POTENTIAL CUSTOMER TYPOLOGY FOR ENTRANCE ON POWDERY PAINTING MARKET. Vojtěch SPÁČIL

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1 POTENTIAL CUSTOMER TYPOLOGY FOR ENTRANCE ON POWDERY PAINTING MARKET Vojtěch SPÁČIL Eonomcá faulta, VŠB-TU, Soolsá třída 33, Ostrava, Česá republa, Abstract The contrbuton s based on case study. Local powdery pantshop was orgnally ntended for mother s company. The necessty to buld new modern faclty has called out the need for demand from external envronment. Pantshop as a newcomer should enter the hghly compettve maret. The potental customers have been namely recruted from metal processng companes and companes from car ndustry whch produce on large seres. Dmensons of sem-fnshed products and dstance of potental customer domcle from pantshop ste (maxmum s 100 m because of logstc reasons) were other restrctve condtons. Based on these lmtatons exploratory research was conducted. 100 qualfed companes were ased about ther behavour and preferences. Research goal was to defne the most sutable type of potental customers for buldng busness contact. Data analyss ncluded hypothess testng and cluster analyss. Keywords: powdery pantng, pantshop, metal processng companes, new maret entrance, potental customer typology 1. INTRODUCTION Frms are always pursung opportuntes to grow by enterng new marets. Whether or when to enter a new maret or ndustry s one of the crtcal decsons that enterprses and busness executves must often mae. The choce of a mode of maret entry s a crtcal component of the strategy, and numerous emprcal studes have focused on ths topc. The mode of maret entry depends on ndustry entry barrers and buyng behavour of ncumbent companes. 2. THEORETICAL BACKGROUND 2.1 Barrers to entry The most drect threat to exstng rvals comes from busness unts that wsh to partcpate n sales and profts n an ndustry. New rvals may come from the domestc or the foregn front and from adjacent or unrelated ndustres. Such newcomers are seldom welcome, yet they often a benefcal mpact [1]. Nevertheless an ncumbent frm may ntentonally allow entry of a wea frm to stop entry of a strong frm. In other words, an ncumbent frm that s confronted by a strong entrant may welcome a wea entrant [2]. Barrers to entry n a maret are one of the ey factors that researchers consder n the maret entry decson [8]. Maret entry barrers are defned as any factors that reduce the motvaton or ablty of potental entrants to enter a newmaret despte the hgh profts enjoyed by the early poneers n that maret [3]. Maret entry barrers vary accordng to the maret structure n that maret barrers are hgher n pure monopoly and tght olgopoly marets than n pure competton marets [4]. Many studes nvestgatng maret entry barrers mplctly assumed equal percepton of entry dffcultes by ndustry partcpants [5]. These studes overlooed two facts. Frst, maret envronments are not unambguous scenes as research n management scence [6] and economcs [7] confrms. Hence, percepton of entry barrers s lely to vary among managers. Second, ths nsght s not trval snce a large body of theory argues and evdence endorses that manageral percepton of envronmental condtons matter

