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.
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.
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Yuliya Sychikova
COO @ DataRoot Labs
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