Segmentation and TargetingBasicsMarket DefinitionSegmentation Research and MethodsBehavior-Based Segmentation

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Market SegmentationMarket segmentation is the subdividing of a market into distinct subsets of customers.

SegmentsMembers are different between segments but similar within.

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Segmentation MarketingDefinitionDifferentiating your product and marketing efforts to meet the needs of different segments, that is, applying the marketing concept to market segmentation.

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Primary Characteristicsof Segments

Basescharacteristics that tell us why segments differ (e.g. needs, preferences, decision processes).Descriptorscharacteristics that help us find and reach segments.(Business markets)(Consumer markets)

IndustryAge/IncomeSizeEducationLocationProfessionOrganizationalLife styles structureMedia habits

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A Two-Stage Approachin Business MarketsMacro-Segments:First stage/rough cutIndustry/applicationFirm size

Micro-Segments:Second-stage/fine cutDifferent customer needs, wants, values within macro-segment

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Relevant Segmentation DescriptorVariable A: Climatic Region 1.Snow Belt 2.Moderate Belt 3.Sun BeltFraction of CustomersLikelihood of Purchasing Solar Water Heater (a)0100%Segment 1Segment 2Segment 3

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Likelihood of Purchasing Solar Water Heater (b)Irrelevant Segmentation DescriptorFraction of Customers0100%Variable B: Education 1.Low Education 2.Moderate Education 3.High EducationSegment 1Segment 2Segment 3

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Variables to Segmentand Describe Markets

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Segmentation in ActionWe segment our customers by letter volume, by postage volume, by the type of equipment they use. Then we segment on whether they buy or lease equipment.Based on this knowledge, we target our marketing messages, fine tune our sales tactics, learn which benefits appeal to which customers and zero in on key decision makers at a company.Kathleen Synnot, VP, Worldwide Marketing Mailing Systems Division, Pitney Bowes, Inc.[quoted in Marketing Masters (Walden and Lawler)]

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SegmentationIf youre not thinking segments, youre not thinking. To think segments means you have to think about what drives customers, customer groups, and the choices that are or might be available to them.Levitt, Marketing Imagination

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STP as Business StrategySegmentationIdentify segmentation bases and segment the market.Develop profiles of resulting segments.

TargetingEvaluate attractiveness of each segment.Select target segments.

PositioningIdentify possible positioning concepts for each target segment.Select, develop, and communicate the chosen concept.

to create and claim value

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Overview of Methods for STPClustering and discriminantanalysisChoice-based segmentation

Perceptual mapping- later

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...D......

Segmentation (for Carpet Fibers)

A,B,C,D: Location of segment centers.Typical members: A:schools B:light commercial C:indoor/outdoorcarpeting D:health clubsDistance betweensegments C and D......Strength(Importance)Water Resistance(Importance)........A...............................CBPerceptions/Ratings for one respondent:Customer Values.

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........................Water Resistance(Importance)

TargetingSegment(s) to serve..........Strength(Importance)

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Product Positioning.Comp 1Comp 2UsWater Resistance(Importance)

Positioning..Strength(Importance)

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A Note on PositioningPositioning involves designing an offering so that the target segment members perceive it in a distinct and valued way relative to competitors.Three ways to position an offering:1.Unique(Only product/service with XXX)2.Difference(More than twice the [feature] vs.[competitor])3.Similarities(Same functionality as [competitor]; lower price)What are you telling your targeted segments?

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Behavior-Based SegmentationTraditional segmentation

(eg, demographic,psychographic)Needs-based segmentationBehavior-based segmentation

(choice models)

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Steps in a Segmentation StudyArticulate a strategic rationale for segmentation (ie, why are we segmenting this market?).Select a set of needs-based segmentation variables most useful for achieving the strategic goals.Select a cluster analysis procedure for aggregating (or disaggregating customers) into segments.Group customers into a defined number of different segments.Choose the segments that will best serve the firms strategy, given its capabilities and the likely reactions of competitors.

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Segmentation: Methods OverviewFactor analysis (to reduce data before cluster analysis).Cluster analysis to form segments.Discriminant analysis to describe segments.

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Cluster Analysis forSegmenting MarketsDefine a measure to assess the similarity of customers on the basis of their needs.Group customers with similar needs. Recommend: the Wards minimum variance criterion and, as an option, the K-Means algorithm for doing this.Select the number of segments using numeric and strategic criteria, and your judgment.Profile the needs of the selected segments (e.g., using cluster means).

