Consumer satisfaction versus churn in the case of upgrades of 3G to 4G cell networks Steven D’Alessandro & Lester Johnson & David Gray & Leanne Carter # Springer Science+Business Media New York 2014 Abstract The current use of 3G technologies has created significant demands for capacity, such as cell TV, and this needs to be balanced with the capital constraints of many firms. Providers face price pressures on margins and the need to update cell networks to 4G in the post-GFC era where capital is scarce. Understanding consumer behavior in this area by use of simulations may be a time- and cost-efficient method, but how accurate is it? This study demonstrates that the use of a simple, agent-based model can lead to accurate initial prediction of parameters of satisfaction with a cell phone provider, and provides a basis of understanding factors of cell phone subscriber choice in the context of the introduction of new technology. Keywords Simulations . Netlogo . Mobile phone networks . 3G versus 4G choice . Provider choice models . Triangulation of models 1 Introduction Simulation in marketing is an innovative and cost-effective means to understand complex, time-dependent consumer behavior, by simplifying decision rules of actors Mark Lett DOI 10.1007/s11002-014-9284-3 S. D’Alessandro (*) : L. Johnson School of Management and Marketing, Charles Sturt University, Panorama Avenue, Bathurst, NSW 2795, Australia e-mail: [email protected]L. Johnson e-mail: [email protected]L. Johnson Melbourne Business School, Charles Sturt University, Panorama Avenue, Bathurst, NSW 2795, Australia D. Gray : L. Carter Department of Marketing and Management, Macquarie University, North Ryde, NSW 2109, Australia D. Gray e-mail: [email protected]L. Carter e-mail: [email protected]
Transcript
Consumer satisfaction versus churn in the caseof upgrades of 3G to 4G cell networks
Steven D’Alessandro & Lester Johnson & David Gray &
Leanne Carter
# Springer Science+Business Media New York 2014
Abstract The current use of 3G technologies has created significant demands forcapacity, such as cell TV, and this needs to be balanced with the capital constraintsof many firms. Providers face price pressures on margins and the need to update cellnetworks to 4G in the post-GFC era where capital is scarce. Understanding consumerbehavior in this area by use of simulations may be a time- and cost-efficient method,but how accurate is it? This study demonstrates that the use of a simple, agent-basedmodel can lead to accurate initial prediction of parameters of satisfaction with a cellphone provider, and provides a basis of understanding factors of cell phone subscriberchoice in the context of the introduction of new technology.
Simulation in marketing is an innovative and cost-effective means to understandcomplex, time-dependent consumer behavior, by simplifying decision rules of actors
Mark LettDOI 10.1007/s11002-014-9284-3
S. D’Alessandro (*) : L. JohnsonSchool of Management and Marketing, Charles Sturt University, Panorama Avenue, Bathurst, NSW2795, Australiae-mail: [email protected]
and allowing for interactions which mimic marketplace phenomena (Ma and Büschken2011; McIntyre and Miller 1992; Neslin and Schneider Stone 1996; Ross et al. 2000).Compared to survey, experimental, and test market research, simulations have thepotential to provide much quicker insights into marketplace behavior at significantlylower costs. One popular and easy to program simulation is Agent-Based Modeling(ABM).
ABM as one type of simulation in marketing has been shown to be useful inshowing the dynamics and complexity of markets and networks. Rand and Rust(2011, p185) list application areas suitable for ABM as consisting of diffusion ofinformation and innovations, retail location decisions, inter-firm relationships, strategyand competition, marketing mix models and retail, and servicescape design. ABMsimulations can also simplify what appears to be multifaceted and novel consumerbehavior. Goldenberg et al. (1999) note that many new innovations and product choicesin markets can be modeled accurately with as few as six parameters.
Yet the acceptance of ABM in published marketing research has been lacking. AsRand and Rust (2011, p182) note:
Critiques of agent-based modeling often come from two points of view: oneviewpoint is that ABM does not deal with real data and is therefore only for “toyproblems”, while another viewpoint is that most agent-based models have somany parameters that they can fit any data and are thus nothing more than“computer games.”
