CPA FERRERE
Services
BUSINESS TECHNOLOGY AND DIGITALIZATION

Business Analytics

The use of predictive and analytical models makes it possible to detect data behavior patterns, making them into true sources of information for improving management and decision-making processes.

In an increasingly competitive and challenging business context, companies require cutting edge solutions to transform voluminous data into sources of information allowing them to design their commercial strategy and optimize the value of their client portfolio.

CPA FERRERE’s Business Analytics solutions for Big Data mining allow companies to generate competitive advantages based on mining and analysis of their data.

We design solutions conceived to facilitate, automate and operationalize analysis client data, making it part of the company’s culture. Such applications make it possible to detect data behavior patterns, transforming them into true sources of information to improve management and decision-making processes, as well as to align the firm’s competitive strategy. 


  • Shift coordination

    What is the optimal personnel assignment for each shift and each area? How can companies ensure a better response to different demand levels expected?  By designing and implementing combinatorial optimization mathematical models it is possible to automate and systematize the optimal assignment of personnel, subject to all the technical requirements and restrictions that must be managed, in a tool affording a business user-friendly interface.

  • Identification of Cross-Sell and Up-Sell opportunities

    In atomized markets, attracting a new client is much more costly that making an existing one more profitable. Here, one of the key aspects is to offer the right product, to the right person, in the right way. By complementing predictive modeling with pattern detection techniques it is possible to maximize the opportunities for special marketing of different products usually purchased together (cross-sell) or for selling a superior category of the same type of product (up-sell) so as to increase sales income and foster client loyalty.

  • Implementation of Business Intelligence applications

    We transform data and destructured information (from inside and outside your organization) into relevant information and reports that facilitate and optimize decision-making at your company.

    Our services include:

    • Corporate Information System Conceptual Modeling Design.
    • Analysis, Reporting and generation of Dashboards.
    • Design and Implementation of Balanced Scorecards.
    • Advice on selection and implementation of IT solutions.
    • Definition of technological architectures.
    • Advice on development of tailored solutions and integration with other applications.
    • Training in Business Intelligence (BI) solutions.
  • Client segmentation and profiling

    The ability to understand consumer behavior is critical for achieving success in matching consumers and products.

    Segmenting and understanding consumer behavior are key for defining the most suitable offer for each client segment. Using cluster analysis techniques it is possible to discover homogeneous client segments based on their profile and consumer patterns, critical factors for optimizing marketing by personalizing actions and campaigns specially designed and executed for each segment.

  • Logistics optimization

    Growing competitive pressure and highly demanding clients require companies to optimize management, by reducing costs, losses and waiting times, and improving the quality of the services offered.

    With Analytical Intelligence it is possible to optimize logistics decision-making so as to achieve the ideal assignment of products among branches.  Using demand prediction techniques it is possible to anticipate consumer demand more precisely and with a greater level of disaggregation.  This makes it possible to maximize expected sales via efficient and fully scalable inventory management, and to considerably reduce obsolescence and shortfalls, as well as to cut time-to-customer, optimizing the firm’s resources.

  • Building loyalty among valuable clients

    Companies face an atomized market where adding a new client can be much more costly than building loyalty with an existing one. Hence it is key to know the client portfolio and offer products in line with their needs to design an effective client retention strategy. Using Lifetime Value estimation techniques it is possible to identify the segments having greatest value for the company. In turn, techniques such as churn propensity models make it possible to identify which clients are most likely to leave. Both types of analysis are combined to focus retention efforts on the valuable clients segments with high churn probabilities.  

  • Detecting risky clients

    By developing scoring models based on predictive modeling of client payment behavior it is possible to sharpen early identification of clients most likely to default or most likely to be recovered in collections. By developing scoring models based on predictive modeling of client payment behavior it is possible to sharpen early identification of clients most likely to default or most likely to be recovered in collections. 

  • Social media information mining

    The use of natural language processing techniques makes it possible to process non-structured data sources, grasp the profiles of people talking about the institution, detect commercial opportunities or monitor brand reputation.

  • Financial industry client optimization (CTarget)

    CTarget is a service 100% dedicated to the financial industry, spawned by the joint efforts of three companies with high levels of specialization in banking, social media and behavior prediction models: Bantotal, Idatha and CPA FERRERE. CTarget transforms all data available, inside and outside the financial institution, into key information for executives to be able to make the relationship with each of their clients more profitable. 

    Over 25 years of experience made it possible to build the best predictive models and specific indicators for financial institutions, incorporating social media as a universal communication channel.  CTarget offers specific financial industry knowledge, embedded in indicators and tools for analysis of client and prospect behavior.

    Functional and technical features:

    • Single front end
    • Artificial intelligence
    • All devices
    • Application of business rules
    • Social media
    • 100% service-oriented
    • Analytical and predictive models
  • Optimization of supervision programs

    The recent modernization of revenue agencies sought to provide them with better tools for optimizing tax management, in particular by strengthening decision-making processes based on data analysis.  This makes it possible: i) to develop a comprehensive vision of the taxpayer, ii) increase efficiency, efficacy and precision in tax supervision, iii) enhance transparency and reduce discretion, and  iv) ensure flexibility and soundness to deal with changes in evasion patterns. 

    The consolidation of data-based decision-making processes required development of capacities to digitalize, store, process, mine and analyze large volumes of heterogeneous information coming from diverse sources, both internal and external.  The solutions developed included the following components:

    i. together with our business partner Quanam we provide computerization and elaboration of integrated data warehouses;

    ii. together with our business partner Quanam we provide implementation of  Business Intelligence (BI) platforms geared to supporting the tax supervision process in its various stages (case selection, crosschecking of data, supervision results); and 

    iii. development of Business Analytics models, incorporating predictive modeling to identify behavior patterns providing indicia of fraudulent practices (evasion, under-declaration, omission) and anomalous behaviors, signaling and prioritizing cases for investigation.  Fraudulent behavior follows predictable patterns, so that by analyzing data it is possible to predict where and when fraud is likely to occur.  

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