Customer Portfolio Analysis Using the SOM

Transcription

1 Association fo Infomation Systems AIS Electonic Libay (AISeL) ACIS 2008 Poceedings Austalasian (ACIS) 2008 Custome Potfolio Analysis Using the SOM Annika H. Holmbom Tuku Cente fo Compute Science (TUCS) Åbo Akademi Univesity Tuku, Finland, Tomas Eklund Institute fo Integated and Intelligent Systems (IIIS) Giffith Univesity Bisbane, Austalia, Babo Back Depatment of Infomation Technologies Åbo Akademi Univesity Tuku, Finland, Follow this and additional woks at: Recommended Citation Holmbom, Annika H.; Eklund, Tomas; and Back, Babo, "Custome Potfolio Analysis Using the SOM" (2008). ACIS 2008 Poceedings This mateial is bought to you by the Austalasian (ACIS) at AIS Electonic Libay (AISeL). It has been accepted fo inclusion in ACIS 2008 Poceedings by an authoized administato of AIS Electonic Libay (AISeL). Fo moe infomation, please contact

2 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. Custome Potfolio Analysis Using the SOM Annika H. Holmbom Tuku Cente fo Compute Science (TUCS) Åbo Akademi Univesity Tuku, Finland Tomas Eklund Visiting eseache, Institute fo Integated and Intelligent Systems (IIIS) Giffith Univesity Bisbane, Austalia Babo Back Depatment of Infomation Technologies Åbo Akademi Univesity Tuku, Finland Abstact In ode to compete fo pofitable customes, companies ae looking to add value using Custome Relationship Management (CRM). One subset of CRM is custome segmentation, which is the pocess of dividing customes into goups based upon common featues o needs. Segmentation methods can be used fo custome potfolio analysis (CPA), the pocess of analyzing the pofitability of customes. This study was made fo a case oganization, who wanted to identify thei pofitable and unpofitable customes, in ode to gain knowledge on how to develop thei maketing stategies. Data about the customes wee gatheed fom the case oganization s own database. The Self-Oganizing Map (SOM) was used to divide the customes into segments, which wee then analyzed in light of poduct sales infomation. Keywods Custome Relationship Management (CRM), Custome Potfolio Analysis (CPA), Data-diven maket segmentation, Self-Oganizing Map (SOM) INTRODUCTION Custome elationship management (CRM) is an impotant topic of management today. The objective of CRM is to integate sales, maketing and custome cae sevice in ode to add value fo both the company and its customes (Chalmeta 2006; Datta 1996; Heinich 2005). CRM fist emeged in 1993 and has developed apidly in ecent yeas thanks to advances in infomation technology ( Buttle 2004; Rygielski et al. 2002), to become the impotant function that it is in today s companies. One of the most impotant tasks within CRM is custome segmentation, the pocess of identifying and gouping customes with simila pofiles o equiements (Lingas et al. 2005). The key element in CRM and custome segmentation is oveall infomation about customes. Today, data about customes ae eadily available though ERPs, copoate data waehouses, and the Intenet. Data can also be puchased fom othe companies, which has lately fomed into a new categoy of business (Buttle 2004; Rygielski et al. 2002). The poblem is that the amount of infomation available fo segmentation is huge and can be vey challenging to deal with because of issues such as missing data, non-unifom distibutions, eos, etc. The extaction of infomation fom lage databases is, theefoe, often pefomed using data mining methods ( Bey and Linoff 2004; Beson et al. 2000; Famili et al. 1997; Rygielski et al. 2002; Shaw et al. 2001). The pupose of the pape is to illustate how the Self-Oganizing Map (SOM) can be used fo one categoy of CRM, custome potfolio analysis (CPA). The wok builds upon the eseach initiated in Holmbom (2007) in which a model fo segmentation of custome data was built. The model was based upon custome data povided by a case company, and the Self-Oganizing Map is used to constuct the model. The model was face validated 412