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Cluster Analysis IssuesDefining a measure of similarity (or distance) between segments.Identifying outliers.Selecting a clustering procedureHierarchical clustering (e.g., Single linkage, average linkage, and minimum variance methods)Partitioning methods (e.g., K-Means)Cluster profilingUnivariate analysisMultiple discriminant analysis

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Doing Cluster Analysisa=distance from member to cluster centerb=distance from I to III

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Wards Minimum Variance Agglomerative Clustering ProcedureFirst Stage:A =2B =5C =9D =10E =15Second Stage:AB =4.5BD =12.5AC =24.5BE =50.0AD =32.0 CD =0.5AE =84.5CE =18.0BC =8.0DE =12.5Third Stage:CDA =38.0CDB =14.0CDE =20.66AB =5.0AE =85.0 BE =50.5Fourth Stage: ABCD =41.0ABE=93.17CDE =25.18Fifth Stage:ABCDE =98.8

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A98.80Wards Minimum Variance Agglomerative Clustering ProcedureBCDE25.185.000.50

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Discriminant Analysis forDescribing Market Segments

Identify a set of observable variables that helps you to understand how to reach and serve the needs of selected clusters.Use discriminant analysis to identify underlying dimensions (axes) that maximally differentiate between the selected clusters.

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Two-Group Discriminant AnalysisNeed for Data StoragePriceSensitivity XXOXOOO XXXOXXOOOO XXXXOOOXOOO XXOXXOXOOOO XXOXOOOOOOOX-segmentO-segmentx = high propensity to buyo = low propensity to buy

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Interpreting Discriminant Analysis ResultsWhat proportion of the total variance in the descriptor data is explained by the statistically significant discriminant axes?Does the model have good predictability (hit rate) in each cluster? Can you identify good descriptors to find differences between clusters? (Examine correlations between discriminant axes and each descriptor variable).

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PDA Example

PDA Segmentation Performs Wards method - Code:

proc cluster data=hold.pda method=wards standard outtree=treedat pseudo; var InnovatorUse_Message Use_Cell Use_PIM Inf_Passive Inf_ActiveRemote_AccShare_Inf Monitor Email Web M_Media Ergonomic Monthly Price; run;

proc tree data=treedat;run;

PDA Segmentation (alternative) Performs K-means method - Code:

proc fastclus data=hold.pda maxc=4 maxiter=10 random=41 maxiter=50 out=clus; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_ActiveRemote_Acc Share_Inf Monitor Email Web M_Media Ergonomic ; run;

proc means data =clus; var InnovatorUse_MessageUse_Cell Use_PIM Inf_Passive Inf_ActiveRemote_AccShare_Inf Monitor Email Web M_MediaErgonomic MonthlyPrice; by cluster;run;

OutputThe following clusters are quite close together and can be combined with a small loss in consumergrouping information: i) clusters 7 and 5 at 0.27, ii) clusters 1 and 6 at 0.28, ii)fused cluster 7-5 and cluster 2 (0.34).

However, when going from a four-cluster

solution to a three-cluster solution, the distance to be bridged is much larger(1.11); thus, the four-cluster solution is indicated by the ESS. In addition, four seems a reasonable number of segments to handle based on managerial judgment.

Four Cluster Solution profile code;proc tree data = treedata nclusters=4 out=outclus no print;run;

** create new data set;

data temp;merge hold.pda outclus;run;** profile these segments;

proc means data =temp; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_ActiveRemote_Acc Share_Inf Monitor Email Web M_MedErgonomic MonthlyPrice; by cluster;run;

PDA profiles

PDA Visual profile

Chart2

1.13097408650.178574855802.1726607451

0.44269026412.204416825400.2710348556

2.11624127591.549143940502.1162412759

2.317477182800.47521301780.9959944072

1.19871441370.20124402572.1699355810

1.34909627220.3113299092.18729218090

1.18553288132.00843217542.19672269180

0.18163254120.42865279712.17232519220

02.18426876251.23081811220.2426965292

1.96863729480.274842219701.7321451523

1.94594232220.737798036902.0888906918

1.84422301020.288513415701.8838228907

00.21314214071.57319199092.0299251496

00.07286266162.1546529940.8327161329

0.166344543402.10376922591.3503262938

Sheet1

IDInnovatorUse_MessageUse_CellUse_PIMInf_PassiveInf_ActiveRemote_AccShare_InfMonitorEmailWebM_MediaErgonomicMonthlyPrice

