Approaches to Segmentation

Transcription

1 STRATEGIC MARKETING DECISIONS Providig Comprehesive Strategy ad Pricig Solutios Approaches to Segmetatio Scott Davis, PhD Pricipal, Strategic Marketig Decisios (916) Scott Davis. Ay distributio or other commercial use is prohibited uless writte permissio is obtaied from Scott Davis ad Strategic Marketig Decisios.

2 Customer segmetatio is a key to developig a successful strategy ad pricig policy i a competitive eviromet. By segmetig i the market, it is possible to determie who the most likely prospects are for your product ad develop a set of product desigs ad prices that will most effectively target them. I additio, it makes it possible to determie who the most appropriate targets are for your competitors, which makes it possible to idetify actios that will have a sigificat impact o your competitors ad will therefore be more likely to trigger a competitive respose. A failure to properly segmet the market whe estimatig demad ofte will lead to a poor ad misleadig characterizatio of the way the market will respod to chages i prices ad product desigs. Specific objectives of segmetatio are to fid systematic variatios amog customer types i their beliefs, attitudes ad prefereces, purchase behaviors, ad their impact o compay performace. The goal is to fid some meaigful ad readily observable characteristics that describe customers who are similar o oe or more of those dimesios. Commo segmetatio criteria iclude demographic or geographic variables, preferece or purchase measures, ad behavioral characteristics (see Table 1). Table 1. Commo Segmetatio Criteria Cosumer Markets Demographic factors: age, geder, marital status, family size ad compositio, occupatio, icome, educatio, ethic backgroud, religio Geographic factors: regio, commuity type (urba, suburba, rural), climate Product preferece ad use: purchase size ad frequecy (usage rate), feature preferece, brad loyalty, price sesitivity, usage occasios, product kowledge or experiece Busiess Markets Demographic factors: idustry, compay size (uit volume, umber of employees, etc.), profit status (profit versus oprofit), fiacial resources or performace, umber of facilities uder maagemet Geographic factors: regio or coutry, commuity type (urba, suburba, rural), populatio, cost of livig, average educatio or skill level Product preferece ad use: purchase size ad frequecy (usage rate), feature preferece, brad or vedor loyalty, price sesitivity, risk aversio, product use, product kowledge or experiece Psychographic: social class, persoality, lifestyle (beliefs ad activities participatig i) Purchasig structure ad process: maagemet structure, are budget process, allocatio of purchase decisio authority, role of the user i the purchase decisios, purchase process Customers may vary i their attitudes about products i a umber of ways. They may have differet beliefs regardig which products are available, the features of those products, ad the ability of various products to satisfy their eeds ad desires. They may also have differet priorities are prefereces for product features ad sesitivity to price. For example, i the market for persoal computers, gamers value highly fuctioal graphics ad audio cards ad are willig to pay a premium for them. Busiess users such as data aalysts will be iterested i CPU speed ad will pay for that capability but are less sesitive to video performace. They will ted to be more cocered with gettig a good value for their ivestmet. By cotrast may seior citizes are primarily iterested i computers that allow them to get their electroic mail ad will be more sesitive to price. They may also vary i how they make purchase decisios. Oe reaso is differeces i how they use iformatio. Some customers may systematically process iformatio cocerig the product features ad how importat they are ad try to optimize. These customers will ted to make a purchase decisio based o which alterative they believe will give them the greatest beefits or utility per dollar spet. I cotrast, other customers may simply make choices based o habit, impulse or rules of thumb. Fore example, brad loyal customers may cotiue to choose their preferred brad as log as price is deemed acceptable while deal proe customers may simply choose the brad offerig the best price discout i absolute or percetage terms. Customers may also differ i terms of who makes the purchase decisio. Whe the decisio maker is also the perso will use the product much more attetio is paid to the product s performace; whereas, a decisio maker whose

