Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context.
GrimmVBergerUBastiansenFet al. A standard protocol for describing individual-based and agent-based models. Ecol Model2006; 198(1): 115–126.
2.
OzcanA.Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools. Lab Chip2014; 14(17): 3187–3194.
3.
VerbekeWDejaegerKMartensDet al. New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res2012; 218(1): 211–229.
4.
KeramatiAArdabiliSM.Churn analysis for an Iranian mobile operator. Telecomm Policy2011; 35(4): 344–356.
5.
HughesAMStrategic database marketing. New York: McGraw-Hill, 2006.
6.
JonesMATaylorVAReynoldsKE.The effect of requests for positive evaluations on customer satisfaction ratings. Psychol Mark2014; 31(3): 161–170.
7.
GaneshJArnoldMJReynoldsKE.Understanding the customer base of service providers: an examination of the differences between switchers and stayers. J Mark2000; 64(3): 65–87.
8.
BlodgettJGWakefieldKLBarnesJH.The effects of customer service on consumer complaining behavior. J Serv Mark1995; 9(4): 31–42.
9.
ItzkowitzJ.Customers and cash: how relationships affect suppliers’ cash holdings. J Corp Finance2013; 19: 159–180.
EpsteinMJWestbrookRA. Linking actions to profits in strategic decision making. MIT Sloan Manag Rev2001; 42(3): 39.
12.
BaumannCElliottGBurtonS.Modeling customer satisfaction and loyalty: survey data versus data mining. J Serv Mark2012; 26(3): 148–157.
13.
JohnsonMDHerrmannAHuberFet al. Customer retention in the automotive industry: quality, satisfaction and loyalty. New York: Springer Science & Business Media, 2012.
14.
FullertonG.The moderating effect of normative commitment on the service quality–customer retention relationship. Eur J Mark2014; 48(3/4): 657–673.
15.
Ribeiro SorianoDJyh-Fu JengDBaileyT. Assessing customer retention strategies in mobile telecommunications: hybrid MCDM approach. Manage Decis2012; 50(9): 1570–1595.
16.
KhanAAJamwalSSepehriM.Applying data mining to customer churn prediction in an internet service provider. Int J Comput Appl2010; 9(7): 8–14.
SilvaKAYapaSTWS. Customer retention: with special reference to telecommunication industry in Sri Lanka. In: Proceedings of the international conference on business and information, (BAI 2009), Kuala Lumpur, 6–8 July 2009, pp.1–15. Kuala Lumpur, Malaysia: Academy of Taiwan Information Systems Research (ATISR). 2013.
19.
SvendsenGBPrebensenNK.The effect of brand on churn in the telecommunications sector. Eur J Mark2013; 47(8): 1177–1189.
20.
ChuB-HTsaiM-SHoC.-S.Toward a hybrid data mining model for customer retention. Knowl-Based Syst2007; 20(8): 703–718.
21.
PhadkeCUzunaliogluHMendirattaVBet al. Prediction of subscriber churn using social network analysis. Bell Labs Tech J2013; 17(4): 63–75.
RichardM.-OChebatJ.-C.Modeling online consumer behavior: preeminence of emotions and moderating influences of need for cognition and optimal stimulation level. J Bus Res2016; 69(2): 541–553.
24.
VagA.Simulating changing consumer preferences: a dynamic conjoint model. J Bus Res2007; 60(8): 904–911.
Ottar OlsenSAlina TudoranABrunsøKet al. Extending the prevalent consumer loyalty modelling: the role of habit strength. Eur J Mark2013; 47(1/2): 303–323.
27.
XuMWaltonJ.Gaining customer knowledge through analytical CRM. Ind Manage Data Syst2005; 105(7): 955–971.
28.
NeslinSAGuptaSKamakuraWet al. Defection detection: measuring and understanding the predictive accuracy of customer churn models. J Mark Res2006; 43(2): 204–211.
29.
FurnessP.Techniques for customer modelling in CRM. Journal of Financial Services Marketing2001; 5(4): 293–307.
30.