2 [8]. Scherer and Ross argue that the heght of entry barrers and the varaton of the heght wth respect to dfferent potental entrants are also relevant [9]. 3. RESEARCH METHODOLOGY The study s based on phone ntervew whch too part on August The sample sze ncluded 100 companes that use servces of pantshop. The potental customers have been namely recruted from metal processng companes and companes from car ndustry whch produce on large seres. Dmensons of semfnshed products and dstance of potental customer domcle from pantshop ste (maxmum s 100 m because of logstc reasons) were other restrctve condtons.owners and producton managers were among most mportant postons who were ased n the companes. The questonnare conssted of 12 topcal and 3 dentfcaton questons (locaton of company ste, number of employees and respondent poston). The data were analyzed through PASW 18.0 verson. The opportunty for maret entry was examned through the ntent to change the present suppler n case of more attractve offer. Swtchng ntent s a bnary dependent varable whch nforms us how dffcult s to change the atttude of potental customers. Postve change ntent means readness for suppler entry whle negatve change ntent expresses the entry barrer. We suppose that swtchng ntent s affected by three ndependent varables: purchase volume (seral producton or relatvely regular producton), frequency of purchase (often or seldom) and present customer behavour (loyalty or swtchng). Because of the categorcal ndependent varables are recoded usng dummy varables the bnary logt model can be appled. The logt model falls somewhere between regresson and dscrmnant analyss. In the model the probablty of a bnary event tang place should be estmated. Consder an event that has two outcomes: swtchng ntent (decson to change the suppler) or loyalty ntent (decson to stay wth present suppler). The probablty of swtchng may be modeled usng the logt model as t s presented n equatons (1), (2), (3) [10]: p 1 p log e b0 b1 1 b2... b 2 (1) or p log e b (2) 1 p 0 or exp p 1 exp 0 b b 0 where p = probablty of swtchng ntent = ndependent varable b = logstcal coeffcent for that predctor varable Snce the probablty of an event must le between 0 and 1, t s mpractcal to model probabltes wth lnear regresson technques, because the lnear regresson model allows the dependent varable to tae values greater than 1 or less than 0. The logstc regresson model s a type of generalzed lnear model that extends the lnear regresson model by lnng the range of real numbers to the 0-1 range. (3)

3 Testng of ndvdual parameters s based on Wald statstc. Ths statstc s a test of sgnfcance of the logstc regresson coeffcent based on the asymptotc normalty property of maxmum lelhood estmates and s estmated as. Wald b 2 SE b (4) where b = logstcal coeffcent for that predctor varable SE b = standard error of the logstcal coeffcent Three null hypotheses concernng wth applcaton of logt model can be formulated as: H 0A : Swtchng ntent does not depend on purchase volume. H 0B : Swtchng ntent does not depend on frequency purchase. H 0C : Swtchng ntent does not depend on past behavour. The typology of customers was done through matrx as a combnaton of two factors. The factors were defned based on buldng logt model. 4. DEVELOPING CUSTOMER TYPOLOGY 4.1 Buldng logt model The chance for maret entry s assessed through declared swtchng ntent. Those respondents who admt to change the suppler become more perspectve objects for maret enterng. 56% of companes are ready for swtchng (see tab.1). Swtchng ntent can be valdated through the other varables (purchase volume, frequency of purchase and tendency to loyalty) whch nform us about past purchasng behavour of companes. Usng logt model whch has been descrbed n chapter 3 the relatonshp between swtchng ntent (dependent varable) and the other ndependent varables was tested. The comparson between observed and predcted data shows us that overall success rate of model s 68% (see tab.1). The success rate of model s much hgher n the case of postve swtchng ntent (82%) than n the case negatve swtchng ntent (50%). Table 1 Classfcaton table The logt model also computed parameters based on equaton (1). p log e 1,031 0,356 1 p 1 1, , 578 where p = probablty of swtchng ntent 1 = purchase volume 2 = frequency of purchase 3 = past behavor (tendency to loyalty) 3 (5)

4 Coeffcents mentoned n equaton 5 come from tab.2. Table 2 Varables n the Equaton In logstc regresson, the log odds, that s log e 1 p p, s a lnear functon of the estmated parameters Thus, for example, f 1 s ncrease by one unt, the log odds wll ncrease by 0,356 unts, when the effect of varables 2 a 3 s held constant. Thus a (0,356) s the sze of the ncrease n the log odds of the dependent varable (swtchng ntent) when the correspondng ndependent varable 1 (purchase volume) s ncreased by one unt and the effect of the other ndependent varables s held constant. The sgn wll determne whether the probablty ncreases (f the sgn s postve) or decreases (f the sgn s negatve). In that context probablty of swtchng ntent s more typcal for companes whch use the servces of pantshop sporadcally and change the supplers very frequently. The mpact of purchase frequency and past behavour on swtchng ntent has been confrmed on the level of sgnfcance 95%. Both hypotheses H 0B and H 0C should be rejected. The relatonshp between swtchng ntent and purchase volume s not statstcally sgnfcant. Null hypothess H 0A cannot be rejected. Table 3 Correlaton Matrx Tab. 3 presents the smple correlaton between all possble pars of logstcal coeffcents. The hghest correlaton s observed between constant and coeffcents of ndependent varables. 4.2 Structure of clusters Because of logstcal coeffcents and statstcal sgnfcance (see tab.2) the past behavour accordngly wth swtchng ntent was used for buldng customer typology sutable for successful maret entrance. The types of customer are descrbed n the matrx as the combnaton of two bnomnal varables (see fg.1). Fg. 1 Typology of Customers