3 priority is maagig a budget will pay much more attetio to the product s price. This is particularly importat i busiess-to-busiess markets i which multiple parties ofte have a impact o the choice decisio. A customer s vedor choice may also ifluece the choice of products available as well as the iformatio they ca receive a bout the alteratives. Market segmets will geerally differ i terms of their potetial for sales volume ad profitability. Price-sesitive customers are goig to be more likely to search out competitive offers ad choosig a alterative of acceptable quality that has the lowest price. The profit potetial of the segmets will ted to be small uless the seller has a cost advatage or the sales volume potetial is sufficietly large to compesate for a small cotributio per uit or geerate scale ecoomies. Customers may also differ i the cost of servig them. I the isurace idustry there are systematic variatios across customer types i terms of both the likelihood ad cost of claims filed. Most isurace compaies employ systematic actuarial aalyses i settig both their offerigs ad rates, with differet rates beig charged to customers havig differet characteristics. For example auto isurers will charge higher rates to youger drivers because of historical evidece that idicates the drivers i that age group have higher accidet rates. QUALITATIVE SEGMENTATION TECHNIQUES Markets ca be segmeted usig judgmet based o qualitative observatios ad a statistical aalysis of available data. Qualitative approaches draw o the assessmet by sales represetatives or maagemet of how purchase behavior varies with customer characteristics. May valuable isights ca be derived by sales represetatives who may observe that differet customer types commo display patters i prefereces ad the ways decisios are made or objectios to purchase. A key to the success of a qualitative segmetatio approach is a systematically gatherig ad processig iformatio about curret ad potetial customers. This data may come simply from direct iteractios with customers or may be augmeted by market research studies such as surveys ad focus groups. Useful iformatio to gather icludes how they use the product, which alteratives they cosider ad how they evaluate their effectiveess i meetig their eeds, how they make purchase decisios, ad how much they value differetiatig features. By observig commo themes i customer resposes, it may be possible to idetify how differet customer types vary i the offerigs they cosider ad how they value competig alteratives. Systematically collectig data ca be a very valuable tool whe usig judgmet to segmet a market ad reduces the reliace o pure ituitio. CROSS TABULAR ANALYSIS Systematically collectig data may also make it possible to employ quatitative tools to assist i segmetig a market. I segmetig a market it is useful to examie how variables relatig to a segmet s attractiveess (such as preferece, curret purchase likelihood, average purchase size, average customer expeditures i the category, expected cost of service, etc.) vary with variables that could be used to defie a segmet. A cross tabular aalysis ca be a useful tool to examie the iterrelatioships betwee categorical variables describig segmets characteristics ad their potetial attractiveess. A cross tabular aalysis begis by defiig categories for the variables to be cosidered. I defiig categories, variables should be fully iclusive i that all observatios should fall ito oe category ad mutually exclusive so that o oe observatio should fall ito more tha oe category. I may cases this may be straightforward as is the case with geder. Ofte, however, variables caot be described i such a obvious way. Categories for cotiuous variables, such as age or icome, must be subjectively defied based o the aalyst s judgmet. Oce the categories are defied, a table is created that summarizes how frequetly a observatio from oe category occurs i aother category. Cosider the example of a customer pael that describes the frequecy of purchase of a soft drik called Fizz Cola. I this example a group of 880 cosumers moitored their soft drik cosumptio for a period of three moths. I this pael demographic data was collected as well as a purchase history for each participat. Oe potetial segmetatio criterio for this market was age. Age could be a meaigful basis for segmetatio if brad preferece varied system-

4 atically by age. To begi the aalysis, each respodet was placed i a age category that was defied by the aalyst. Brad choice probabilities also were divided ito five categories to describe brad preferece or loyalty. Those who chose Fizz less tha twety percet of the time could be viewed as preferrig aother brad while those choosig Fizz more tha 80 percet of the time could be viewed as relatively brad loyal. The survey results are summarized i Table 2. Each cell cotais the cout of participats i each age group with a give choice probability. If brad choice probability was uiflueced by age the oe would expect the etries i each cell to be close to the expected umber of etries i the cell that would occur if the observatios were allocated to cells radomly based o how frequetly each category occurred i the etire set of observatios. I the example, the expected umber of cases of those uder 21 choosig Fizz less tha 20 percet of the time would be the proportio of pael members uder 21 (181/880) times the proportio of the pael members choosig Fizz less tha 20 percet of the time (142/880) times the umber of pael members (880), which would be Sice the actual umber of observatios i that cell is 16, we might coclude that youg people are less likely to prefer aother brad tha the populatio as a whole. Table 2. Crosstabulatio for soft drik cosumers Age Group Choice Probability Crosstabulatio Age Group Total *Excludes lower value 20 ad Uder Cout Expected Cout Expected Cout Expected Over 60 Cout Expected Cout Expected Cout Choice Probability Up to 20% 20-40% * 40-60% * 60-80% * % * Pears o Chi-Square Cofidece % The sigificace of a classificatio scheme ca be assessed by the degree to which the etries i each cell differ from the values that would occur if the variables were idepedet. Pearso Chi-Squared statistic provides a measure of how likely it is that the two categorical variables are idepedet. It is computed usig the formula: 2 χ 2 ( o ) ij eij, e i j Where o ij is the observed umber of observatios i the cell row i ad colum j ad e ij is the umber or observatios that would be expected if the row variable ad colum variable were idepedet. This value is large if the observed values differ from the values that would be expected with idepedece. To determie whether this value is statistically sigificat it should be iterpreted i terms of the umber of rows ad colums i the table. The degrees of freedom for this statistic are give by the umber of rows mius oe times the umber of colums mius oe, or (r-1) (c-1). For the pael i this example the Chi-Squared statistic is 44.4 ad there are (5-1) (4-1), or 12 degrees of freedom. If age ad choice probability were urelated, this value would occur less tha.5 percet of the time whe there are 12 degrees of freedom. ij