3 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. by expets fom the sales depatment of the case oganization. The model could potentially be used to adjust maketing effots to incease the pofitability of customes. METHODOLOGY Custome Segmentation Custome segmentation is an example of analytical CRM, i.e., the use of analytical tools to study custome data (Paas and Kuijlen 2001). Custome segmentation is the pocess of gouping customes into subgoups with simila behavio o needs, in ode to bette seve o taget the customes (Buttle 2004; Lingas et al. 2005). The identified segments can then be moe effectively tageted with suitable maketing stategies (Fank et al. 1972, p. 26; Wedel and Kamakua 1999, p. 5). Custome segmentation is also used to identify unpofitable and pofitable customes in the custome base, as well as custome elationships with development potential. This is efeed to as Custome Potfolio Analysis, o CPA (Buttle 2004). CPA is impotant as seveal studies have found that the 20/80 ule holds fo custome pofitability as well; 20% of customes account fo 80% of pofits, and vice vesa (Kim et al. 2006; Pak and Baik 2006). Although CPA is elated to segmentation and can be seen as a subset of it, the pupose is diffeent and many of the methods used ae unique (Teho and Halinen 2007). CPA can beneficially be applied to analyze segments identified using segmentation methods. Thee ae a lage vaiety of diffeent methods available fo CPA. Howeve, although thee is a geat wealth of theoetical liteatue suounding CPA available, vey little liteatue appeas to show how CPA is actually being used by companies today (Teho and Halinen 2007). Much of the liteatue concens mathematical optimization models, such as potfolio theoy (e.g., Tunbull 1990) and custome lifetime value (e.g., Kim et al. 2006). These methods geneally view the custome base in the same way as a potfolio of investments, to be managed using the same methods. Thee ae two main bases fo segmentation, i.e., demogaphic data, such as socioeconomic and lifestyle measues, and poduct specific measues, such as poduct usage, custome band attitudes, band pefeences, benefits sought and esponse sensitivity to diffeent maketing campaigns. Demogaphic data ae the most commonly used base fo segmentation (Fank et al. 1972; Tsai and Chiu 2004; Wedel and Kamakua 1999). Segmentation can also be divided into two majo goups based upon the appoach used: maket-diven and data-diven segmentation. Maket-diven segmentation uses data to divide customes into segments. These segments ae befoehand set accoding to chaacteistics that descibe a specified custome pofile, e.g., one that has been detemined to be pofitable. Data-diven segmentation is pefomed on actual custome data, e.g., the shopping behavio of a custome (Beson et al. 2000). As fo CPA, thee ae a lage vaiety of methods available fo segmentation. Many commonly used segmentation methods belong to the family of clusteing appoaches. Most of the methods in this aea ae statistical tools, such as k-means clusteing and hieachical clusteing methods. Data mining appoaches, such as sequence analysis, maket basket analysis and neual netwoks ae also employed. Othe commonly used appoaches ae decision tee-elated appoaches (e.g., CHAID) and fuzzy clusteing appoaches (e.g., Fuzzy FCM). In this study, a data-diven exploatoy CPA, based upon demogaphic data and coupled with poduct sales infomation, will be pefomed. The Data The data wee povided by a case company that sells poducts anging fom simple peiodicals to advanced consulting sevices, to othe companies (B2B). The company wanted to pefom a custome potfolio analysis in ode to detemine which of its customes wee pofitable and woth developing its elationship with, and convesely, which customes wee bette let go of. In addition, the company wanted to detemine which goups of customes puchased which poducts. Oveall, the stategic goal was to ceate a tool to be used by the sales depatment in ode to adjust company maketing pactices, i.e., to detemine suitable maketing effot levels fo diffeent, peviously unknown, categoies of customes. The data wee extacted fom the case company s data waehouse and consist of data about customes and thei puchasing behavio. The customes ae companies fom diffeent lines of business, e.g., sevice, constuction, industial, wholesale, and etail. The data oiginate fom the customes annual epots and the case company s own data waehouse. They contain desciptive categoies, descibing the attibutes of the customes, as well as sales infomation concening the majo poducts. Based upon a pilot test of the data and a eview in coopeation with the case company, a numbe of small and vey lage customes (appeaing as outlies in the esults) wee emoved, and some poduct categoies with small and infequent puchases wee meged. The motivation fo doing this was that the lagest customes ae aleady individually seved by an own sales epesentative, and the smallest customes wee usually one-time puchases. The desciptive categoies consisted of: 413

