Segmentation is a process of finding groups of customers who are similar to one another, are different from other groups, and exhibit differences that are important for the business.

There is no magic method to solve all three of those requirements simultaneously.

Segmentation requires trying multiple methods and evaluating the results to determine whether they are useful for the business question.

It often occurs that the statistically “best” segmentation is difficult to understand in the business context. A model that is statistically “not as strong” – but is clear and actionable – may be a preferable result.

In this chapter, we give an overview of methods to demonstrate some common approaches.

Clustering is the process of finding groups inside data. Key problems include:

• Determining which variables to use
• Finding the right number of clusters
• Ensuring the groups differ in interesting ways

Classification is the process of assigning observations (e.g., customers) to known categories (segments, clusters). Some important concerns are:

• Predicting better than chance
• Optimizing for positive vs. negative prediction
• Generalizing to new data sets

seg.raw <- read.csv("http://goo.gl/qw303p")
seg.df  <- seg.raw[ , -7]     # remove the known segment assignments

summary(seg.df)

      age           gender        income            kids        ownHome   
 Min.   :19.26   Female:157   Min.   : -5183   Min.   :0.00   ownNo :159  
 1st Qu.:33.01   Male  :143   1st Qu.: 39656   1st Qu.:0.00   ownYes:141  
 Median :39.49                Median : 52014   Median :1.00               
 Mean   :41.20                Mean   : 50937   Mean   :1.27               
 3rd Qu.:47.90                3rd Qu.: 61403   3rd Qu.:2.00               
 Max.   :80.49                Max.   :114278   Max.   :7.00               
  subscribe  
 subNo :260  
 subYes: 40  

We create a simple function to look at mean values by group. (This is a placeholder for a more complex evaluation of an interpretable business outcome.)

seg.summ <- function(data, groups) {
  aggregate(data, list(groups), function(x) mean(as.numeric(x)))  
}

seg.summ(seg.df, seg.raw$Segment)

     Group.1      age gender   income     kids  ownHome subscribe
1  Moving up 36.33114   1.30 53090.97 1.914286 1.328571     1.200
2 Suburb mix 39.92815   1.52 55033.82 1.920000 1.480000     1.060
3  Travelers 57.87088   1.50 62213.94 0.000000 1.750000     1.125
4  Urban hip 23.88459   1.60 21681.93 1.100000 1.200000     1.200

Clustering methods work by looking at some measure of the distance between observations. They try to find groups whose members are close to one another (and far from others).

A common metric is Euclidian distance, the root square of differences. Manually we could compute:

c(1,2,3) - c(2,3,2)

[1] -1 -1  1

sum((c(1,2,3) - c(2,3,2))^2)

[1] 3

sqrt(sum((c(1,2,3) - c(2,3,2))^2))

[1] 1.732051

Note that distance is between observations, and the result is a matrix of distances between all pairs (in this case, just one pair).

dist() computes Euclidian distance:

sqrt(sum((c(1,2,3) - c(2,3,2))^2))

[1] 1.732051

dist(rbind(c(1,2,3), c(2,3,2)))

         1
2 1.732051

In case of mixed data types (e.g., continuous, binary, ordinal), dist() may not be appropriate because of the huge implied scale differences. daisy() is an alternative that automatically rescales.

library(cluster)                  
seg.dist <- daisy(seg.df)       # daisy works with mixed data types
as.matrix(seg.dist)[1:4, 1:4]   # distances of first 4 observations

          1         2         3         4
1 0.0000000 0.2532815 0.2329028 0.2617250
2 0.2532815 0.0000000 0.0679978 0.4129493
3 0.2329028 0.0679978 0.0000000 0.4246012
4 0.2617250 0.4129493 0.4246012 0.0000000

From the previous tree, we select observations from close and far branches:

seg.df[c(101, 107), ]  # similar

         age gender   income kids ownHome subscribe
101 24.73796   Male 18457.85    1   ownNo    subYes
107 23.19013   Male 17510.28    1   ownNo    subYes

seg.df[c(278, 294), ]  # similar

         age gender   income kids ownHome subscribe
278 36.23860 Female 46540.88    1   ownNo    subYes
294 35.79961 Female 52352.69    1   ownNo    subYes

seg.df[c(173, 141), ]  # less similar

         age gender   income kids ownHome subscribe
173 64.70641   Male 45517.15    0   ownNo    subYes
141 25.17703 Female 20125.80    2   ownNo    subYes

The cophenetic correlation coefficient is a measure of how well the clustering model (expressed in the dendrogram) reflects the distance matrix.

cor(cophenetic(seg.hc), seg.dist)

[1] 0.7682436

Get a vector of class (cluster) assignment:

seg.hc.segment <- cutree(seg.hc, k=4)     # membership vector for 4 groups
table(seg.hc.segment)

seg.hc.segment
  1   2   3   4 
124 136  18  22 

Compare them with our quick function:

seg.summ(seg.df, seg.hc.segment)

  Group.1      age   gender   income     kids  ownHome subscribe
1       1 40.78456 2.000000 49454.08 1.314516 1.467742         1
2       2 42.03492 1.000000 53759.62 1.235294 1.477941         1
3       3 44.31194 1.388889 52628.42 1.388889 2.000000         2
4       4 35.82935 1.545455 40456.14 1.136364 1.000000         2

K-means attempts to find K groups, such that the sum of squared distances for points within clusters are minimized with respect to the cluster center.

