About The Client
Organization
Direct-to-consumer retail brand with over 100K monthly active users
Industry
Retail & eCommerce
Technologies Used
- AWS Personalize, SageMaker, Lambda
- Snowflake, Redshift, DynamoDB
- Segment, Google Analytics, Shopify API
- Python (Pandas, NumPy, FastAPI)
- React.js, Tailwind CSS
- Terraform, GitHub Actions
Business Need
The client experienced high bounce rates and poor customer retention despite aggressive ad spend. Their existing analytics tools provided limited behavioral segmentation. Key issues:
- No personalized product recommendations on site or email
- Inconsistent tracking across mobile, web, and in-app interactions
- Delayed campaign performance insights
- Poor visibility into customer journey across sessions
TECHARC Approach
TECHARC engineered a full-stack behavioral intelligence platform:
- 1.Customer Data Platform: Aggregated interactions from Shopify, Segment, and custom apps
- 2.Behavioral Modeling: Clustering, cohorting, and lookalike audience ML
- 3.Recommendation Engine: Built using AWS Personalize and deployed via Lambda APIs
- 4.Real-Time Analytics Layer: Integrated Snowflake and Redshift for live segmentation queries
- 5.Marketing Orchestration: Trigger-based engagement flows integrated into email, push, and retargeting ads
Our Solution
We delivered a unified Customer Intelligence System:
- 360° User Profiles: Unified customer identity across devices
- Behavioral Segmentation Engine: Real-time clustering and churn prediction
- Smart Product Recommender: Personalized rankings for homepage, cart, and post-purchase
- Engagement Tracker: Campaign triggers linked to user actions and thresholds
- Marketing Dashboard: Funnels, LTV insights, and drop-off analysis for marketers
Key Benefits
- 38% increase in repeat purchase rate within 3 months
- 4x improvement in CTR on personalized campaigns
- Unified user view across 5+ marketing and sales systems
- Enabled real-time experiments and A/B tests via a custom interface
Functional & Architectural Overview
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