AI Data Driven Multi Touchpoint Attribution and Marketing Analytics Platform
Enterprise-grade AI-powered marketing analytics platform with deep learning-based multi-touch attribution, offline media measurement, and ML-driven budget optimization.
The Challenge
Modern marketing organizations face significant challenges in understanding the true impact of their marketing investments. Traditional last-click attribution is inaccurate and undervalues upper-funnel channels. Customer journeys span 5-10+ touchpoints across multiple channels, making it impossible to accurately determine which interactions drive conversions without advanced ML models. Additionally, TV, Radio, and CTV advertising cannot be measured with traditional analytics, requiring sophisticated statistical uplift modeling.
Multi-Touch Attribution Complexity
Traditional last-click attribution undervalues upper-funnel channels. Customer journeys span 5-10+ touchpoints across multiple channels, making it impossible to accurately determine which interactions drive conversions without advanced ML models.
Offline Media Measurement Challenge
TV, Radio, and CTV advertising cannot be tracked with pixels or cookies. Measuring incremental impact on website traffic and conversions requires sophisticated statistical uplift modeling comparing baseline vs. media airing periods.
Enterprise Data Integration
Integrating 15+ data sources including Google Ads, Facebook Ads, LinkedIn, TikTok, Snapchat, BigQuery, Attributy Tracking Platform, Adjust, DV360, and offline media APIs - each with unique authentication, rate limits, schemas, and update frequencies.
Production ML at Scale
Deploying deep learning models in production for hundreds of companies requires automated training pipelines, model versioning, S3 artifact management, real-time inference, and graceful fallback mechanisms for reliability.
Multi-Tenant Architecture
Supporting hundreds of client companies with complete data isolation, independent model training, timezone-aware scheduling, and parallel processing while maintaining 99%+ uptime and <60 minute processing time per company.
Budget Optimization Complexity
Optimizing budget allocation across channels and sources requires analyzing historical spend, ROAS, customer journey paths, and offline media impact to generate actionable recommendations with multiple strategy options.
Our Solution
We developed a comprehensive AI-powered marketing analytics platform that consolidates data from 15+ sources into a unified system. Our solution leverages custom deep learning models (3-layer MLP) for accurate multi-touch attribution achieving 80%+ accuracy, dynamic uplift modeling for offline media measurement (TV, Radio, CTV), and machine learning-powered budget optimization using linear regression and Markov chain analysis. The platform operates on a modular pipeline architecture with automated daily processing, comprehensive error handling, and multi-tenant support for hundreds of companies.
Deep Learning Attribution
Custom 3-layer MLP neural network with 20+ engineered features achieving 80%+ accuracy and 0.85+ AUC-ROC scores, with SHAP interpretability and automated weekly retraining.
Enterprise Data Integration
Modular ETL architecture integrating 15+ platforms (Google Ads, Facebook, LinkedIn, TikTok, BigQuery, Attributy Tracking Platform, Adjust, DV360) with standardized interfaces and multi-tenant PostgreSQL schemas.
ML-Powered Budget Optimization
Linear regression and Markov chain analysis generating optimal budget allocation recommendations across channels and sources with multiple strategy options (conservative, balanced, aggressive).
Production MLOps Pipeline
Automated weekly model retraining, AWS S3 artifact management, model versioning, real-time inference, and comprehensive fallback mechanisms ensuring 99%+ reliability.
AI Engineering Excellence
Our development team engineered a world-class AI system that demonstrates exceptional technical sophistication. We built custom 3-layer Multi-Layer Perceptron (MLP) neural networks for multi-touch attribution achieving 80%+ accuracy, implemented production-grade MLOps practices with automated training and AWS S3 integration, created scalable data engineering pipelines integrating 15+ data sources, and developed statistical uplift models for offline media measurement (TV, Radio, CTV).
Deep Learning Attribution Model
Custom 3-layer Multi-Layer Perceptron (MLP) neural network architecture that transforms customer journey data into rich feature vectors with 20+ features including channels, devices, behavioral signals, and temporal patterns.
Custom MLP: 12-8-2 architecture with ReLU and Sigmoid activations
20+ engineered features: channels, devices, time on site, actions, touchpoint sequences
SHAP integration for model interpretability and feature importance
Automated weekly retraining with early stopping and model checkpointing
Production models achieving 80%+ accuracy with 0.85+ AUC-ROC scores
Graceful fallback to last-click attribution when insufficient data
Enterprise Data Engineering
Modular ETL architecture integrating 15+ data sources including Google Ads, Facebook, LinkedIn, TikTok, Snapchat, BigQuery, Attributy Tracking Platform, Adjust, DV360, and offline media APIs with standardized interfaces and robust error handling.
15+ platform integrations: Google Ads, Facebook, LinkedIn, TikTok, Snapchat, Bing, Outbrain, Yahoo, DV360, Adjust, Attributy Tracking Platform, BigQuery
Multi-tenant PostgreSQL architecture with isolated schemas per company
Automated data quality validation and completeness checks
Incremental data processing with bulk insert operations
Timezone normalization and schema standardization across sources
Comprehensive error handling with email and Slack notifications
Production MLOps Pipeline
Enterprise-grade MLOps practices with automated training pipelines, model versioning, AWS S3 artifact management, real-time inference, and comprehensive monitoring for production reliability.
