Aim of Customer Loyalty Analysis is detecting suitable strategies to make loyal a customer. Like in Customer Satisfaction Analysis PLS-Path Modeling (PLSPM) is a suitable technique. As PLS-PM assumes homogeneity over population and customers\u2019 behaviors are different about loyalty, a PLS-Path model should be run in each homogeneous subgroup. Nevertheless these groups are not known. Then two way are possible: to use an a priori marketing segmentation or to adopt a model-based segmentation. The paper presents a short reflection on these different approaches

Chirico, P.

LO PRESTI, A.

English

Aim of Customer Loyalty Analysis is detecting suitable strategies to make loyal a customer. Like in Customer Satisfaction Analysis PLS-Path Modeling (PLSPM) is a suitable technique. As PLS-PM assumes homogeneity over population and customers’ behaviors are different about loyalty, a PLS-Path model should be run in each homogeneous subgroup. Nevertheless these groups are not known. Then two way are possible: to use an a priori marketing segmentation or to adopt a model-based segmentation. The paper presents a short reflection on these different approaches

P. CHIRICO

A. LO PRESTI

Archivio Istituzionale della Ricerca- Università del Piemonte Orientale

Customer Loyalty Analysis in an heterogeneousmarket: a comparison between a priorisegmentation and model-based segmentationPaolo Chirico, Anna Lo PrestiAbstract Aim of Customer Loyalty Analysis is detecting suitable strategies to im-prove the loyalty of customers. Like in Customer Satisfaction Analysis PLS-PathModeling (PLS-PM) is a suitable technique. As PLS-PM assumes homogeneity overpopulation and customers’ behaviors are different about loyalty, a PLS-Path modelshould be run in each homogeneous subgroup. Nevertheless these groups are notknown. Then two way are possible: to use an a priori marketing segmentation or toadopt a model-based segmentation. The paper presents some considerations aboutthese different approaches.Key words: Customer Loyalty, Market Segmentation, PLS-Path Modeling1 IntroductionIf acquiring new clients is usually more onerous than maintaining the actual ones(addressing to them suitable actions), it becomes even more difficult when the mar-ket is in a recession phase. Therefore, taking care of clients adopting efficient loyaltypolitics becomes even more important in periods of doldrums - as the present one -than in expansion ones.Nevertheless, the identification of appropriate strategies of Client Loyalty (CL)cannot leave aside from a right comprehension of the psychological processes un-derlying the client loyalty toward a brand or a provider. What does the client takeinto account when he decides to repurchase a product or to renew the use of a ser-Paolo ChiricoDip. Statistica e Matematica Applicata, Universita’ degli Studi di Torino, e-mail:paolo.chirico@unito.itAnna Lo PrestiDip. Statistica e Matematica Applicata, Universita’ degli Studi di Torino e-mail:anna.lopresti@unito.it12 Paolo Chirico, Anna Lo Prestivice? What can induce him to choose a different brand or to switch the provider?These are some of the questions which the marketing managers have to answer to.CL models try to find suitable answers to the questions (see 2). The parameter es-timations of these models allow us to evaluate how much some factors, consideredoutstanding, act on client loyalty and, consequently, suggest us the more suitablepolicy of CL. Let us suppose, for example, that the CL model identifies the eco-nomic convenience of the offer as the most important factor affecting the client loy-alty toward a given brand. Then, to maintain the clients loyal toward the brand, themarketing manager will have to keep under control their perception of convenienceof the brand.Nevertheless, reasonably loyalty behavior of the clients cannot be considered ho-mogeneous. So, some clients could consider more important the convenience whileothers could give more importance to the perception of trust (quality and reliability)suggested by the brand. Again, other clients could have an innate predisposition tobe loyal, so their behavior doesn’t depend on the brand, but on their own way ofbeing. It is evident that a model indiscriminately estimated on the overall clienteledoesn’t get these particularities to emerge and finally would give an informationunhelpful to define appropriate policies of CL. Properly a single model should beestimated on each different segment. Nevertheless two orders of reasons make noteasy an a priori individualization of such segments:1. often the segments available to marketing manager is not suitable for the evalua-tion of Loyalty;2. the differences among the segments should be related to the different client be-haviors toward the brand, but these differences can be underlined only by the CLmodels.1.1 Marketing segmentation and Customer LoyaltyTypically, developing a marketing strategy, the segmentation should aim to defineclusters of clients that can be satisfied by a same product. Among these, the com-pany picks out those retained more interesting (targets) and offers a product or ser-vice developed to satisfy their specific requests. The variables used to this purpose,segmentation basis may concern the benefits requested (benefit segmentation), thepsychological profile (psychographic segmentation) or demographic characteristicsas sex, age or zone of residence (demographic segmentation) (see [7]). Such vari-ables typically are observed by ad hoc surveys.The demographic segmentation is the easier to carry out, but only in some