4 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. Risk facto, which is an intenally calculated measue of potential financial losses Company age Solvency, which was calculated fom the financial statement Tunove Change in tunove (%), compaed to the pevious yea Balance sheet total, which seves as a measue of company size Retun on equity (ROE), which was calculated fom the financial statement. The poduct categoies consisted of 18 diffeent poducts, labeled poducts A-R. The poducts ae geneally speaking infomation sevice poducts, whee poduct I (a significant consulting sevice) is the most expensive poduct and poduct L (a simple filteed data poduct) is the cheapest one. Poduct O stands fo oveall puchases of poducts and poduct R fo othe poducts (one-time analyses and othe poducts difficult to categoize). The data collected wee fo the peiod of 2002 to The data set contained 1,841 customes, i.e. 9,205 ows of data % of the customes had incomplete desciptive data, i.e., 3.6 % of the data values wee missing. The missing data wee not consideed a poblem, as the SOM is able to deal with small amounts of missing data (Bigus 1996). The SOM Atificial neual netwoks (ANNs) have been widely applied to vaious business poblems ( Smith and Gupta 2002; Vellido et al. 1999b). ANNs ae commonly divided into two main categoies: supevised and unsupevised leaning appoaches (Haykin 1999). Supevised netwoks lean pattens by using taget outcomes, and ae thus most often used fo classification tasks, i.e., whee classes ae pedetemined. Maket-diven segmentation would be pefomed using supevised leaning ANNs. Unsupevised leaning is used fo exploatoy analysis, clusteing, and visualization (Kohonen 1998). Kohonen s Self-Oganizing Map (SOM) is the most commonly used unsupevised ANN. The SOM is a twolaye feedfowad netwok, in which each neuon leans to ecognize a specific input patten (Kohonen 2001). Each neuon is epesented by a pototype vecto, i.e. an n-dimensional weight vecto. The algoithm is basically a two-step pocess; in the fist step, the best matching neuon (BMU, best matching unit) fo an input data ow is located on the map, and secondly, it and its suounding neuons within a cetain neighbohood adius ae tuned to bette match (i.e., lean fom) the input data, based upon a leaning ate facto. The pocess is epeated until a cetain stopping citeion is eached, fo example, the taining length. The esult of the taining pocess is a visual clusteing that shows similaities and dissimilaities in the data (Kohonen 2001). Essentially, the SOM is a nonlinea pojection technique that displays high-dimensional data on a twodimensional gid, by peseving the elationships (o topology) in the data but not the actual distances (Deboeck and Kohonen 1998). Commonly, the SOM is visualized using the U-matix (Unified Distance Matix) of the map, which displays the Euclidean distances between neuons in shades of colo (Ultsch 1993). Fom the pespective of segmentation-based CPA, the SOM has seveal advantages. Compaed to mathematical optimization methods and most statistical appoaches, the main advantage of the SOM is that it is a highly visual method. This makes it simple to pesent and explain esults to business decision makes. Also, judging the esults is moe intuitive fo a non-mathematically inclined audience. The SOM is also vey obust, equiing vey little pepocessing of the data, and unlike most statistical appoaches, is non-paametic. The SOM is an exploative tool, meaning that vey little a pioi knowledge is equied, and it is possible to uncove unexpected pattens in data. Decision tees ae simple to use and highly visual appoaches, but coectly deciding the split lines is impeative (Pyle 1999), and they ae unsuitable to exploatoy analysis whee no pedefined classes exist. Regession appoaches and classification-based neual netwoks ae also unable to deal with data when pedefined classes ae not available. The SOM has been widely applied in finance, economy and maketing (Kaski et al. 1998; Kohonen 1998; Oja et al. 2003). Fo example, the SOM has been used fo financial benchmaking (Back et al. 1998; Eklund et al. 2003), maco-economic analysis (Kaski and Kohonen 1996; Länsiluoto 2007), and bankuptcy pediction (Back et al. 1995; Kiviluoto 1998; Matín-del-Bío and Seano-Cinca 1993). Howeve, egadless of its obvious benefits, the SOM has not been widely applied in custome segmentation tasks. Examples include Rushmeie et al. (1997), who used the SOM to visualize demogaphic custome segments fo maketing puposes, Vellido et al. (1999a), who used the SOM fo demogaphic segmentation of online customes, Lee et al. (2004; 2005), who used the SOM fo demogaphic segmentation of online games, and Lingas et al. (2005), who used the SOM fo tempoal analysis of supemaket customes duing a peiod of 24 hous. This study diffes fom the pevious 414