1415746314556415280

2547565654736115310

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Sheet2

Cluster1234Recode1234Scaled to rangeSeg-1Seg-2Seg-3Seg-4

1Innovator3.712.432.195.11INNOVATOR0001INNOVATOR1.13097408650.178574855802.1726607451

2Use_Messag3.685.633.193.49USE_MESSAGE0100USE_MESSAGE0.44269026412.204416825400.2710348556

3Use_Cell5.845.434.315.84USE_CELL1001USE_CELL2.11624127591.549143940502.1162412759

4Use_PIM5.892.333.063.86USE_PIM1000USE_PIM2.317477182800.47521301780.9959944072

5Inf_Passiv5.023.886.133.65INF_PASSIVE0010INF_PASSIVE1.19871441370.20124402572.1699355810

6Inf_Active5.23.96.253.51INF_ACTIVE0010INF_ACTIVE1.34909627220.3113299092.18729218090

7Remote_Acc3.865.045.312.16REMOTE_ACC0010REMOTE_ACC1.18553288132.00843217542.19672269180

8Share_Inf3.393.736.133.14SHARE_INF0010SHARE_INF0.18163254120.42865279712.17232519220

9Monitor4.295.5554.43MONITOR0100MONITOR02.18426876251.23081811220.2426965292

10Email5.963.312.885.59EMAIL1000EMAIL1.96863729480.274842219701.7321451523

11Web5.663.041.445.97WEB0001WEB1.94594232220.737798036902.0888906918

12M_Media5.22.451.945.27M_MEDIA0001M_MEDIA1.84422301020.288513415701.8838228907

13Ergonomic3.954.165.55.95ERGONOMIC0001ERGONOMIC00.21314214071.57319199092.0299251496

14Monthly24.625.345.332.6MONTHLY0010MONTHLY00.07286266162.1546529940.8327161329

15Price290273488411PRICE0010PRICE0.166344543402.10376922591.3503262938

0

Sheet2

Sheet3

4.9512195123.6785714292.3333333332.333333333

3.6341463413.755.5777777783.5

5.8292682935.8035714295.4666666674.388888889

3.8536585375.96428571423.277777778

3.8292682934.9107142863.7555555566.166666667

3.560975615.2857142863.7111111116.166666667

2.121951223.9285714295.3555555565

3.2682926833.3571428573.6666666675.944444444

4.439024394.2857142865.6444444445.055555556

5.6097560985.9107142863.1555555562.944444444

5.9512195125.53.0222222221.555555556

5.2682926835.1252.2666666672.055555556

5.80487804943.9777777785.555555556

2.08346864371.070620034500

0.13685580620.25504945782.11974438310

2.13082472882.09281008271.59441014990

1.12140540432.398268793900.7730155674

0.06514571971.020904255502.1308877557

01.36809016250.11908986842.0668756091

01.24387846312.22637318671.9815691618

00.07004806020.31407169552.1098353677

0.246488889602.18453891311.2377352496

1.69556009911.88701714480.13430008360

2.11255095651.89569538670.70487828440

1.82993142731.74831386150.12024602120

1.86092997510.022633677901.6069911475

Means

VariableOverallCL1CL2CL3CL4

Innovator3.473.712.432.195.11

Use_Messag4.213.685.633.193.49

Use_Cell5.565.845.434.315.84

Use_PIM4.015.892.333.063.86

Inf_Passiv4.455.023.886.133.65

Inf_Active4.55.23.96.253.51

Remote_Acc3.993.865.045.312.16

Share_Inf3.713.393.736.133.14

Monitor4.794.295.5554.43

Email4.725.963.312.885.59

Web4.475.663.041.445.97

M_Media4.015.22.451.945.27

Ergonomic4.633.954.165.55.95

Monthly28.824.625.345.332.6

Price332290273488411

Proportion0.350.3190.10.231

PDA Visual profile

PDA profilesCluster 1. Phone users who use Personal Information Management software, to whom Email and Web access, as well as Multimedia capabilities are important.

Cluster 2. People who use messaging services and cell phones, need remote access to information, appreciate better monitors, but not for multi-media usage.