con-texts it is useful to define segments of customers that desire the same product. Forthe same reasons it seems to be not able to define successful strategies of loyalty. Thebenefit segmentation is the more direct type of segmentation, even if its definitionneeds of an a priori clear idea about the possible benefits requested by the market.It is useful to define the product or service that should satisfy the clients (see [3]).In some contexts, individuals having the same requests (for example those that wantCustomer Loyalty Analysis in an heterogeneous market 3an economic city-car) could have the same behavior of loyalty (i.e. to give main im-portance to the economic convenience). In these cases such segmentation could besuitable to identify different loyalty behaviors. The psychographic segmentation isbased on the idea that individuals showing similar behavior require similar products(or services). In this segmentation the basis are behavioral variables related to thegeneral consumption; it is carried out on large population by specialized agencies,that generally give consultancy to the interested firms. This type of segmentationcan be helpful to analyze CL if behavioral variables pertaining to Loyalty are in-cluded among the basis. The following table summarizes the degree of utility of thethese kind of segmentation for CL analysis:Table 1 Segmentation utility for CLSegmentation CL usefulnessDemographic lowBenefit middlePsychographic high (in theory)2 PLS-Path Modeling in Customer Loyalty AnalysisFrom a modeling point of view, the analysis of the CL can be assimilated to CSanalysis of which the CL represented an extension at the beginning. Therefore, themost important CL modeling as well as in the case of CS, is the Structural Equa-tion Modeling (SEM). Such modeling consists of a main model (inner model) whichrelates latent (unobservable) variables that are associated to the constructs and defin-ing the phenomenon under study. The unobserved variables are indirectly measuredthrough a model based on directly observable variables (outer model). In SEM twoapproaches exist:• SEM-ML (Structural Equation Modeling-Maximum Likelihood), the hard mod-eling, that requires restrictive assumptions depending on the maximum likelihoodmethod;• PLS-PM (Partial Least Squares-Path Modeling), the soft modeling, that requiresless restrictive assumptions since the parameters estimation is based on partialleast square method.If segmentation in SEM-ML has been still achieved by Finite Mixture Modelsfrom several years (see [5]), segmentation in PLS-PM is more recent. We recall,among these, the following methods: Finite mixture Partial Least Squares (FIMIX-PLS, see [4] and [5]), Partial Least Squares Typological Path Modeling (PLS-TPM,see [6]) and REsponse Based Units Segmentation (REBUS-PLS, see [2]).In the following table 2 some features of these methods are reported; we remandto the references for a more in deep exposition.4 Paolo Chirico, Anna Lo PrestiTable 2 Features of the methodsf eatures\methods FIMIX-PLS PLS-TPM REBUS-PLSdistributional assumption yes no nodifferent outer models no no yesiterative algorithm no yes yesnumber of groups fixed a priori yes yes no3 Short ConsiderationsOn the basis of the features reported in table 2, both TPM-PLS and REBUS-PLSseem to be very interesting for CL-analysis (in particular REBUS-PLS seems to bethe most powerful). Nevertheless these methods are quite recent and their propertiesstill are not completely explored. Like all iterative methods they lead to a local op-timum, not necessary being the best partition. Moreover there is not a formal proofof their convergence, but only practical results. On the other hand the classic meth-ods of marketing segmentation are not oriented to CL analysis: they could turn tobe useful if variables regarding loyalty behavior were included in the segmentationbasis.References1. Esposito Vinzi, V., Lauro, N.C., Amato S.: PLS Typological Regression: Algorithmic, Clas-sification and Validation. In: Vichi, P. Monari, P., Mignani, S., Montanari, A. (eds.) New De-lovpments in Classification and Data Analysis, pp. 113-140. Springer Verlang, Berlin (2004)2. Esposito Vinzi, V., Trinchera, L., Squillaciotti, S., Tenenhaus, M.: REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling. Appl. Stochastic ModelsBus. Ind., 24, 439-458. (2008)3. Haley, R.I.: Benefit Segments: Backwards and Forwards. Journal of Advertising Research,feb-mar, 19-25. (1984)4. Hann, C., Johnson, M., Herrmann, A., Huber, F.: Capturing Customer Heterogenity using aFinite Mixture Approach. Scmalenbach Business Review, 54, 243-269. (2002)5. McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley and Sons, New York (2000)6. Squillacciotti, S.: Prediction oriented classification in PLS path modelling. In: Esposito Vinzi,V., Chin, W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares: Concept Meth-ods and Applications. Springer, Heidelberg (2009)7. Wedel, M., Kamakura, W.A.: Market Segmentation: conceptual and methodological founda-tions. Kluwert Academic Publications, Boston (1998)

Customer Loyalty Analysis in an heterogeneus market: a comparison between a priori segmentation and model based segmentation

Chirico, Paolo

LO PRESTI, Anna

Institutional Research Information System University of Turin

https://iris.uniupo.it/retrieve/handle/11579/92963/53824/2009_short_CLADAG.pdf

Customer Loyalty Analysis in an heterogeneus market: a comparison between a priori segmentation and model based segmentation

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