporting Tools

We also work with a wide range of modern technologies based on project requirements.

TensorFlowAnalyticsCachingPush Notifications

Infrastructure / Workflow

Git

GitHub

GitLab

CI/CD

Code Reviews

Agile

Testing & QA

Results

Measured outcomes across efficiency, scalability, and system performance improvements.

Efficiency

+24%

Weekly Retained Users

Automation

+31%

Recommendation Interactions

Scalability

+17%

Session Completion

Weekly Retained

Before

41%

After

50.8%

Rec Interaction Rate

Before

28%

After

36.7%

Avg. Session Time

Before

3m 02s

After

3m 31s

Business Impact Snapshot

Increased weekly retained users by 24% and improved recommendation interactions by 31%.
SuperCook improved recurring utility value and created stronger retention performance in core cohorts.

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