This paper provides an example of a “middle path,” between these two concernsabout ABM. It develops an ABM based on literature which focuses on three importantconsumer constructs in cell provider choice: coverage, price, and service. It thenexamines the evidence for accuracy for this approach, based on longitudinal surveyresearch. We find that an ABM with only three parameters can accurately predict theparameters of coverage, customer service, and price and provide important guidelinesinto the introduction of upgraded networks, which cannot always be modeled in surveyresearch. The ABM also provides reasonable accuracy of results from longitudinalsurvey research at a much lower cost and in a shorter space of time. Recommendationsare then provided as to how the ABM process can inform and be improved bytriangulation with real-world data, so that it does not focus on only “toy problems.”
2 Literature review
2.1 Research on customer switching as it relates to cell provider choice
A number of models have been developed to describe the customer switching process(Colgate and Hedge 2001; Colgate and Lang 2001; Keaveney 1995; Lees et al. 2007;Levesque and McDougall 1993; Bansal and Taylor 1999). The most general, theKeaveney model for customer switching, is based on an analysis of 800 criticalincidents that lead to customer dissatisfaction and then to customer switching across45 service industries. It identified eight major factors behind service provider customerexit: pricing, inconvenience, core service failure, service encounter failure, response to
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service failure, competition, ethical problems, and involuntary switching. Building onthis research, Chuang (2011) developed what he called a “pull and suck” model toexplain changing cell providers in Taiwan. Consumers were found to switch providersbecause of better service and coverage but were likely to remain with a current providerbecause of good service, switching costs, and habit. Also, ease of use and cell phoneservice quality have been shown to predict network demand (Wahab et al. 2010).
Other studies suggest that there are two main factors which influence the use ofone type of cell phone network over another: access to new technology and price.Iyengar et al. (2008) in a conjoint study found that choice probabilities and usagelevels of cell product features depended largely on price. The authors then used theseestimates to evaluate the expected revenues and profits of alternative plans and pricingschemes. Other studies suggest that it is the experience of product quality, level ofservice charges, level of call charges, and level of satisfaction with the serviceprovider that predict network use (Goode et al. 2005). Deng et al. (2010) suggestthat trust, perceived service quality, and perceived customer value, including func-tional value and emotional value, contribute to generating customer satisfaction withnetwork providers.
There is evidence that the behavior of peers has an influence on “churn,” or leaving anetwork provider of other customers (Dierkes et al. 2011). Moreover, the degree ofconsumer confusion over the myriad of marketing offers leads them to rely on verysimple strategies such as seeking word of mouth information or advice (Turnbull et al.2000). This implies that decision rules about network choice, from a consumerstandpoint may be interactive and be derived from simple rules or heuristics thatgovern choice.
Mah (2004), in a case study of the introduction of the 3G network for Hutchinson,noted that introducing technology had significant advantages in terms of demand,particularly if this was coupled with a low price strategy on the existing network.The challenge then for many managers and marketers is to model how these factorsinteract in real time so that decisions can be made across a number of possible futurescenarios (new technology and new price competition) as existing research methodol-ogies rely on future forecasting using past data. These scenarios also need to bepresented to a manager in useable format, with the option of providing tests of futuremarketing strategies. The review therefore suggested that the development of a simu-lation of cell provider choice should include price, desire for new technology (in thiscase better bandwidth offered by 4G), and customer service. While there is theoreticalsupport for the development of a model based on these constructs, it is also important toexamine the accuracy simulations have had in recent times in predicting actual con-sumer behavior.
2.2 The accuracy of simulation models in marketing
Marketing theory and practice have over the last several decades developed a numberof brand choice models or simulations (Grahn 1969; Dodds 1973; Winer 1986;Labeaga-Azcona et al. 2010; Lee et al. 2011). This research has focused on frequentlypurchased fast moving consumables and has used large scanner-based datasets tomodel choice, based on behavioral characteristics and the impact of the firm’s market-ing strategy.