5 To iterpret the table, it appears that youg cosumers are much more likely to purchase Fizz tha the populatio as a whole ad that older adults are more likely to choose aother brad. This aalysis does ot explai why the differet age groups chose the way they did. Differeces could be due to differeces i brad preferece, but could also be due to differeces i sesitivity to other marketig variables such as price, price promotio frequecy, or the availability of the product at certai purchase locatios. As such, it would be valuable to examie cosumer perceptios, prefereces ad buyig behavior i more depth. However, it would usually be a mistake to aggregate fidigs o these dimesios across segmets. I this example it appears that age may be a useful criterio i segmetig the market sice the choices of differet age groups differ sigificatly from oe aother. However, age may be oe of several criteria that ca be used i explaiig differeces i soft drik choice i this example. Similar cross-tabulatio tables could be costructed examiig the covariatio of other variables that could explai variatios i choice likelihood, such a geder, family or eighborhood icome, lifestyle variables (such as participatio i sports or other social activities), ad the like. There will ofte be multiple measures of a segmet s attractiveess or a brad s effectiveess i servig it. This example looked at choice probability but it may also make sese to cosider other performace variables such as the umber of uits sold per customer or profits per customer. A segmetatio scheme will be more useful whe the segmetatio criteria provide a sigificat explaatio of multiple measures of segmet attractiveess or brad performace. INTERACTION DETECTION ANALYSIS Whe there are may variables that could provide possible segmetatio criteria, it ca be challegig to itegrate them ito a uified segmetatio scheme usig a cross tabular aalysis. Iteractio detectio approaches ca be used to cosider how a set of explaatory variables ca be used to form segmets that explai variatios i a specified depedet variable. As such, these approaches provide a methodology for itegratig ad prioritizig statistical isights that could be obtaied by a set of cross-tabular aalyses. CHAID (Chi-squared Automated Iteractio Detectio) is a widely used techique that selects explaatory variables based o a Chi-squared test betwee the categories of these variables ad the categories of the specified depedet variable. For each potetial explaatory variable, a Chi-squared statistic is computed for each set of categories of it ad the depedet variable. The algorithm typically chooses the explaatory variable with the largest Chi-squared statistic as the first basis for formig subgroups from the total populatio. Oce this split occurs, the aalysis ca be repeated for each of the subgroups to determie if ay of the explaatory variables ca statistically sigificatly form additioal subgroups from the populatio of each of the previously formed subgroups. The process cotiues util either there are o more sigificat splits possible or the user termiates the process. The result is a tree i which the truk ode is comprised of the etire populatio ad the odes defied by each of the braches represet the part of the populatio that falls withi the defied segmet. To illustrate, we retur to the example of the Fizz Cola pael data. As i the cross tabular aalysis, we are attemptig to defie a segmetatio scheme that explais variatios i the depedet variable, brad choice probability. A set of five potetial explaatory variables was tested: age, geder, marital status, a idicator of physical activity (how frequetly they participate i a idividual or team sport), ad a idicator of social activity (how frequetly they get together with o-family members for social activities other tha sports). The CHAID aalysis fidigs are summarized i Figure 1. The algorithm determied that age was the most sigificat variable i explaiig variatios i choice probability. As i the cross tabular aalysis, youger people were more likely to choose Fizz tha older adults. I cotrast to the cross tabular aalysis, it determied that explaatory power was stroger whe the adults i the age group from 21 to 60 were combied ito a sigle category. The explaatory variables were tested to see if ay of them could provide a sigificat explaatio of the variatio withi each of the three age-defied subcategories. The CHAID aalysis determied that variatio i choice probability amog those uder twety ad those over 60 could be sigificatly explaied by geder, with males beig more likely to choose Fizz. It also determied that the variatio i choice prob-

