Customer Behavior Analysis to Increase Online Purchase Rates

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

DRL Team
AI R&D Center
11 Jun 2020
4 min read
Customer Behavior Analysis to Increase Online Purchase Rates
Client Services


  • 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.

Tech Stack

OpenAI Gym


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.


  • 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.


  • 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.

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