JSS  Vol.3 No.10 , October 2015
Strategic Modeling for the Characterization of the Conditions That Allow the Anticipation of the Consumer’s Requests*
Abstract: In order to recognize and anticipate customer’s requests, reinforce and manage the communication and operational flows, analyses of which are the services that the consumers demand to the energy companies are extremely important. To improve each relationship channel and to recognize the customers’ profile, expectations and needs, it has been necessary to reinforce the operational efficiency concerning costs and agility. Operational effectiveness answering the demands is pursued, considering the amount of services offered and generated by relationship channels, communication facilities and operational conditions provided by the companies. A huge amount of data had to be organized to represent this scenario and forecast the relationship. To represent it, models were created to monitor and predict customers’ requirements based on historical and online data. There were established regional resources availability, weather conditions, problems concerning the energy distribution grid, as well as the motivation of the customers to use alternative medias or relationship channels. The big data treatment techniques were used in order to provide the necessary agility to achieve the monthly/hourly volume of data (millions of registers per month) and permit communication clusters’ views.
Cite this paper: Lima, C. , Luz, B. , Takemoto, S. , Barisson Jr., P. , Tezzin, R. , Peres, L. , Santos, F. , Anarelli, T. and Silva, A. (2015) Strategic Modeling for the Characterization of the Conditions That Allow the Anticipation of the Consumer’s Requests*. Open Journal of Social Sciences, 3, 146-160. doi: 10.4236/jss.2015.310021.

[1]   Parmenter, D. (2007) Key Performance Indicators: Developing, Implementing and Using Winning KPIs. John Wiley & Sons, Inc., Hoboken, 236 p.

[2]   Alexander, J. (2006) Performance Dashboards and Analysis for Value Creation. Wiley, Hoboken.

[3]   Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000) CRISP-DM 1.0 - Step-by-Step Data Mining Guide.

[4]   Shearer, C. (2000) The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5, 13-22.

[5]   Clifton, C. (2004) Introduction to Data Mining. Purdue University, West Lafayette.

[6]   Li, Y. (2004) CRISP-DM.

[7]   IBM SPSS Modeler CRISP-DM Guide.

[8]   Sathi, A. (2013) Big Data Analytics, Disruptive Technologies for Changing the Game. 2nd Edition, MC Press Online, 73 p.

[9]   Soares, S. (2012) Big Data Governance—An Emerging Imperative. MC Press Online, 342 p.