Machine Learning & Predictive Data Analytics
The key element in every business Digital Transformation process is Data Analytics.
Quenta Solutions helps the companies to increase their business activities by predicting the users behavior through all the data they generate when interacting with the different applications.
The goal in Quenta Solutions is to support the companies in the process of having a better knowledge of their customers, so that they can get the maximum benefit from their data and reach the business objectives, obtaining competitive advantage in the market.
By combining business knowledge, machine learning techniques and predictive analytics, Quenta Solutions can make a better approach to increase business activities and to improve the company strategy.
Machine Learning techniques and Predictive Analytics services owned by Quenta Solutions make the Digital Transformation an easier process for business, since they allow us to anticipate every future action by considering historical data and real time data. This is a key factor in Decision Making for every company in aspects such as:
- Detect and prevent fraud.
- Prevent customer leakage (customer loyalty actions).
- Potential customers prediction (age, gender, civil status, hobbies, preferences, purchasing power, etc.).
- Predict products demand (travels, courses, hotels, tickets, etc.).
- Define the optimal products catalog for each market area (content based classification).
- Offers and customized recommendations.
- Prevent claims and questions (customer support).
- Predict Hotel Occupancy (leisure offers, lively events, customized feeding, etc.).
Quenta Solutions analyzes business data coming from all users, historically and in real time, and adds a new data track for long periods, using new business metrics. When data is analyzed in a deep way, business predictions can be established in such a way they can change the course of the business strategy.
An objective function is established to make the predictions. A series of questions initializes the process, whose answers are used to take the necessary data to make the prediction accurate.
Relevant data is gathered and normalized to allow the mathematical algorithms to parse them. This is a key part in the Machine Learning process.
An analysis technique is applied over the parsed data cluster to obtain the predictive model.
The generated predictive model is applied over the real data, which is gathered in real time, which allows to get a business prediction based on the main objective.
In this way, we can anticipate and take decisions with the highest levels of accuracy to generate more business volume to the companies, providing them competitive advantage in the market.