6 ability amog adults i the age group could be best explaied by marital status, with those who are married beig more likely to choose Fizz. The ed result is a set of six segmets that are defied by a combiatio of age, geder ad marital status. Figure 1. CHAID Classificatio Tree AGE CAT EGORY % F ; DF877,2 20 ad Uder (181) (20.6%) GE NDE R % F ; DF179, (206) (231) (49.7%) MAR IT AL STATUS % F ; DF435,1 Over 60 (262) (29.8%) GE NDE R % F ; DF260,1 Male(85) (9.7%) Female(96) (10.9%) Sigle(94) (10.7%) Married(343) (39.0%) Male(129) (14.7%) Female(133) (15.1%) I this example brad choice likelihood was the oly measure of segmet performace/attractiveess cosidered i statistically formig a segmetatio scheme. To be cofidet that this scheme is the most appropriate, it would be useful to cosider other measures to see if a similar patter occurred. I this example, a measure of usage itesity (e.g. umber uits purchased per week or moth) or category expeditures would also be valuable depedet variables i determiig differeces i buyig behavior. Oe could have great cofidece i the segmetatio scheme if a similar segmetatio scheme arose from a CHAID aalysis of those other variables. CLUSTERING METHODS Cross tabular ad iteractio detectio aalysis are procedures that are used to segmet the market by idetifyig criteria that ca be used to idetify criteria that ca be used to divide the aggregate populatio ito statistically sigificat subgroups. By cotrast, clusterig methods start at the idividual level ad form segmets by aggregatig idividuals with similar characteristics ito groups or segmets. To begi the researcher chooses oe or more criteria that will be used to measure similarity betwee idividuals. For segmetatio aalyses these variables are typically chose from survey resposes or behavioral data. Commoly used survey data icludes attitude statemets, attribute importace ratigs or estimates, brad ratigs, ad lifestyle or psychographic statemets. Idividual purchase or usage data may also be used if available. Whe multiple variables are cosidered, it may be ecessary to stadardize them 1 so the differet variables ca receive a comparable weight i 1 To compute a stadardized value for a observatio of a variable oe subtracts the mea of all observatios of that variable ad divides that differece by the stadard deviatio. The result is a trasformed variable with a mea of zero ad a stadard deviatio of oe.

7 measurig similarity betwee idividuals. Care should be exercised i determiig the variables to be used i segmetatio, ofte referred to as basis variables, sice icludig variables that do ot differetiate amog clusters i a meaigful maer causes a serious deterioratio i the results of clusterig methods. While it is geerally ot possible to kow i advace which variables will differetiate amog clusters, the aalyst should be able to form a set of behavioral hypotheses that will guide the selectio of basis variables. A secod decisio cocers the choice type of clusterig approach to be used. There are two commoly applied approaches to clusterig. I a partitio clusterig approach, the aalyst decides i advace how may clusters should be formed. While computatio methodologies vary, a commoly employed procedure will be described ituitively. The first step is to specify seed observatios are eeded, oe for each of the desired clusters. The ext step is to calculate the distace from each of the remaiig observatios to each of the seeds ad assig each observatio to the earest seed to form a iitial set of clusters. Oce this iitial sortig is completed, the algorithm may termiate if the solutio is acceptable or reassig observatios amog the clusters to produce greater homogeeity withi each. A k-meas procedure is commoly used to reassig observatios. This approach calculates a cluster cetroid, which is a poit that miimizes the sum of the squared distace betwee it of each of the observatios i the cluster. The cetroids for each cluster are the used as ew seeds ad each observatio is reassiged to the cetroid to which it is closest. New cetroids are calculated ad the process cotiues util further movemet of the cetroids fails to produce a statistically sigificat improvemet i withi cluster homogeeity ad betwee cluster heterogeeity. A secod approach is kow a hierarchical clusterig. I this procedure similarity or distace measures are computed betwee each observatio ad all others. The clusterig process begis by fidig the pair of observatios that are the most similar to each other i terms of the chose basis variables ad joiig them to form a group. The ext step is to joi the ext closest pair of observatios ad joi them or joi a existig observatio with a previously formed group if the computed distace betwee the observatio ad the group is shorter tha the distace betwee ay pair of uattached observatios. Figure 2 provides a example that illustrates how groups are formed as a fuctio of distace betwee observatios ad groups. I the example, the closest observatios are 2 ad 9 at a distace of approximately oe ad they are joied first. Observatios 17 ad 21 are joied ext followed by observatios 7 ad 11. The ext groupig occurs whe observatio 14 is joied with the group formed by observatios 2 ad 9. The process cotiues util all observatios are joied i a sigle group. As ca be see i Figure 2, the results of the clusterig process ca be depicted i tree-like structures called dedrograms. The aalyst chooses umber of segmets based o the system that proves to be the most useful. There are several issues that should be cosidered whe udertakig a hierarchical cluster aalysis. First is to decide how similarities ad differeces amog observatios should be measured. Whe distaces are used typically some form of Mikowski metric is used. The geeral formula for this type of distace is ij k k k D i j Where, Dij the distace betwee observatios i ad j ik ad jk the ratig of variable i ad j respectively o basis variable k a positive umber The Euclidea distace, which is a Mikowski metric with equal to 2, is the most commoly used metric. Other commoly used Mikowski metrics are the city block metric (1) ad the domiace metric that cosiders oly the dimesio with the maximum differece ( ). A correlatio coefficiet or a measure of matchig coefficiets (percet of commo elemets) may be used if similarities betwee observatios are to be used i formig clusters. The appropriate measure depeds o the type of variables (iterval, biary, or cout) used as a basis for clusterig aalysis ad the aalysts judgmet.