PDA profiles..Cluster 3. Pager users who have a high need for fast information sharing (receiving as well as sending) and also remote access. They use neither email extensively, nor the Web, nor Multi-media, but do require a handy, non-bulky device.

Cluster 4. Innovators who use cell phones a lot, have a high need for Email, Web, and Multi-media use. They also require a sleek device.

Profile based on Demos/behaviour

Name the segmentsCluster 1 - Sales Pros:Cluster 1 consists mainly of sales professionals: 54% of the cluster membersindicated Sales as their occupation. They use the cell phone heavily, and many(45%) own a PDA already; practically all have access to a PC. Their work oftentakes them away from the office. They mostly read two of the selectedmagazines: 30% read BW. From the needs data, we see that they are quite pricesensitive.

Cluster 2 Service Pros:Cluster 2 is made up primarily of service personnel (39%) and secondarily ofsales personnel (23%). They use cell phones heavily, but only about one fifthcurrently use a PDA. They spend much time on the road and in remote locations.They read PC Magazine, 29%. From the needs data, we see that they are quiteprice sensitive.

Name the segmentsCluster 3 Hard Hats:Cluster 3 is made up predominantly of construction (31%) and emergency (19%)workers. They use cell phones, but usually do not own a PDA. By the nature of their work, they have high information relay needs and generally work in remote locations.

They exchange information with colleagues in the field (e.g. construction workers on the site). Many read Field & Stream (31%) and also PC Magazine. Note also from the needs data, that they are the least price sensitive (willing to pay highest price plus monthly fee) and also have the lowest income.

This apparent anomaly occurs because these folks are less likely to have to pay for the device themselves, raising the question of whose preferencestheir own or their employerswill drive the adoption decision

Name the segmentsCluster 4 Innovators:Cluster 4 represents early adopters (see needs data), predominantly professionals (lawyers, consultants, etc.). Every cluster member has access to a PC, 89 percent already own PDAs. They read many magazines, especially BW 49%, PCMag 32%. Most are highly paid and highly educated.

Who to targetDiscuss.

Interpreting Cluster Analysis ResultsSelect the appropriate number of clusters:Are the bases variables highly correlated? (Should we reduce the data through factor analysis before clustering?)Are the clusters separated well from each other?Should we combine or separate the clusters? Can you come up with descriptive names for each cluster (eg, professionals, techno-savvy, etc.)? Segment the market independently of your ability to reach the segments (i.e., separately evaluate segmentation and discriminant analysis results).

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Discrimination based on demographics/behaviourproc discrim data=temp outstat=outdisc method=normal pool=yes list crossvalidate; class cluster; priors prop; vars age education etc ; run;

** all relevant vars. not used to create segment solutions;

Discrimination based on demographics/behaviourThis allows us a way to target and profile future customers:

Discrimination based on demographics/behaviour

Discrimination based on demographics/behaviourThe first discriminant function above explains 51% the variation. According to its coefficients, i.e., the four groups are particularly different with respect to the amount away from the office.

In addition, the function shares high correlation with the level of education, possession of a PDA, and income.

The second function explains 32% of the variance and primarily distinguishes the occupation types construction/emergency from sales/service, and the third function separates Sales and Service types.

Visualising relationships

Correspondence AnalysisProvides a graphical summary of the interactions in a tableAlso known as a perceptual mapBut so are many other chartsCan be very usefulE.g. to provide overview of cluster resultsHowever the correct interpretation is less than intuitive, and this leads many researchers astray

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InterpretationCorrespondence analysis plots should be interpreted by looking at points relative to the originPoints that are in similar directions are positively associatedPoints that are on opposite sides of the origin are negatively associatedPoints that are far from the origin exhibit the strongest associationsAlso the results reflect relative associations, not just which rows are highest or lowest overall

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Software for Correspondence AnalysisEarlier chart was created using a specialised package called BRANDMAPCan also do correspondence analysis in most major statistical packagesFor example, using PROC CORRESP in SAS:

*---Perform Simple Correspondence AnalysisExample 1 in SAS OnlineDoc; proc corresp all data=Cars outc=Coor; tables Marital, Origin; run;

*---Plot the Simple Correspondence Analysis Results---; %plotit(data=Coor, datatype=corresp)