5 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. ones in that the SOM is hee used fo CPA, based upon demogaphic infomation as well as poduct sales infomation, and fo multiple yeas of data. To ou knowledge, this has not been done peviously. TRAINING THE MODEL Viscovey SOMine 4.0 ( was used to tain the maps in this study. SOMine is based upon the batch-som taining algoithm (Kohonen 2001) and also uses a stepwise inceasing map size duing the taining pocess, which makes it a vey efficient implementation of the SOM algoithm (Deboeck 1998). In addition, SOMine is vey use fiendly and includes a numbe of advanced data pe-pocessing and analysis tools, such as automated clusteing of the map based upon Wad s hieachical clusteing method. The demogaphic data of the companies (isk facto, age, solvency, tunove, change in tunove%, balance sheet total, and ROE) wee fist used to ceate one map. The esults of the demogaphic segmentation wee then matched with the sales infomation fo each poduct. Even though the SOM is faily toleant towads noisy o missing data (Bigus 1996; Smith and Gupta 2002), data pe-pocessing is an impotant pat of the data-mining task. Pe-pocessing efes to the task of dealing with data quality issues such as missing, eoneous, o outlie data ( Beson et al. 2000; Famili et al. 1997; Hand et al. 2001; Pyle 1999). In this application, sigmoid (o logistic) tansfomation (Bishop 1995) was used to deal with outlie data. The sigmoid tansfomation was used because it emphasizes the cente input values while educing the influence of exteme input values (Bishop 1995; Laose 2005). Vaiance scaling was futhe used to make the vaiables compaable. Geneally speaking, the size of the map is dependent upon the pupose of the application. A lage hexagonal map is good fo visualization (moe accuate on the individual ecod level), wheeas a small map is moe suitable fo clusteing (squeezes data into a smalle numbe of goups) (Desmet 2001; Kohonen 2001). In this case, a map size of 700 nodes was selected as a balance between clusteing and visualization since the goups wee not expected to be vey homogeneous and we wanted to be able to accuately judge the inta-cluste diffeences. As the softwae uses the batch SOM algoithm, the leaning ate does not need to be specified (Deboeck 1998), and the only othe paamete equied is the tension. The tension is essentially a value fo the neighbohood adius in the final taining stage, whee a small tension esults in high local detail (accuacy), while a high tension has an aveaging (smoothing) effect on the map. In this case, the default value 0.5 (aveage) was used. The neighbohood function is always Gaussian. Although the U-Matix of the SOM can be manually intepeted to identify the clustes, two-stage clusteing (Vesanto and Alhoniemi 2000) is an easie and moe objective method of identifying the clustes on the map. In two-stage clusteing, the neuons on the map ae clusteed based upon thei Euclidean distances, using a suitable clusteing algoithm. In this case, Wad s hieachical clusteing method, included in the SOMine softwae, was used to identify the clustes on the final map. The final map is displayed in Figue 1. The clusteing of the map esulted in ten clustes of vaious sizes, labeled C1-C10. The colo of the cluste only signifies cluste membeship, and does not imply any value. 415

6 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. Figue 1: A U-matix of the segmentation esults, using the custome data fom yeas The map was ceated accoding to the vaiables fom the desciptive categoies In ode to intepet the map, and in paticula the chaacteistics of each cluste, the component planes (displayed in Figue 2) of the map must be used. The component planes show the distibution of values acoss the map, accoding to one vaiable at a time. The values accoding to one vaiable ae displayed by the colo of the neuon, whee wam colos (ed, oange, and yellow) illustate high values and cool colos (blue) illustate low values. The appoximate values ae indicated by the scale unde each component plane. The map is intepeted by eading the component planes fo each cluste. Fo example, Cluste 6 displays medium to high values in solvency and ROE, and low values in age, tunove, and balance sheet total. Cluste 6 also shows vaying isk factos, fom low to high. We can conclude that these ae faily young and small companies, although vey pofitable. We can also see that the isk facto is extemely lage in segments C2 and C5, which means that these segments contain less eliable companies. The oldest companies ae found in segments C4, C5 and C7. Figue 2: The component planes of the map, showing the values accoding to one component at a time 416

