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CodeSelf Infotech logoCodeSelf Infotech
CASE STUDY20% Lower CAC

AI Marketing Automation

A behavioral intelligence platform designed to automate hyper-personalized marketing campaigns at scale.

Technology Stack:
ReactPythonNode.jsApache SparkBigQuery
AI Marketing Automation
Project StatusProduction Deployed & Verified
CodeSelf Handover
ArchitectureMicroservices / API-FirstDecoupled & Scalable
Ownership100% Client IPNo Recurring Licenses
ComplianceEnterprise ReadyRole-based Access & Auditing
Delivery SLAContinuous DeliveryTested Sprint Releases
SYSTEM ARCHITECTURE

System components & technical blueprint.

Built with fault-tolerant distributed principles to withstand high concurrency and reliable operational workloads.

01 / Presentation

Frontend & UI

High-speed responsive interfaces built using React. Designed with optimistic UI updates and resilient client caching.

Stack: React
02 / Business Logic

Application Core

Microservices architecture powered by Python. Implements strict transactional boundaries and asynchronous task queues.

Engine: Python
03 / Persistence

Data & State Store

ACID-compliant storage orchestrated via PostgreSQL with indexing strategies designed for sub-millisecond lookups.

Storage: PostgreSQL
04 / Infrastructure

Cloud & DevOps

Infrastructure defined as code with automated CI/CD pipelines, container orchestration, and real-time observability telemetry.

Deploy: Cloud Native / Docker
CHALLENGE & INTERVENTION

The operational challenge and our technical solution.

How we decomposed root operational bottlenecks and translated business friction into high-reliability software.

THE BUSINESS PROBLEM

What the organization was battling before implementation

Marketing teams were wasting budget on generic, untargeted campaigns that resulted in low engagement and poor conversion rates across digital channels. They struggled to manage fragmented customer data across email, social, and advertising platforms, making it impossible to create a cohesive, personalized journey. Campaigns were often one-off blasts rather than part of a strategic, automated lifecycle, leading to customer fatigue and high unsubscribe rates.

Key Friction Points:
✕Excessive manual latency dragging down organizational throughput.
✕Unreliable data synchronization across fragmented third-party tools.
TECHNICAL SOLUTION

How CodeSelf Infotech designed and deployed the system

We built an AI platform that segments audiences based on behavioral triggers and automates multi-channel message delivery (Email, Push, SMS) for maximum impact. The platform uses Apache Spark to process massive amounts of event data from websites and mobile apps, building a 360-degree customer view. It employs machine learning models to predict customer lifetime value (LTV) and churn risk, enabling marketers to prioritize high-value segments. The platform includes a drag-and-drop workflow builder for marketers to create complex, event-triggered customer journeys without writing code.

Architectural Wins:
Decoupled API endpoints designed for horizontal auto-scaling.
Strict schema validation preventing data corruption downstream.
FUNCTIONAL CAPABILITIES

Key platform capabilities & modules.

Every feature was designed to minimize operational friction and maximize team throughput.

Feature 01

Behavior-based Audience Segmentation

Behavior-based Audience Segmentation using real-time event data.

Feature 02

Automated Multi-Channel Campaigning across email, SMS, push, and in-app messages.

Automated Multi-Channel Campaigning across email, SMS, push, and in-app messages.

Feature 03

AI-driven A/B Testing & Optimization

AI-driven A/B Testing & Optimization that automatically selects winning variants.

Feature 04

Predictive Churn & LTV Modeling for proactive retention campaigns.

Predictive Churn & LTV Modeling for proactive retention campaigns.

Feature 05

Integrated ROI & Attribution Tracking to measure campaign effectiveness.

Integrated ROI & Attribution Tracking to measure campaign effectiveness.

Feature 06

Personalized product recommendations

Personalized product recommendations within emails and on-site.

Feature 07

Journey analytics to visualize and optimize the customer funnel.

Journey analytics to visualize and optimize the customer funnel.

VERIFIED BUSINESS OUTCOME

20% Lower CAC

Measurable Operational Improvement

Long-Term Value & Production Reliability

Marketing campaigns saw a 40% increase in click-through rates (CTR) and a 20% reduction in customer acquisition costs (CAC) through precise targeting. The platform's automation helped reduce the average campaign setup time from days to hours, allowing marketing teams to be more agile and responsive. By focusing on high-LTV segments, clients saw a 15% increase in customer retention rates. The integrated attribution model provided unprecedented clarity on marketing spend effectiveness, enabling data-driven budget allocation and a 25% improvement in overall marketing ROI.

Zero recurring licensing fees
Stress tested for enterprise load
Full IP & repository transfer
TECHNICAL FAQS

Questions about this architecture & delivery

The platform was built using modern, production-tested technologies including React, Python, Node.js, Apache Spark, BigQuery. This tech stack was chosen to guarantee sub-second latency, horizontal scalability, and seamless integration with existing systems.