5 Based on declared swtchng ntent and past behavour (tendency to loyalty) four types of clusters (segments) were defned (maret share of clusters s presented n tab.4): Optmzers (46%) those companes stress prce as a ey drver for choce of suppler. The companes always change supplers f cheap opton appears on the maret. Conservatves (22%) show the tendency to swtchng. They prefer lmted number of supplers among them they choose the best prce proposal. Due to ther relance on varnshng qualty they perceve to test newcomers as too rsy busness. Seeers (10%) are loyal because they are satsfed wth product qualty of present suppler. If newcomer fulfls ther demand they are ready for change. Satsfed customers (22%) were loyal n the past and they are gong to stay wth present suppler also n future. Varnshng qualty and delvery terms are assessed as man motvators for dong busness wth present suppler. Table 4 Structure of Clusters varnshng qualty delvery terms prces satsfed customers 2,00 2,59 3,00 seeers 2,00 2,20 2,80 conservatves 2,05 2,55 2,82 optmzers 2,00 2,48 2,91 1,50 1,70 1,90 2,10 2,30 2,50 2,70 2,90 Fg. 2 Key Factors for Dfferent Customer Types 5. CONCLUSION Logt model has been appled for estmatng probablty of swtchng ntent. That s very mportant for decson mang of maret newcomer. Tend to swtchng s more favourable for potental supplers. The probablty of swtchng ntent can be statstcally explaned by two ndependent varables (frequency purchase, past behavour). Based on past behavour and swtchng ntent the potental customers can be classfed nto four categores. Namely optmzers and seeers are sutable target segments for enterng the maret. Those two categores are dfferent n the preferences of prce or vanshng qualty.

6 LITERATURE [1] GROSS, A. C., BANTING, P. M., MEREDITH, L. N. and I. D. Ford. Busness Maretng. Boston: Irwn, s. ISBN [2] ASHYIA, M. Wea entrants are welcome. Internatonal Journal of Industral Organzaton, 2000, vol.18, pp [3] NIU, Y., DONG, L.C., CHEN, R. Maret entry barrers n Chna. Journal of Busness Research, 2012, vol. 65, pp [4] KARAKAYA, F, STAHL, M.J. Barrers to entry and maret entry decsons n consumer and ndustral goods marets. Journal of Maretng, 1989, vol.53, no.2, pp [5] SCHNELL, M. A. What determnes the effectveness of barrers to entry n lberalsed arlne marets? Journal of Ar Transport Management, 2004, vol. 10, no. 6, pp [6] FOMBRUN, C.J., ZAJAC, E.J., Structural and perceptual nfluences on ntra ndustry stratfcaton. Academy of Management Journal, vol.30, no.1, pp [7] AIGINGER K., McCABE, M., MUELLER, D.C., WEISS, C., Do Amercan and European ndustral organzaton economsts dffer? Revew of Industral Organzaton, 2001, vol. 19, pp [8] LEFEBVRE, L., MASON, R., LEFEBVRE, E. The nfluence prsm n SMEs: the power of CEO s perceptons on technology polcy and ts organzatonal mpacts. Management Scence, 1997, vol. 43, pp [9] SCHERER, F. M., ROSS, D. Industral Maret Structure and Economc Performance. Boston: Houghton Mffln, pp. ISBN [10] MALHOTRA, N. K. Maretng research. 6 th edton. New Yersey: Prentce Hall, pp. ISBN