7 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. ANALYSIS OF THE RESULTS The esults of the custome segmentation ae summaized in Table 1, which shows the sizes and distinguishing featues of each of the clustes. Clustes C1, C2 and C3 ae the lagest ones. Table 1. Division of customes into clustes accoding to the segmentation pesented in Figue 1 Clustes Customes % Distinguishing featue(s) Puchased poducts Cluste 1 1, No specific attibute A-R, except M Cluste 2 1, High isk facto A-R, except M Cluste 3 1, Highest solvency A-R, except M Cluste 4 1, Oldest companies, high solvency A-R, except M Cluste Lage companies, high solvency A-R, except M & Q Cluste High isk facto, good solvency, vey high pofitability A-R, except M Cluste Both old and young companies, good tunove A-R, except M Cluste Lagest tunove, lage balance sheet A-R, except M & Q total Cluste Lage balance sheet total A-R, except L & M Cluste Lagest change in tunove (%) A-R, except M & Q The clustes identified ae as follows: Cluste 1: an aveage goup with no specifically identifying chaacteistics. Risk facto, age, tunove, and balance sheet total ae low, and solvency is medium to high. Retun on equity is good on aveage. One of the thee lagest goups in tems of numbe of customes. Cluste 2: exhibits a consideably highe isk facto, lowe solvency, and lowe etun on equity than in Cluste 1. This goup also contains the customes with the lowest etun on equity, as well as the companies with the lowest solvency. One of the thee lagest goups in tems of numbe of customes. Cluste 3: simila to Cluste 1 except fo a consideably highe solvency. The aveage company age is somewhat highe, although isk facto seems to be simila to that of Cluste 1. One of the thee lagest goups in tems of numbe of customes. Cluste 4: contains the oldest companies in the dataset, and geneally exhibits a high solvency and good pofitability. Cluste 5: is a mid-size cluste containing lage than aveage companies. Solvency is good, and the companies ae faily new. Pofitability is aveage. Cluste 6: a mid-size cluste that contains the most pofitable companies in the dataset. In geneal small, gowing companies. Nealy half of the cluste displays a vey high isk facto, but some companies ae also vey solvent. Cluste 7: a mid-size cluste that contains faily lage companies in tems of tunove and total assets. Solvency is good on aveage, and the cluste contains a mix of old and new companies. Cluste 8: is one of the thee small clustes and contains the lagest companies in tems of assets and tunove. The companies ae solvent and faily pofitable, and thei isk facto is vey low. Company age is above aveage. Cluste 9: is anothe small goup of lage companies. This cluste diffes fom Cluste 8 in that the companies ae newe and tunove is lowe. Cluste 10: is the smallest and final cluste identified. It contains apidly gowing companies that ae faily pofitable and solvent, and have a faily low isk facto. Afte the clustes wee identified, the next step was to compae the sales infomation fo each poduct categoy to the ceated segments. The full table can be found in Appendix 1. The colo scale in Appendix 1 visualizes 417

