PISWAS: Improving What-If Analysis Model to Suggest the Best Offers Scenarios in Telecom Companies

Roua Achi; Kadan Joumaa; Salah Dowaji

doi:10.15866/irecos.v11i12.11032

PISWAS: Improving What-If Analysis Model to Suggest the Best Offers Scenarios in Telecom Companies

Roua Achi^(1*), Kadan Joumaa⁽²⁾, Salah Dowaji⁽³⁾

^(*) Corresponding author

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DOI: https://doi.org/10.15866/irecos.v11i12.11032

Abstract

Affordable offers are one of the best ways to increase the revenue in telecom companies. Decision makers can predict potential revenue before launching an offer, depending on what-if analysis system. However, previous what-if analysis models in the literature have been directed just to predict an impact of a specific scenario. Therefore, our main goal in this approach is to enhance what-if analysis to suggest the best scenarios, in addition to predict their impacts. This approach depends on enhanced k-means algorithm to categorize customers into segments of the same behavior or usage. The authors applied also Multiple Linear Regression algorithms to predict revenues of offers’ scenarios suggested by our system. To validate our model, we built a new interactive guided application, so named PISWAS system. This system was tested with real customers’ data from a local important telecom company in our country. Then we compared the obtained results of two selected launched offers in this company. As a result, PISWAS system was very helpful for decision makers to assess and select the perfect offers’ scenarios relating to their company business need.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords

What-If Analysis; K-means; Multiple Linear Regression; Telecom; Decision Making; Offers

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References

Soorena Merat, P.Eng, Dr. Wahab Almuhtadi, P.Eng, (2015) Standard ARPU Calculation Improvement Using Artificial Intelligent Techniques, International Journal on Smart Sensing and Intelligent Systems Vol. 8, No. 4, December 2015 1917.
http://dx.doi.org/10.1109/ccece.2015.7129493

Mohammad Ridwan Ismail, Mohd Khalid Awang, M Nordin A Rahman, Mokhairi Makhtar, (2015) A Multi-Layer Perceptron Approach for Customer Churn Prediction, International Journal of Multimedia and Ubiquitous Engineering, 10 (7), 213-222.
http://dx.doi.org/10.14257/ijmue.2015.10.7.22

Dr. Mrinal Kanti Das, (2016) Developing Payout-Loyalty Matrix for Telecom Retailers – A Study on Murshidabad and Nadia districts, Social Science Electronic Publishing (SSRN).
http://dx.doi.org/10.2139/ssrn.1342235

Dr. Hurmaz D. Patel, Dr. Mehul P.Desai, (2016) A Study on Relationship between Customer Satisfaction, Customer Loyalty and Intention to switch from one bank to another bank surat ciy, Adarsh Journal of Management Research - Vol. : 9 Issue : 1
http://dx.doi.org/10.21095/ajmr/2016/v9/i1/103771

Ghatasheh, N., Najdawi, A., Abu-Faraj, M., Faris, H., Corporate e-Learning Environment Using Concept Maps: a Case Study, (2013) International Review on Computers and Software (IRECOS), 8 (11), pp. 2655-2662.

C. Rolland, C. Ben Achour, C. Cauvet, J. Ralyté, A. Sutcliffe, N-A. M. Maiden, M. Jarke, P. Haumer, K. Pohl, E. Dubois and P. Heymans, (1998) A Proposal for a Scenario Classification Framework, Requirements Engineering Journal (REJ), Vol. 3, N°1, pp. 23-47.
http://dx.doi.org/10.1007/bf02802919

M. Jarke, K. Pohl, (1993) Requirements Engineering: An Integrated View of Representation, Process and Domain, In Proc. of the 4th European Software Conference, Springer Verlag.
http://dx.doi.org/10.1007/3-540-57209-0_8

M. Jarke, J. Mylopoulos, J.M. Schmidt, Y. Vassiliou, (1992) DAIDA - An Environment for Evolving Information Systems, ACM Trans., In Information Systems, vol. 10, n° 1.
http://dx.doi.org/10.1145/128756.128757

C. Rolland, (1997) A primer for method engineering, proceeding of INFORSID’97, Toulouse, France.
http://dx.doi.org/10.4000/adlfi.10920

Katarína Belanová, (2012) “What-if” Analysis In Investment Decision Making.
http://dx.doi.org/10.5817/fai2012-3-1

Rahul Singh, Prashant Shenoy, Maitreya Natu, Vaishali Sadaphal and Harrick Vin, (2013) Analytical Modeling for What-if Analysis in Complex Cloud Computing Applications, ACM/IFIP/USENIX 12th International Middleware Conference.
http://dx.doi.org/10.1145/2479942.2479949

Christian Bird, Thomas Zimmermann, (2012) Assessing the value of Branches with What-if Analysis, ACM 978-1-4503-1614-9/12/11…$15.00.
http://dx.doi.org/10.1145/2393596.2393648

Daniel Deutch, Zachary Ives, Tova Milo, Val Tannen, (2013) Caravan: Provisioning for What-If Analysis.
http://dx.doi.org/10.1145/2389241.2389249

Herodotos Herodotou, Shivnath Babu, (2011) Profiling, What-if analysis, and Cost-based Optimization of MapReduce Programs, Proceedings of the VLDB Endowment, Vol. 4, No. 11.
http://dx.doi.org/10.14778/2350229.2350239

D. Selvaraj, R.Dhanasekran, (2013) AReview on current MRI brain tissue segmentation, feature extraction and classification techniques, (IJECIERD) Vol. 3, Issue 5, 2013, Dec 2013, 11-30
http://dx.doi.org/10.1016/j.neucom.2012.06.021

Judith, J., Jayakumari, J., Enhanced Distributed Text Document Clustering Based on Semantics, (2013) International Review on Computers and Software (IRECOS), 8 (10), pp. 2545-2551.

Haridas, K., Thanamani, A., An Efficient Image Clustering and Content Based Image Retrieval Using Fuzzy K Means Clustering Algorithm, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 147-153.
http://dx.doi.org/10.15866/irecos.v9i1.1044

K. A. Abdul Nazeer, M. P. Sebastian (2009) Improving the Accuracy and Efficiency of the k-means Clustering Algorithm, Proceedings of the World Congress on Engineering 2009 Vol I.
http://dx.doi.org/10.1109/csie.2009.813

M.Esmaeili, M.Osanloo, F.Rashidinejad, A. A.Bazzazi, M.Taji, (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with computers, Vol. 30 n. 4, pp. 549-558.
http://dx.doi.org/10.1007/s00366-012-0298-2

Jan Adamowski, Hiu Fung Chan, Shiv O. Prasher, Bogdan Ozga-Zielinski, Anna Sliusarieva, (2012) Comparision of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water resources research.
http://dx.doi.org/10.1029/2010wr009945

Colin Cameron, Frank A.G. Windmeijer, (1995 ) An R-squared measure of goodness of fit for some common nonlinear regression models,
http://dx.doi.org/10.1016/s0304-4076(96)01818-0

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