Bluetab

an IBM Company

Casos

Provision

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Provision

Within the telecommunications sector, there has always been a desire to individualize the offer to each of its clients, so that they obtain exactly the product they need. This has not been possible due to a thousand circumstances, both technical and functional.

Bluetab has developed a solution that allows flexibility and self-management of this process of provision of telecommunications services to its clients in a multinational operator, which has allowed an improvement in both end-customer satisfaction and internal management of the entire workflow . Based on a flow engine, it has been possible to orchestrate automatically the configuration of the telecommunications service package that a business client needs to develop their business. The technological environment where this entire platform has been developed is Grails 1.XX, Grails 2, Sencha (ExtJS) and Grails 3.

SUCCESS STORIES

Regulatory Information Energy CNMC

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Regulatory Information Energy CNMC

The remuneration model of the electricity market in Spain requires companies engaged in the distribution of electricity to provide a large amount of information to the CNMC about their investments and electricity grids with a level of detail that is hardly available in business management systems of distribution.

A system capable of communicating with the different data sources in order to obtain all the necessary information for the preparation of the reports that the different distributors have to present annually to the regulatory body regarding their expenses and investments made, has been developed.

The main processes that make up this system in charge of preparing and presenting information to the regulatory agency are:

  • barra-bluetab Investments: process that contains all the economic and technical information, as well as the time dedicated to the work, necessary to relate the investments made with the corresponding facilities.
  • barra-bluetab Consistencies: analysis process that guarantees that the information is valid to be presented correctly in the different reports.
  • barra-bluetab Inventory: process that contains all the information related to each of the facilities.
  • barra-bluetab Loading information and Master Data: processes responsible for transferring all the information from the source systems and their respective configurations necessary to adapt the data to the criteria of current legislation.
SUCCESS STORIES

NPS

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NPS

Project for the Customer Experience area of ​​a Telco operator. Preparation and quantitative and qualitative analysis of customer surveys to determine the basic indicator of customer experience with the company and its services.

The Bluetab Data Scientist team designed an automatism for the extraction and processing of the sample databases and the subsequent collection, processing and analysis of customer responses. For this, SPSS migrated to R and Python, Spider, database treatment in SQL was used in the first versions to continue with the preparation of the introduction of survey results carried out with AI.

SUCCESS STORIES

Fraud prediction model

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Fraud prediction model

Design of the predictive model of Web Frauds carried out by Bluetab for a leading financial institution in the Spanish market

It consists of developing a predictive algorithm capable of classifying browsing sessions according to their similarity to sessions in which a fraudulent transfer has occurred. The output of this algorithm will be a scoring (or a probability) that will allow ordering these sessions by their probability of being fraudulent.

For this, information from the management of alerts and claims of fraud in Remote Banking Transfers is used, as well as the daily information of the browsing sessions on the web and in the APP; therefore, the target audience or population on which this model will be executed is the one that has started a session by one means or another.

Our methodology is based on the construction of the target based on the crossing between fraud and navigation information (the most critical process of a predictive model), in the diagnosis of inconsistencies among fraudulent operations that do not have an associated session, outliers with some wrong record on date.

SUCCESS STORIES

CRM with artificial intelligence

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CRM with artificial intelligence

As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking of the installed customer base.

By implementing ML, it is possible to enhance the commercial strategy of the entity, broadening the customer vision and integrating with the current CRM, which will increase the probability of success of the actions to be carried out, improving income and customer satisfaction and loyalty. For this, the algorithm prioritizes through existing campaigns crossing with alternative products such as loans, mortgages, funds, cards, pension plans, or different types and insurance coverage (home, health, car)

As output, it is intended to show the improvement by measuring the increase in specific indicators such as:

  • barra-bluetab Contracts
  • barra-bluetab value-customer ratio
  • barra-bluetab % digitization per customer
  • barra-bluetab Click-through rate
SUCCESS STORIES

MULTI-BRAND NDWH

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MULTI-BRAND NDWH

Among the emerging operators in the telecommunications business, there has been an initiative that allows a single entity to integrate several commercial brands, integrating the processes common to all of them and making them as efficient as possible.

So, for the particular case of data management, a single, centralised repository is being built for all the relevant information for the different commercial brands, to allow for a unique exploitation of the data.

This platform has been developed on Google Big Query, Google Looker, Oracle, Microsoft and Tableau.

SUCCESS STORIES

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