8 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. how the clustes can be divided into goups consisting of the lagest, aveage, and smallest customes accoding to the sales infomation. If the amount of sales wok expended fo each of the segments is the same, the division of the custome segments can be extended to descibe pofitable, aveage and non-pofitable customes. Lagest customes in tems of sales The lagest customes ae located in clustes C7 and C % of the companies in the custome base belong to this goup. These companies have the lagest aveage sales figues fo nealy evey poduct. In the SOM map pesented in Figue 1, these clustes ae located in the lowe ight cone of the map. Thee ae both young and old companies that possess a lage tunove in Cluste 7. Some of the younge companies in this segment have a slightly highe isk facto, and some of the customes have a high solvency. The lage balance sheet total fo the customes in Cluste 8 is a sign that these companies ae lage in size. These companies have the lagest tunove and a high solvency. Aveage customes in tems of sales Accoding to the segmentation model, the aveage customes in tems of sales ae located in clustes C1, C4, C6, C9 and C10. They constitute 43.7 % of the total custome base. The companies in Cluste 1 do not have a dominating desciptive component. A small pat of the customes in this cluste has an inceased isk facto, anothe pat is slightly olde, and a thid pat has a good solvency. The oldest customes ae located in cluste C4. These have a high solvency, and some of them have an inceased isk facto. The companies in Cluste 6 have a high isk facto, but they possess a high solvency and the lagest etun on equity. The companies in Cluste 9 ae lage in size, as they have a lage balance sheet total. The customes in Cluste 10 ae gowing companies. Also, the specific company that makes the highest oveall puchases belongs to this cluste. The ode of pioity fo these aveage clustes, accoding to the infomation gained fom the segmentation, would be as follows: clustes C10 and C9 (made most puchases), Cluste 4 (aveage) and clustes C1 and C6 (made least puchases). Accoding to the model, Cluste 9 is vey simila to the clustes with the companies who conducted most puchases, i.e., clustes C7 and C8. This would indicate the possibility that futue good customes could be found in Cluste 9. Similaly, the pooest customes in this goup ae located in Cluste 6, which is vey diffeent fom the best pefoming clustes. Pooest customes in tems of sales The companies in clustes C2, C3 and C5 ae the pooest customes, i.e., they puchase the least amount of poducts. Thei shae of the custome base is 50.3 % and these customes, theefoe, constitute the lagest goup. Accoding to the model, the companies in Cluste 2 have a high isk facto. Howeve, many of the single companies that have puchased the lagest amounts of a specific poduct ae located in this cluste. The customes in cluste C3 have the highest solvency. A small shae of these companies has a slightly inceased isk facto, and anothe shae is slightly olde. The companies in Cluste 5 ae olde with a high isk facto. A common facto amongst the pooest customes is the high isk facto, which means that they ae not eliable customes. CONCLUSIONS AND FUTURE RESEARCH In this pape, the use of the SOM fo custome potfolio analysis has been illustated. A custome segmentation based upon demogaphic data was pefomed using the SOM, identifying ten clustes of customes displaying diffeent demogaphic chaacteistics. The esulting clustes wee then coupled with sales data, and a custome potfolio analysis was pefomed in ode to identify pofitable and unpofitable customes. The esulting model was face validated by expets fom the sales depatment of the case oganization. The sales depatment of the case oganization could potentially develop its maketing stategies based on the esults of this wok. Thee ae seveal inteesting topics of eseach that should be pusued in the futue. Fistly, the model cannot identify a univesal demogaphic featue o set of featues that can pedict custome pofitability, although custome size gives an indication of puchase potential. Futhe eseach should be conducted to see if the addition of othe demogaphic featues could incease the pedictive pefomance of the model. Secondly, pedicting puchase potential is potentially a valuable addition to the model. This could be done using statistical models. Thidly, pedicting the level of effot equied to push a custome to a moe pofitable level of elationship should be eseached, e.g., using Makov chain analysis. Finally, maket basket analysis was peliminaily pefomed in Holmbom (2007), and should be futhe developed. 418

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11 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. ACKNOWLEDGEMENTS The authos would like to thank the case oganization fo its paticipation in the study. The authos also gatefully acknowledge the financial suppot of the National Agency of Technology (Titan, gant no /08), the Academy of Finland (Visiting eseache gant no ), and the Foundation fo Economic Education (gant no ). APPENDIX 1 Clustes C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Poduct A 2.min min 2.max Max / Poduct B min 2.min Max 2.max Poduct C Min / 2.min Max 2.max Poduct D min 2.max Max / 2.min Poduct E min 2.min Max 2.max Poduct F min 2.max Max 2.min Poduct G min Max 2.max 2.min Poduct H 2.min Max 2.max min Poduct I min 2.min 2.max/ Max Poduct J 2.min 2.max/ Poduct K min 2.min Max / Min Max 2.max Poduct L min 2.min 2.max 0 Max Poduct M Poduct N min 2.max Max 2.min Oveall puchases 2.min min Max 2.max Poduct P 2.min Min 2.max Max Poduct Q 2.max 0 Max / 0 0 Poduct R min 2.min Max 2.max 421

12 19 th Austalasian Confeence on Infomation Systems Custome Potfolio Analysis Using the SOM 3-5 Dec 2008, Chistchuch Holmbom, et al. Clustes with the two customes making most of the puchases (MAX, 2.MAX) wee maked with wam colos, i.e. diffeent shades of ed, and espectively, clustes with the two least puchasing customes (min, 2.min) wee maked with cool blue colos. The compaison was made accoding to aveage cluste sales. Also, the cluste whee the company that puchased the most, measued in, is located was maked fo each of the poducts sepaately (). COPYRIGHT Holmbom, Eklund and Back The authos assign to ACIS and educational and non-pofit institutions a non-exclusive licence to use this document fo pesonal use and in couses of instuction povided that the aticle is used in full and this copyight statement is epoduced. The authos also gant a non-exclusive licence to ACIS to publish this document in full in the Confeence Papes and Poceedings. Those documents may be published on the Wold Wide Web, CD-ROM, in pinted fom, and on mio sites on the Wold Wide Web. Any othe usage is pohibited without the expess pemission of the authos. 422