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Cars by Marital Status

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Segmentations Other details

Tandem SegmentationOne general method is to conduct a factor analysis, followed by a cluster analysisThis approach has been criticised for losing information and not yielding as much discrimination as cluster analysis aloneHowever it can make it easier to design the distance function, and to interpret the results

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Tandem k-means Exampleproc factor data=datafile n=6 rotate=varimax round reorder flag=.54 scree out=scores; var reasons1-reasons15 usage1-usage10;run;

proc fastclus data=scores maxc=4 seed=109162319 maxiter=50; var factor1-factor6;run;

Have used the default unweighted Euclidean distance function, which is not sensible in every contextAlso note that k-means results depend on the initial cluster centroids (determined here by the seed)Typically k-means is very prone to local maximaRun at least 20 times to ensure reasonable maximum

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Cluster Analysis OptionsThere are several choices of how to form clusters in hierarchical cluster analysisSingle linkageAverage linkageDensity linkageWards methodMany othersWards method (like k-means) tends to form equal sized, roundish clustersAverage linkage generally forms roundish clusters with equal varianceDensity linkage can identify clusters of different shapes

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FASTCLUS

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Density Linkage

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Cluster Analysis IssuesDistance definitionWeighted Euclidean distance often works well, if weights are chosen intelligentlyCluster shapeShape of clusters found is determined by method, so choose method appropriatelyHierarchical methods usually take more computation time than k-meansHowever multiple runs are more important for k-means, since it can be badly affected by local minimaAdjusting for response styles can also be worthwhileSome people give more positive responses overall than othersClusters may simply reflect these response styles unless this is adjusted for, e.g. by standardising responses across attributes for each respondent

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MVA - FASTCLUSPROC FASTCLUS in SAS tries to minimise the root mean square difference between the data points and their corresponding cluster meansIterates until convergence is reached on this criterionHowever it often reaches a local minimumCan be useful to run many times with different seeds and choose the best set of clusters based on this RMS criterionSee http://en.wikipedia.org/wiki/K-means_clustering for more k-means issues

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Iteration History from FASTCLUS Relative Change in Cluster Seeds Iteration Criterion 1 2 3 4 5 1 0.9645 1.0436 0.7366 0.6440 0.6343 0.5666 2 0.8596 0.3549 0.1727 0.1227 0.1246 0.0731 3 0.8499 0.2091 0.1047 0.1047 0.0656 0.0584 4 0.8454 0.1534 0.0701 0.0785 0.0276 0.0439 5 0.8430 0.1153 0.0640 0.0727 0.0331 0.0276 6 0.8414 0.0878 0.0613 0.0488 0.0253 0.0327 7 0.8402 0.0840 0.0547 0.0522 0.0249 0.0340 8 0.8392 0.0657 0.0396 0.0440 0.0188 0.0286 9 0.8386 0.0429 0.0267 0.0324 0.0149 0.0223 10 0.8383 0.0197 0.0139 0.0170 0.0119 0.0173

Convergence criterion is satisfied.

Criterion Based on Final Seeds = 0.83824

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Results from Different Initial Seeds19th run of 5 segments

Cluster Means

Cluster FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 1 -0.17151 0.86945 -0.06349 0.08168 0.14407 1.17640 2 -0.96441 -0.62497 -0.02967 0.67086 -0.44314 0.05906 3 -0.41435 0.09450 0.15077 -1.34799 -0.23659 -0.35995 4 0.39794 -0.00661 0.56672 0.37168 0.39152 -0.40369 5 0.90424 -0.28657 -1.21874 0.01393 -0.17278 -0.00972

20th run of 5 segments

Cluster Means

Cluster FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 1 0.08281 -0.76563 0.48252 -0.51242 -0.55281 0.64635 2 0.39409 0.00337 0.54491 0.38299 0.64039 -0.26904 3 -0.12413 0.30691 -0.36373 -0.85776 -0.31476 -0.94927 4 0.63249 0.42335 -1.27301 0.18563 0.15973 0.77637 5 -1.20912 0.21018 -0.07423 0.75704 -0.26377 0.13729

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Howard-Harris ApproachProvides automatic approach to choosing seeds for k-means clusteringChooses initial seeds by fixed procedureTakes variable with highest variance, splits the data at the mean, and calculates centroids of the resulting two groupsApplies k-means with these centroids as initial seedsThis yields a 2 cluster solutionChoose the cluster with the higher within-cluster varianceChoose the variable with the highest variance within that cluster, split the cluster as above, and repeat to give a 3 cluster solutionRepeat until have reached a set number of clustersI believe this approach is used by the ESPRI software package (after variables are standardised by their range)