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Discrete choice models can also be seen as another type of simulation. These haveincluded behavioral specifications of latent class choice models, multinomial probit,hybrid logit, and non-parametric methods (Ben-Akiva et al. 1997). Experiments usingsimulated data in discrete choice models have been evaluated with later survey research(Ma and Büschken 2011; Wedel et al. 1999). Research in ABMs though has not yetdeveloped such a rigorous approach to validation and has tended to rely on puresimulations in marketing and business without resort to the same attention to empiricalvalidation (Garcia 2005; Rand and Rust 2011; Goldenberg et al. 2009). Notableexceptions are Garcia et al. (2007) and Goldenberg et al. (2002), who have usedempirical datasets not only to examine accuracy but to improve ABMs. This paperfollows a similar path and shows how ABMs need to be validated by external data andhow this process also increases the understanding of marketing phenomena, such aschanging cell providers.
3 Method
The method consisted of two major steps: the development of the simulation usingNetlogo (an agent-based modeling computer program), followed by the evaluation ofthis model by examination of similar parameters in survey research.
3.1 Development of an agent-based model using Netlogo
In this case, we developed a simulation, ABM using Netlogo (http://ccl.northwestern.edu/netlogo/) which was a full factorial design experiment. This represented thefollowing experimental conditions, a 2 (“FourG”; 5 %, 10 %, of coverage)×2(“Price3G”; $10, $20, per month]×2 (“Price4G”; $10, $20, per month)×2(“capacity”; five, ten users per cell)×2 (“tolerance”; 0, 1), or 32 experimentalconditions. The last experimental condition, “tolerance,” was meant to capture afirm’s efforts at increased customer service, which hopefully helps build tolerance ofnetwork capacity constraints and price in the short term. For each experimentalcombination, Netlogo simulated 200 time periods. Each run of the model contained apopulation of 500 agents.
Outcome measures included the percentage of satisfied customers, loss of cus-tomers, and the mean use of 3G and mean use of 4G. Satisfied customers were a countof those agents who had achieved the highest bandwidth at the lowest cost. Loss ofcustomers refers to the number of agents not present at the end of each run of themodel. Means of 3G and 4G were the average number of agents in a particular cell,adjusted for capacity. These were measured after each time period.
Netlogo is described by its designers as a “multi-agent programmable modelingenvironment,” where rules of behavior of agents (Netlogo calls these “turtles”) canrepresent consumers in a marketplace. These can be programmed using a set ofrules which may be deterministic or “learnt” over time. Actions of agents usuallycan be traced by their position in the “world,” or a set of “patches,” which canrepresent a geographic boundary or in this case a use of a particular product ortechnology.
The Netlogo model used in this study consisted of the following parameters:
Network4G—Coverage of 4G cell access points (5 %, 10 %)—Represented by BlackPatches which had a bandwidth of 2003G—Remaining cell access points, or Represented by Green Patches with abandwidth of 120Capacity of each cell (3G and 4G): number of agents (five or ten consumers per cell).PricePrice of 4G ($10–$20 per month)Price 3G ($10–$20 per month)Consumers (Agents)Overall tolerance (of bad service)—(0–1). This modeled the importance of cus-tomer service by network providers.Each agent (consumer) had a random normal distribution of the importance ofservice (bandwidth preference or choice of 4G over 3G).Each agent (consumer) also had random normal distribution of the importance ofprice, both for 3G and 4G networks.The agents as shown by the faces in Fig. 1 start in a random position, in anunsatisfied state, and search (move) for a patch (Black 4G or Green 3G) whichmaximizes their access to bandwidth but also minimizes the price they pay. Agentswere also assigned a random distribution of bandwidth required and price theywere willing to pay for that bandwidth.Any agent (consumer) who finds the optimal mix of bandwidth and price stops atthat patch (green for 3G and black for 4G, each at respective prices) and issatisfied, and this is shown in the model by the face changing from a sad gray toa happy white. Unsatisfied consumers continue to search until they find theiroptimal choice and are shown in the observer window in Fig. 1 as an unhappyface. Unsatisfied consumers are shown in the model interface as sad gray faces.After a period of time (12 ticks, which simulates a 1-year telecommunicationscontract), consumers who are unsatisfied leave the network providers; this issimulated in the model by the agents who “die” or leave the Netlogo representationof the market. This simulated the effect of being tied to a contact of 12 months andnot being able to switch to another provider until that contract ended.