8 Figure 2. Cluster Aalysis Classificatio Dedrogram Subject ID Rescaled Cluster Distace Aother desig decisio cocers how groups should be treated i the clusterig process. The sigle likage method computes the distace betwee clusters as the shortest distace betwee a member of oe group ad a member of aother group. I cotrast the complete likage method computes the distace betwee clusters as the maximum distace betwee a member of oe group ad a member of aother group. Average likage approaches calculate the average distace betwee the members of oe group ad the members of the secod group. Ward s method is aother approach that calculates the total sum of squared deviatios from the mea of a cluster ad jois clusters that produce the smallest possible icrease i the error sum of squares. The choice of likage rule ca have a sigificat impact o the clusters that are formed. For example, complete likage clusters ted to be relatively compact ad cosistig of highly similar observatios but may be sesitive to the orderig of data ad may yield sigificatly differet results if observatios are dropped. I cotrast, Sigle likage methods yield solutios that are less sesitive to the order of the similarity or distace data but may form log elogated clusters. Hierarchical clusterig methods have the advatage of allowig the aalyst to visualize the likage process ad see what observatios joi at each stage. They also do ot require specifyig a umber of clusters a priori. However, hierarchical methods are more computatioally itesive i that distaces must be calculated betwee each pair of poits ad distaces eed to be cotiually recalculated as clusters are formed. Solutios may differ, occasioally dramatically, depedig o the similarity/distace measures ad likage methods used. If the aalyst is cocered about the stability of the results, the most isightful umber of clusters may be determied by a hierarchical aalysis ad the resultig cluster cetroids ca be used as seed values for a partitio clusterig aalysis. Aother potetial limitatio of hierarchical cluster aalysis is its tedecy to form clusters of roughly equal size, eve if the uderlyig populatio would be expected to have segmets with sigificat differeces i size.

9 Oce acceptable clusters are formed, it is importat to iterpret them i terms of the basis variables. A cross tabular aalysis i which oe of the categorized variables is cluster membership ad the other is a basis variable ca reveal systematic deviatios i the basis variable across clusters. Similarly a iteractio detectio aalysis, such as CHAID, ca idetify the correspodece betwee the clusters ad combiatios of basis variables that ca be used to apply the segmetatio scheme to the populatio as whole. Segmetig the market is importat coductig pricig research sice the factors ifluecig willigess to pay ad demad are likely to differ substatially across market segmets. It is critical to idetify these differeces whe possible prior to estimatig demad ad the factors ifluecig a customer s willigess to pay. A failure to do so may result i average estimates that may apply to oe of the market segmets which will usually lead to pricig policy mistakes. Scott Davis is Pricipal ad Fouder of Strategic Marketig Decisios