A New Machine Learning Model
Over the past year, our Data Management & Analytical Processing division has been focused on developing predictive models for purchase behavior. These models are built on advanced machine learning algorithms capable of delivering highly accurate predictions regarding customer spending patterns.
In an environment where credit/debit card transactions are growing rapidly, the ability to analyze and harness this data can make a significant impact. By leveraging this data, businesses can gain valuable insights to develop more personalized and effective services for their customers.
Will the customer make additional transactions?
In which sector will these transactions occur?
At which merchants will they shop?
How much will they spend at each merchant?
On which days of the month will the transactions take place?
Our predictive models can answer these questions with accuracy levels reaching up to 99%.
Achieved Results
The success of this project has been made possible by utilizing cutting-edge machine learning techniques (specifically, Probabilistic Neural Networks) combined with our extensive experience in data analysis and preparation.
Our models provide additional detail compared to traditional neural networks: the degree of uncertainty in the prediction. This is very important when you want to provide a service, not only personalized, but also as reliable as possible.
Specifically, our framework is able to predict, for the next month, for each customer and each individual merchant from whom they have previously purchased:
- The likelihood that the customer will be able to make new credit or debit card transactions
- If this probability exceeds 50%, the expectation of total expenditure incurred
- The forecast of the number of expenditure events
- And finally, the prediction of the days on which the customer will carry out his transactions
Our approach
Our passion for these issues and satisfaction with the result of our work has finally prompted us to share the path of study, analysis and experimentation that we have undertaken, certain that it can be an interesting contribution both for those in the field and for those who wish to approach these types of solutions.
Contact us to request the full study document.