HuangYKechadiT.An effective hybrid learning system for telecommunication churn prediction. Exp Syst Appl2013; 40(14): 5635–5647.
31.
JahromiATStakhovychSEwingM.Customer churn models: a comparison of probability and data mining approaches. In CampbellCMaJJ (eds) Looking forward, looking back: drawing on the past to shape the future of marketing. New York: Springer, 2016, pp.144–148.
32.
ZhangZWangRZhengWet al. Profit maximization analysis based on data mining and the exponential retention model assumption with respect to customer churn problems. In Data mining workshop (ICDMW), 2015 IEEE international conference, Atlantic, NJ, 14–17 November 2015, pp. 1093–1097. New York: IEEE.
33.
YeYLiuSLiJ.A multiclass machine learning approach to credit rating prediction. In Information processing (ISIP), 2008 international symposiums, Moscow, May 23, 2008, pp. 57–61. New York: IEEE.
34.
LemmensACrouxC.Bagging and boosting classification trees to predict churn. J Mark Res2006; 43(2): 276–286.
35.
HuangBKechadiMTBuckleyB.Customer churn prediction in telecommunications. Exp Syst Appl2012; 39(1): 1414–1425.
36.
BurezJVan den PoelD.Separating financial from commercial customer churn: a modeling step towards resolving the conflict between the sales and credit department. Exp Syst Appl2008; 35(1): 497–514.
37.
LeeCKwakNLeeC.Understanding consumer churning behaviors in mobile telecommunication service industry: cross-national comparison between Korea and China. In: Proceeding of the international conference on information systems, Fort Worth, TX, 2015. AIS.
38.
HeBShiYWanQet al. Prediction of customer attrition of commercial banks based on SVM model. Procedia Comput Sci2014; 31: 423–430.
39.
RajuPSBaiVRChaitanyaGK.Data mining: techniques for enhancing customer relationship management in banking and retail industries. IJIRCCE2014; 2(1): 2650–2657.
40.
SundarkumarGGRaviV.A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Eng Appl Artif Intell2015; 37: 368–377.
41.
MiguéisVLVan den PoelDCamanhoASet al. Modeling partial customer churn: on the value of first product-category purchase sequences. Exp Syst Appl2012; 39(12): 11250–11256.
42.
MiguéisVLCamanhoAe CunhaJF.Customer attrition in retailing: an application of multivariate adaptive regression splines. Exp Syst Appl2013; 40(16): 6225–6232.
43.
RichterYYom-TovESlonimN.Predicting customer churn in mobile networks through analysis of social groups. in Proceedings of the 2010 SIAM international conference on data mining 2010, Columbus, OH, 29 April 2010, pp. 732–741. Philadelphia, PA: Society for Industrial and Applied Mathematics.
ZhangXLiuZYangXet al. Predicting customer churn by integrating the effect of the customer contact network. In 2010 IEEE International conference on service operations and logistics and informatics (SOLI), Qing Dao, 15 July, 2010, pp. 392–397). New York: IEEE.
51.
RisseladaHVerhoefPCBijmoltTH.Dynamic effects of social influence and direct marketing on the adoption of high-technology products. J Mark2014; 78(2): 52–68.
52.
LuoQZhongD.Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tourism Manage2015; 46: 274–282.
53.
AndersonEW.Customer satisfaction and word of mouth. J Serv Res1998; 1(1): 5–17.
54.
KeaveneySM.Customer switching behavior in service industries: an exploratory study. J Mark1995; April: 71–82.
55.
HassounaMB. Agent based modelling and simulation: an examination of customer retention in the UK mobile market. PhD dissertation, Brunel University, School of Information Systems, Computing and Mathematics, UK, 2012.
56.
TwomeyPCadmanR.Agent-based modelling of customer behaviour in the telecoms and media markets. info2002; 4(1): 56–63.
EpsteinJM.Modelling to contain pandemics. Nature2009; 460(7256): 687.
60.
AdamiC.Introduction to artificial life, vol. 1. New York: Springer Science & Business Media, 1998.
61.