Fig. 1 The Netlogo observer window
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Agents or consumers with a higher tolerance are less likely to leave the marketbecause of expensive prices and/or not being able to access new technology (4G)which has a higher bandwidth. The model presented in this paper is quite simple,and the agents’ do not “evolve” consumer decision rules over time as is the casewith other ABM studies dealing with online shopping behavior (Ahn 2010). Thismay well be an avenue for future research or the development of a more complexmodel once the first principles of the marketplace can be accurately modeled.The Netlogo window as shown, in Fig. 1, allows decision makers to manipulate anumber of variables and observe the outcomes in real time. The software alsoallows an experiment to be run on what is the effect of different combinations ofthese user inputted variables and to record the outcomes of the model.
3.2 Comparison study
In order to examine the accuracy of the Netlogo model, a similar but more detailedsurvey study on provider choice was conducted. The data used were longitudinal, sothat the accuracy of the simulation in terms of both satisfaction and switching behaviorcould be examined.
3.2.1 Sample and procedure
The sample used to examine the accuracy of the simulation parameters was from alarger study which used longitudinal survey data on cell provider switching andconsisted of two waves, 11 months apart. At stage 1, 1,600 respondents wereinterviewed with the response rate at 71 %. Participants who took part in stage 1 werecontacted by the panel provider and invited to take part in a stage 2 study. Some 971participants, or 61 %, of the original sample agreed to take part in the follow-up (stage2). The survey sample encompassing both time periods was collected from a nationalonline panel and consisted of a roughly even gender balance males 52 % and females48 %. There was close to a uniform spread of age groups with 47.5 % being less than44 years of age. Roughly, 7.5 % had a college degree, and there was a spread ofdemographics in the sample; for example, 15 % of respondents worked in a blue collarindustry, and 40 % of those surveyed were married with dependent children. Surveyrespondents are ambivalent toward their current cell service provider in respect ofsatisfaction. About a third, 33.2 %, were satisfied with their current provider, and aquarter, 24.9 %, were at least highly satisfied.
3.2.2 Measurement
In order to measure satisfaction with the provider, items were adapted from Aydinand Özer (2005), including overall satisfaction with the provider, satisfaction withcoverage, cost, customer service, and overall contract terms. These were measuredon a seven-point scale from 1 (highly dissatisfied) to 7 (highly satisfied). Toexamine whether actual experienced events influenced the decision to switch pro-viders, these measures were also included as dichotomous variables in the secondwave of the study.
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An analysis of reasons participants listed for switching providers bears someresemblance to variables in the ABM. About 44 % of respondents indicated that theywould be likely to switch providers based on poor coverage, 30.4 % listed dissatisfac-tion with monthly costs as a reason to switch, and 27.7 % saw poor service as a triggerto switch cell providers (note: multiple responses were allowed).
4 Results
The analytical procedure consisted of two stages. In the first, there was an analysis ofvariance of the outcome of experimental runs of the Netlogo simulation. In the second,there was a comparison of regression results from the Netlogo simulation and surveyresearch.
In order to identify significant model conditions, an ANOVA of simulation resultswas conducted. For the sake of brevity, only significant ANOVA results are shown inTable 1. As can be seen from the results, the number of 4G access points, capacity ofeach network type, the price of 3G, and tolerance seemed to influence the number ofsatisfied consumers and the mean use of technology (4G and 3G). Loss of customers isdetermined mainly by lack of tolerance of bad service (F=69.57, p<.01, 2=0.07), or inother words, customer service can be used to reduce churn. There are a number ofinteractions, but the effect sizes are small, with eta-squared ( 2) being around 0.05 to0.01. Implications for providers are that planning for capacity is important, but you donot have to provide access to all consumers. The price of the old technology is moreimportant than the price of the new technology. Good customer service is importantbecause it increases tolerance and prevents churn.
