Customer Behavior Analysis to Increase Online Purchase Rates

Analyzing customer behavior on the web pages, predicting and preventing revenue losses.

Yuliya Sychikova
COO @ DataRoot Labs
11 Jun 2020
4 min read
Customer Behavior Analysis to Increase Online Purchase Rates
Client Services
Industries
Retail

Summary

  • Out client is one of the largest CPA networks in Eastern Europe.
  • In the client’s business model, a gap between the payment from the CPA to affiliate and the payment from customer to CPA, gives a customer the time to change their mind about the purchase. The above leads to suppressed conversion purchase rates.
  • We were challenged with reducing those revenue losses by analyzing the customer behavior on the web pages, predicting and preventing such cases.
4
Months
6
Engineers
4
Technologies

Tech Stack

Firebase
OpenAI Gym
Python
TensorFlow

Timeline

2 Weeks
Data Labelling and Processing
Data Engineer
1 Week
Solution Architecture Design
Solution Architect
2 Weeks
Hypothesis Generation & Validation
Deep Learning Researcher
1 Week
Architecture Modelling
Deep Learning Researcher
3 Weeks
Feature Engineering
Deep Learning Engineer
Deep Learning Researcher
2 Weeks
Data Streaming Pipeline Development
Data Engineer
6 Weeks
Training & Tuning Cycle
Deep Learning Researcher
2 Weeks
Integration & Deployment
Backend Developer
Dev Ops

Tech Challenge

  • 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.

Solution

  • We’ve developed a detailed events tracking plan of customers activity and then tested the implementation against the historical data.
  • The above helped to measure the most important behavioral patterns, correlating with customers purchase activity.
  • Deep learning technologies helped us find the unintuitive correlations between users activity and purchasing outcomes, which are hard to find and interpret.

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.

Have an idea? Let's discuss!

Book a meeting
Yuliya Sychikova
Yuliya Sychikova
COO @ DataRoot Labs
Do you have questions related to your AI-Powered project?

Talk to Yuliya. She will make sure that all is covered. Don't waste time on googling - get all answers from relevant expert in under one hour.
OR
Send us a note
Optional
File requirements pdf, docx, pptx

Author

Yuliya Sychikova
COO @ DataRoot Labs
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.

Co-Authors

Ivan Didur
CTO @ DataRoot Labs
offices map
Kyiv (HQ)
Max Frolov
CEO @ DataRoot Labs
Tel Aviv
Ivan Didur
CTO @ DataRoot Labs
Los Angeles
Yuliya Sychikova
COO @ DataRoot Labs
builds and implement AI-powered systems across different verticals to help our clients operate effectively.
GoodFirms Badge