HeppenstallAJCrooksATSeeLMet al. Agent-based models of geographical systems. New York: Springer Science & Business Media, 2011.
62.
ZhangTZhangD.Agent-based simulation of consumer purchase decision-making and the decoy effect. J Bus Res2007; 60(8): 912–922.
63.
GilbertGN.Agent-based models. New York: Sage, 2008.
64.
RoozmandOGhasem-AghaeeNHofstedeGJet al. Agent-based modeling of consumer decision making process based on power distance and personality. Knowl-Based Syst2011; 24(7): 1075–1095.
65.
KennedyCTheodoropoulosGSorgeVet al. Aimss: an architecture for data driven simulations in the social sciences. In: Computational science: ICCS 2007, Beijing, China, May 27–30, 2007, pp. 1098–1105. New York: Springer.
66.
HassanSAntunesLPavónJ.Mentat: a data-driven agent-based simulation of social values evolution. MABS2009; 9: 135–146.
67.
AnL.Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol Model2012; 229: 25–36.
68.
GimblettHR.Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes. New York: Oxford University Press, 2002.
69.
DeadmanPRobinsonDMoranEet al. Colonist household decisionmaking and land-use change in the Amazon rainforest: an agent-based simulation. Environ Plan B2004; 31: 693–710.
70.
VaishnaviVKKuechlerW.Design science research methods and patterns: innovating information and communication technology. Boca Raton, FL: CRC Press, 2015.
71.
von AlanRHMarchSTParkJet al. Design science in information systems research. MIS Quarterly2004; 28(1): 75–105.
72.
MarchSTSmithGF.Design and natural science research on information technology. Decis Support Syst1995; 15(4): 251–266.
73.
NewellASimonHAHuman problem solving. Englewood Cliffs, NJ: Prentice-Hall, 1972.
74.
PeffersKTuunanenTRothenbergerMAet al. A design science research methodology for information systems research. J Manag Inf Syst2007; 24(3): 45–77.
75.
KuechlerBVaishnaviV.On theory development in design science research: anatomy of a research project. Eur J Inf Syst2008; 17(5): 489–504.
76.
HassounaMArzokyM. Agent based modelling and simulation: toward a new model of customer retention in the mobile market. In: Proceedings of the 2011 summer computer simulation conference, The Hague, Netherlands, June 27–30, 2011, pp. 30–35. Vista, CA: Society for Modeling & Simulation International.
77.
BarnesJGHowlettDM.Predictors of equity in relationships between financial services providers and retail customers. Int J Bank Mark1998; 16(1): 15–23.
78.
RokachLMaimonO.Data mining with decision trees: theory and applications. Singapore: World Scientific Press, 2014.
79.
KotsiantisSB.Decision trees: a recent overview. Artif Intell Rev2013; 39(4): 261–283.
80.
TsaiC.-FLuY.-H.Data mining techniques in customer churn prediction. Recent Pat Comput Sci2010; 3(1): 28–32.
81.
DudaROHartPEStorkDG.Pattern classification. Hoboken, NJ: John Wiley & Sons, 2012.
82.
FriedmanJHastieTTibshiraniR.The elements of statistical learning, vol. 1. Berlin: Springer, 2001.
83.
NisbetJElderRMinerG.Handbook of statistical analysis and data mining applications. London: Academic Press, 2009.
84.
IglesiasCAGarijoMGonzálezJC.A survey of agent-oriented methodologies. In SinghMPRaoASMullerJP, Intelligent agents V: Agents theories, architectures, and languages. New York: Springer, 1999, pp. 317–330.
85.
BellDKashefiASalehNet al. A data-driven agent based simulation platform for early health economics device evaluation. In Proceedings of the agent-directed simulation symposium, Pasadena, CA, 3 April 2016, p. 5. Vista, CA: Society for Computer Simulation International.
86.
HanJKamberMPeiJ.Data mining: concepts and techniques. Amsterdam: Elsevier, 2011.
87.
DasguptaKSinghRViswanathanBet al. Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th international conference on extending database technology: advances in database technology, Nantes, 25 March 2008, pp. 668–677. New York: ACM.