Two types of dependent measures were compared in the regression results. One forsatisfaction (the percentage satisfied in the ABM and satisfaction in the survey study)and then switching behavior (churn, or loss of turtles or actors in the ABM andchanging service providers in the survey study). An ordinary least squared regressionwas used in the first instance to compare the effectiveness of the ABM to predictconsumer attitudes, and a binary logistic regression was used to compare predictedbehavior of the ABM with that in the survey research. The dependent variable wascoded in both studies of behavior as 1 (switched provider) and (0 did not changeprovider). In order to provide a direct comparison between the ABM and real-worlddata, the results focused on factors common to both approaches.
A regression analysis in Table 2 compares the outcome of the ABM results withthose from survey research. There is a similarity of the importance of coverage (oraccess to 4G in the simulation) in predicting satisfaction (β=0.28, p<.01) in thesimulation and also survey research (β=0.25, p<.01). As can also been seen, theimpact of overall customer service match closely the results from fieldwork. That is,good customer service is similarly associated with a higher count of satisfied agents(β=0.29, p<.01) and with attitudinal satisfaction with the current cell service provider(β=0.34, p<.01) in the survey. Satisfaction with monthly cost in the survey (β=0.23,p<.01) compares well with that in the simulation, where higher costs were associatedwith a less number of satisfied agents (β=−0.35, p<.01), hence the negative regressioncoefficient in the ABM results compared to the survey results. The survey research alsoshowed that satisfaction with contract terms had a significant effect on overall
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satisfaction (β=0.21, p<.01). The survey does a better job of prediction of satisfactionR2=0.39, at much higher cost and longer time frame than the ABMmodel where the R2
for the number of satisfied agents is 0.29. Note that a direct statistical comparison of thebetas obtained from each method is not appropriate, since different measures both ofindependent and dependent variables were used. Rather, the results can be used todetermine if the pattern of betas obtained from the model is similar to that from surveyresearch. An example is the first line of independent variables of Table 2, which has 4Gcoverage, which was a percentage of coverage in the Netlogo model, with an attitudinal
measure of adequate coverage in the survey research. These are similar but differentconcepts. The dependent variable is also different across the two studies, withsatisfaction in the Netlogo model being a count of satisfied agents, while in thesurvey research this is an attitudinal measure.
Table 3 shows the overall results in terms of churn in the ABM with switchingbehavior in longitudinal survey research. Again, poor service is a predictor of loss ofcustomers (agents) in the ABM (β=−0.23, p<.01) and if experienced was a significantfactor in switching cell phone providers in the survey (β=0.68, p<.01). The ABMmodel did not find support for switching because of poor coverage as was the case inthe survey respondents in the survey (β=0.26, p<.01). This may have well occurredbecause the survey respondents were addressing overall coverage issues rather thanaccess to new technological networks. The survey research also showed that frustrationwith contract terms (β=0.68, p<.01) was a major driver of switching cell serviceproviders. Because contract terms were not modeled in the simulation, this may explainin part the better prediction of overall switching behavior in the survey (pseudo R2=
Table 2 Regression results: Netlogo simulation and survey research for satisfaction with cell phone provider
Netlogostandardized beta
Survey researchstandardized beta
Independent variable
4G coverage/Coverage 0.28** 0.25**
Price of 3G/Monthly cost −0.35** 0.23**
Tolerance/Customer service 0.29** 0.34**
Capacity 0.08** n/a
Overall contract terms n/a 0.21**
Dependent variable R2=0.29 Number ofagents satisfied
R2=0.39 Satisfaction withcurrent supplier
With the independents, the first term refers to simulation parameters, the second to those in survey research
**p<.01
Table 3 Binary logistic regression results for Netlogo and survey research
Netlogostandardized beta
Survey research log-linearstandardized beta
Independent variables
4G coverage/coverage −0.02 0.26**
Price of 3G/monthly cost 0.01 0.10
Contract terms n/a 0.68**
Tolerance/customer service −0.23** 0.57**
Dependent variable Churn = loss of agents Switched to another provider
Pseudo R2 0.06 0.13
With the independents, the first term refers to simulation parameters, the second to those in survey research
**p<.01
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0.13) than with the ABM (pseudo R2=0.06). These low values show the difficulty inboth simulations and survey research to accurately predict behavior.