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Another Clustering MethodOne alternative approach to identifying clusters is to fit a finite mixture modelAssume the overall distribution is a mixture of several normal distributionsTypically this model is fit using some variant of the EM algorithmE.g. weka.clusterers.EM method in WEKA data mining packageSee WEKA tutorial for an example using Fishers iris dataAdvantages of this method include:Probability model allows for statistical testsHandles missing data within model fitting processCan extend this approach to define clusters based on model parameters, e.g. regression coefficientsAlso known as latent class modeling

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Segmentations via Choice Modelling

Choice Models1.Observe choice: (Buy/not buy => direct marketersBrand bought=> packaged goods, ABB)2.Capture related data:demographicsattitudes/perceptionsmarket conditions (price, promotion, etc.)

3.Link1 to 2 via choice model => model revealsimportance weights of characteristics

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Choice Models vs SurveysWith standard survey methods . . .preference/importancechoice weights perceptionspredictobserve/askobserve/askBut with choice models . . .importance choice weights perceptionsobserveinferobserve/ask

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Behavior-Based Segmentation ModelStage 1:Screen products using key attributes to identify the consideration set of suppliers for each type of customer.Stage 2:Assume that customers (of each type) will choose suppliers to maximize their utility via a random utility model. Uij = Vij + eijwhere:Uij=Utility that customer i has for supplier js product.Vij=Deterministic component of utility that is a function of product and supplier attributes.eij=An error term that reflects the non-deterministic component of utility.

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Specification of the Deterministic Component of UtilityVij = Wk bijkk where:i=an index to represent customers, j is an index to represent suppliers, and k is an index to represent attributes.bijk=is perception of attribute k for supplier j.wk=estimated coefficient to represent the impact of bijk on the utility realized for attribute k of supplier j for customer i.

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A Key Result from this Specification:The Multinomial Logit (MNL) ModelIf customers past choices are assumed to reflect the principleof utility maximization and the error (eij) has a specific formcalled double exponential, then:eVijpij= k eVik where:pij=probability that customer i chooses supplier j.Vij=estimated value of utility (ie, based on estimates of bijk) obtained from maximum likelihood estimation.^^^

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Key idea: Segment on the basis of probability of choice1.Loyal to us2.Loyal to competitor3.Switchables: loseable/winnable customers

Applying the MNL Model in Segmentation Studies

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Switchability SegmentationCurrent Product-Market by SwitchabilityQuestions:Where should your marketing efforts be focused?How can you segment the market this way?

Loyal to UsLosableWinnable Customers (business to gain)Loyal toCompetitor

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Using Choice-Based Segmentation for Database Marketing A B C DAverageCustomerPurchasePurchaseProfitabilityCustomerProbabilityVolumeMargin= A B C130%$31.000.70$6.5122%$143.000.60$1.72310%$54.000.67$3.6245%$88.000.62$2.73560%$20.000.58$6.96622%$60.000.47$6.20711%$77.000.38$3.22813%$39.000.66$3.3591%$184.000.56$1.03104%$72.000.65$1.87

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Managerial Uses of Segmentation AnalysisSelect attractive segments for focused effort (Can use models such as Analytic Hierarchy Process or GE Planning Matrix).Develop a marketing plan (4Ps and positioning) to target selected segments.In consumer markets, we typically rely on advertising and channel members to selectively reach targeted segments. In business markets, we use sales force and direct marketing. You can use the results from the discriminant analysis to assign new customers to one of the segments.

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Checklist for Segmentation StudiesIs it values, needs, or choice-based? Whose values and needs?Is it a projectable sample?Is the study valid? (Does it use multiple methods and multiple measures)Are the segments stable?Does the study answer important marketing questions (product design, positioning, channel selection, sales force strategy, sales forecasting)Are segmentation results linked to databases?Is this a one-time study or is it a part of a long-term program?

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Concluding RemarksIn summary,Use needs variables to segment markets.Select segments taking into account both the attractiveness of segments and the strengths of the firm. Use descriptor variables to develop a marketing plan to reach and serve chosen segments.Develop mechanisms to implement the segmentation strategy on a routine basis (one way to do this is through information technology).

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