Reference

Need:The company has the purpose to increase the average ticket of the stores excluding spending on gasoline. It is necessary to predict the most optimal patterns to replicate them in other stores, using historical data and data mining. Approaches: Why is this store so beneficial? Which are the most important predictors? Which is the best timetable for a store? Which products should the store have in a specific geographical area? Can I replicate a star store in other places?...
Prediction of the energy production of different wind farms in different conditions (speed, direction of the wind, hour, area, etc.) Purpose: to achieve a more efficient use and suggest prices Problematic: Small deviations in the speed and direction of the wind cause big deviations in the production. The wind farms are distributed in different areas of Spain with different behaviours. Precision of 80% in the production prediction. Increase of 18% of the efficiency....
Definition and implementation of the Query&Reporting solution and the definition of the reports Cost reduction through efficiency in the payments and decrease in the duplications of systems to increase the satisfaction of the client with less times of answer (time to market) and improvement of the management of the cash through the optimal monitoring and control....
Optimization of the advertising investment in media taking into account various factors: Choosing the most profitable investment depending on the product Determining the right time to invest in advertising Selecting which are the most appropriate media for the investment See the full case...
Prediction of the energetic consumption of several shopping centres in a given time slot. Generation of predictive models with wide heterogeneity of variables. Design of a simulator or case manager to make questions. Generation of reports from the obtained results. Graphical user interface for an efficient management. Value conclusions regarding consumption of the shopping centres with similar characteristics (proximity). Prediction of the 86% of the energetic consumption...
Company's need: Deeper knowledge of consumers Innovate on the relationship strategies with consumers: recruitment and retention. Solution: Predictive models able to indicate the characteristics of the clients with higher probability of abandonment on the short, medium and long-term listed according to their value for the company. Results: The three main causes of the clients' abandonment are exposed, Prediction of the 80 % of users that would be leaving the company. See the full case...

Apara is a company born in 2002 with 100% Spanish capital as a spin-off of the Quality and Technology Group. More than fifteen years of experience in Predictive Analytics and Business Intelligence prove its success. Apara has developed dVelox, the first Spanish business platform regarding Predictive Analytics mentioned by Gartner in more than 15 reports. 

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