5 Discussion
This paper outlined the development of a simulation model using Netlogo, which caninform management decision making with respect to investment in upgrading cellphone networks by a basic understanding of consumer behavior, of satisfaction, andto some extent of choice of cell service provider. Importantly, it was shown that asimple model could represent reality in some circumstances and that empirical researchcan help expand as well as validate original conclusions from a simulation, somethingthat also been found in other modeling studies (Garcia et al. 2007; Goldenberg et al.2002).
Simulation results show that there is an important interaction between prices ofdifferent technologies, and that pricing in the use of existing technology (in this case3G) is much more important than pricing of new, possibly more expensive technol-ogy. It is also not necessary to provide access to all consumers of new technology;only a fraction needs access for there not to be significant churn or loss of business.When compared to survey research, the model matches well the importance ofcoverage, service, and somewhat price with respect to satisfaction. Importantly, thereis a need for a relationship marketing strategy, which does not have to be extensive inorder to be effective in preventing churn, as this will increase in the short term thetolerance of consumers. Care needs to be taken that simulations like ABM may notmodel accurately all facets of actual behavior. Nevertheless, ABMs can provide initialestimates of parameters without resort to more expensive and time-consuming surveyresearch. It should also be remembered that survey results which aim to predictbehavior can only be provided at best contextualized predictions (Feldman andLynch 1988). That is, cognitive reasons for switching cell phone providers may onlybe activated when there is a clear motive to do so (the customer is nearly out ofcontract for example). It is also possible that in providing consumers possible reasonsfor switching providers, this may, according to Feldman and Lynch (1988), influenceconsumers to select these same reasons, because they were measured rather than theactual reasons for switching.
ABMs should therefore not be considered as an endpoint but rather part of a two-way process of understanding the marketplace; they may well model some but not allconsumer characteristics. Also, findings from qualitative and quantitative research maybe fed back into an ABM to improve long-term predictions on behavior.
ABMs can be used to generate understanding about the marketplace by providingpointers to important aspects of consumer behavior to be studied in larger studies priorto the introduction of new technology (Garcia 2005; Bohlmann et al. 2010). We maynot know the impact of 4G technology, for example, until it is introduced in sufficientvolume in a marketplace. ABMs such as Netlogo at least allow a manager to provide a“best guess” of what the possible impact of technology will be, without the need toconduct extensive and expensive test markets, which delay managerial decision mak-ing. Also, results from beta testing, even at a qualitative level, can be used to developprototype ABMs which can be later calibrated with survey research.
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Simulations like ABM, which are straightforward to program, allow the managerand researcher to examine a range of scenarios, in this case price and network capacity.Care needs to be taken that results are triangulated with follow-up survey research.Findings from survey research can be used to develop more accurate and valid models.In this case, the survey research suggests that frustrations with contract terms should beincluded in any simulations of cell provider choice.
Importantly, simulations like Netlogo are a simple means to allow the manager orresearcher to examine a range of scenarios (32 were studied in this paper) and to look athighly unusual events like a “market failure.” This was simulated in a trial where a lowcapacity and low price for 3G networks led to unsatisfied consumers leaving themarketplace so that only a reduced number were left served by the 4G network, acondition easily dealt with by a slight increase in consumer tolerance or greatercustomer service. Interestingly, without attention to customer service, this type ofsituation may well be faced by network providers who cut costs for high accessconsumers but cannot provide sufficient capacity for the high bandwidth customers(Poynter 2006).
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