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Summary

This project involved developing an AI-powered customer behavior analysis system for an e-commerce company. The client needed an intelligent system to analyze customer behavior, identify patterns, and provide actionable insights to increase online purchase rates. The main problem was the low conversion rate despite high website traffic, indicating a need for deeper understanding and optimization of the customer journey.

Business Impact

  • Predicting the probability of both the purchase intent and actual purchase helped our client to prioritize calls queue and reduce the influence of bad traffic on the business by 16%.

  • The solution also helped to optimize the customer journey on the website to prevent revenue losses from unrealized purchases.

Tech challenges

  • By using deep learning, our team first aimed to understand customer behavior and then model the probability of purchase based on user’s web-surfing experience.

  • Those recommendations had to be revenue driven, maximizing profits of the service, while providing high quality services to a customer.

  • Integration of our Apache Spark + Tensorflow architecture with clients Elastic + PostgreSQL + RabbitMQ.

Timelines
1

2 Weeks

Data Labelling and Processing

Data Engineer

2

1 Week

Solution Architecture Design

Solution Architect

3

2 Weeks

Hypothesis Generation & Validation

Deep Learning Researcher

4

1 Week

Architecture Modelling

Deep Learning Researcher

5

3 Weeks

Feature Engineering

Deep Learning Engineer, Deep Learning Researcher

6

2 Weeks

Data Streaming Pipeline Development

Data Engineer

7

6 Weeks

Training & Tuning Cycle

Deep Learning Researcher

8

2 Weeks

Integration & Deployment

Backend Developer, Dev Ops

Case Study Info

  • Industry:
    Retail
  • Stack:
    Firebase, OpenAI Gym, Python, TensorFlow

Highlights

  • Advanced Predictive Analytics
  • Comprehensive Data Integration
  • User-Friendly Dashboard
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