AI Driven Customer Personalised Services


Use Cases

Showing preferred car models to customers and predicting propensity score for conversion

Propensity Model


Attribution Model

Getting content attribution at page and component level from user journey analysis


Drop-off Model

Predicting customer's next activity and possible drop-off from the website


It was an end-to-end project to develop very personalized services for the online customers of that Automobile major. The tasks involved designing the end system architecture, implementing the proposed solution and integrating the developed system with their existing system. This project also involved the deployment of the AI-driven applications in their production system for A/B testing.


The end application was fully developed and deployed on Google Cloud platform and AWS. The batch data from the website was feed into Amazon S3 and it was then transferred into Google Cloud Storage for further processing. Mainly, data processing was done in two phases: Batch Processing and Real-Time Processing. The batch pipeline was created to train the model on historical data and Real-Time Pipeline was created for analyzing the online customers' data and predicting the most preferred car models for customers in near real time. The full application was developed within a container using Docker. 

Tools & Technologies

  • Google Cloud (Cloud-ML, Data Proc, Storage, Big-Query, Cloud-SQL)

  • Amazon Web Service (AWS S3)

  • Adobe Target

  • TensorFlow

  • Airflow

  • Flask


  • Kafka

  • Docker​