Automated weekly model retraining (Saturdays) with conditional triggers
Model versioning: accuracy-based naming with automatic best-model selection
AWS S3 integration for model artifact storage and backup
Real-time inference pipeline with consistent preprocessing
Model performance tracking: accuracy, AUC-ROC, F1-score metrics
Comprehensive fallback mechanisms ensuring 99%+ system reliability
ML-Powered Budget Optimization
Advanced budget allocation engine using linear regression, Markov chain analysis, and historical performance data to generate optimal spend recommendations across channels and sources.
Channel-level and source-level optimization models
Multiple strategy options: conservative, balanced, aggressive approaches
Offline media integration: TV, Radio, CTV impact included in recommendations
Markov chain analysis for customer journey path optimization
Historical ROAS-based recommendations with spend share calculations
Daily updated recommendations with automatic model retraining
Offline Media Measurement
Statistical uplift modeling for TV, Radio, and CTV advertising that measures incremental impact on website traffic and conversions using constant median and baseline comparison methods.
TV uplift calculation: constant median method with country-level analysis
Radio uplift measurement: statistical modeling for incremental impact
CTV uplift analysis: digital-first approach with DV360 integration
Baseline comparison: measures incremental traffic/conversions during media airing
Automated report generation with email distribution and CSV exports
Integration with budget optimizer for unified media planning
Modular Pipeline Architecture
20+ independent modules with clear separation of concerns, enabling rapid feature development, easy integration of new data sources, and maintainable codebase supporting hundreds of companies.
20+ independent modules: data_import, merge, touchpoint, uplift, report, budget_optimizer
Modular pipeline design: Daily Run, TV Report, Radio Report pipelines
Configuration-driven architecture with JSON-based settings
Reusable components and standardized interfaces
Easy extensibility for new data sources and features
Production-grade error handling and status management
Deep Learning Attribution Model Architecture
Our custom Multi-Layer Perceptron (MLP) architecture transforms raw customer journey data into rich feature vectors with 20+ engineered features including channel sequences, device types, behavioral signals (time on site, actions, pages viewed), and temporal features (time from previous touchpoint). The model uses a 12-8-2 architecture with ReLU and Sigmoid activations, achieving 80%+ accuracy in production with 0.85+ AUC-ROC scores. Models train automatically on Saturdays with early stopping, checkpointing, and only save if they exceed the 70% accuracy threshold and outperform previous models.
12-8-2 MLP Architecture
Custom neural network with ReLU hidden layers and Sigmoid output activation for binary classification
20+ Engineered Features
Channels, devices, behavioral signals, temporal patterns, and touchpoint sequences with StandardScaler normalization
SHAP Interpretability
Model interpretability with SHAP values for feature importance and attribution credit distribution
Impact & Results
00%+
Deep MTA model accuracy
00+
Data sources integrated
00%+
System uptime reliability
00-30%
Marketing ROI improvement
00%+
Reduction in manual reporting
<00min
Daily processing time per company
Operational Efficiency
80%+ reduction in manual reporting effort through automated daily processing at 1 AM company timezone. Real-time insights provide near real-time visibility into marketing performance with automated email distribution and CSV exports.
Marketing Performance
Deep learning MTA provides accurate credit to all touchpoints (not just last-click), identifying undervalued upper-funnel channels. Budget optimizer recommendations increase overall marketing ROI by 10-30% through data-driven allocation.
System Reliability
99%+ system uptime with comprehensive error handling, email/Slack notifications, and automated recovery. Processing completed within 60 minutes per company for full daily pipeline including data import, attribution, reporting, and budget optimization.
Scalability & Multi-Tenancy
Multi-tenant PostgreSQL architecture supports hundreds of companies with isolated schemas, independent model training, and parallel processing. Timezone-aware scheduling optimizes resource usage with horizontal scaling capability for future growth.
Technology Stack
We leveraged modern AI technologies, machine learning frameworks, and cloud infrastructure to build a scalable, production-grade marketing analytics platform.
Python
PostgreSQL
AWS
TensorFlow/Keras
scikit-learn
pandas
NumPy
SHAP
Key Technical Achievements
Modular Pipeline Architecture
20+ independent modules (data_import, merge, touchpoint, uplift, report, budget_optimizer) with clear separation of concerns, enabling rapid feature development and easy integration of new data sources.
Production MLOps Pipeline
Automated weekly model retraining (Saturdays), model versioning with accuracy-based naming, AWS S3 artifact storage, and comprehensive monitoring with fallback mechanisms.
Multi-Source Data Integration
15+ platform integrations (Google Ads, Facebook, LinkedIn, TikTok, Snapchat, BigQuery, Attributy Tracking Platform, Adjust, DV360) with standardized interfaces, error handling, and data quality validation.
Real-Time ML Inference
Real-time inference pipeline with consistent preprocessing, SHAP-based attribution credit calculation, and graceful fallback to last-click attribution ensuring 99%+ reliability.