To compute this, K-means requires numeric input and a specified number of clusters. We first convert our factor variables to numeric (arguably OK because they're binary):

seg.df.num <- seg.df
seg.df.num$gender    <- ifelse(seg.df$gender=="Male", 0, 1)
seg.df.num$ownHome   <- ifelse(seg.df$ownHome=="ownNo", 0, 1)
seg.df.num$subscribe <- ifelse(seg.df$subscribe=="subNo", 0, 1)
summary(seg.df.num)

      age            gender           income            kids     
 Min.   :19.26   Min.   :0.0000   Min.   : -5183   Min.   :0.00  
 1st Qu.:33.01   1st Qu.:0.0000   1st Qu.: 39656   1st Qu.:0.00  
 Median :39.49   Median :1.0000   Median : 52014   Median :1.00  
 Mean   :41.20   Mean   :0.5233   Mean   : 50937   Mean   :1.27  
 3rd Qu.:47.90   3rd Qu.:1.0000   3rd Qu.: 61403   3rd Qu.:2.00  
 Max.   :80.49   Max.   :1.0000   Max.   :114278   Max.   :7.00  
    ownHome       subscribe     
 Min.   :0.00   Min.   :0.0000  
 1st Qu.:0.00   1st Qu.:0.0000  
 Median :0.00   Median :0.0000  
 Mean   :0.47   Mean   :0.1333  
 3rd Qu.:1.00   3rd Qu.:0.0000  
 Max.   :1.00   Max.   :1.0000  

We try K=4 groups:

set.seed(96743)        # because starting assignments are random
seg.k <- kmeans(seg.df.num, centers=4)

And examine the quick comparison:

seg.summ(seg.df, seg.k$cluster)

  Group.1      age   gender   income      kids  ownHome subscribe
1       1 56.37245 1.428571 92287.07 0.4285714 1.857143  1.142857
2       2 29.58704 1.571429 21631.79 1.0634921 1.301587  1.158730
3       3 44.42051 1.452632 64703.76 1.2947368 1.421053  1.073684
4       4 42.08381 1.454545 48208.86 1.5041322 1.528926  1.165289

It appears potentially more interesting than the hclust “men and women” solution we saw previously.

Model-based clustering (mixture modeling) assumes that observations are mixed from populations with different distributions of the basis variables (i.e., different means and variances).

mclust is one package to find such models (flexmix is another). The data must be numeric. Unlike hclust and kmeans, mclust suggests the number of groups, based on fit statistics.

library(mclust)
seg.mc <- Mclust(seg.df.num)   # use all defaults
summary(seg.mc)

----------------------------------------------------
Gaussian finite mixture model fitted by EM algorithm 
----------------------------------------------------

Mclust EVE (ellipsoidal, equal volume and orientation) model with 2 components:

 log.likelihood   n df       BIC       ICL
      -5197.499 300 39 -10617.45 -10636.59

Clustering table:
  1   2 
247  53 

We can force mclust to find other numbers of clusters:

seg.mc4 <- Mclust(seg.df.num, G=4)  # 4 clusters
summary(seg.mc4)

----------------------------------------------------
Gaussian finite mixture model fitted by EM algorithm 
----------------------------------------------------

Mclust EVE (ellipsoidal, equal volume and orientation) model with 4 components:

 log.likelihood   n df       BIC       ICL
      -5325.941 300 63 -11011.22 -11127.95

Clustering table:
  1   2   3   4 
 64  54 163  19 

Compare the 2-cluster and 4-cluster solutions:

BIC(seg.mc, seg.mc4)

        df     BIC
seg.mc  39 1281140
seg.mc4 63 1281276

seg.summ(seg.df, seg.mc$class)

  Group.1      age   gender   income      kids  ownHome subscribe
1       1 40.17283 1.485830 52653.51 1.3400810 1.449393  1.000000
2       2 45.98504 1.433962 42934.79 0.9433962 1.566038  1.754717

A primary differentiator appears to be subscription status. Group 1 is non-subscribers, with lower average age and higher income. They might be good targets for a campaign, depending on the business goal.

(Note: Mclust results tend to change from version to version. The results here differ from older versions shown in the textbook. There is no absolute “right” answer for many questions of this kind!)

Polytomous latent class analysis attempts to find mixture membership (latent classes) using only categorical data.

To illustrate, we create a data frame with sliced versions of continuous variables converted to factors:

seg.df.cut        <- seg.df
seg.df.cut$age    <- factor(ifelse(seg.df$age < median(seg.df$age), 
                                   "LessAge", "MoreAge"))
seg.df.cut$income <- factor(ifelse(seg.df$income < median(seg.df$income),
                                   "LessInc", "MoreInc"))
seg.df.cut$kids   <- factor(ifelse(seg.df$kids < median(seg.df$kids), 
                                   "FewKids", "MoreKids"))
summary(seg.df.cut)

      age         gender        income          kids       ownHome   
 LessAge:150   Female:157   LessInc:150   FewKids :121   ownNo :159  
 MoreAge:150   Male  :143   MoreInc:150   MoreKids:179   ownYes:141  
  subscribe  
 subNo :260  
 subYes: 40  

Note factor level alphabetization; consider ordinal levels if relevent.

For simplicity, we create a reusable model formula:

seg.f <- with(seg.df.cut, 
              cbind(age, gender, income, kids, ownHome, subscribe)~1)

Then fit 3- and 4-group models:

library(poLCA)
set.seed(02807)
seg.LCA3 <- poLCA(seg.f, data=seg.df.cut, nclass=3)
seg.LCA4 <- poLCA(seg.f, data=seg.df.cut, nclass=4)

Check the model fits:

seg.LCA4$bic

[1] 2330.043

seg.LCA3$bic

[1] 2298.767

The 3-group model had stronger fit. But is it more useful?

Given two assignment vectors, it's not obvious which categories should match to one another. Function mapClass finds the highest correspondence:

library(mclust)
mapClass(seg.LCA3$predclass, seg.LCA4$predclass)

$aTOb
$aTOb$`1`
[1] 4

$aTOb$`2`
[1] 2

$aTOb$`3`
[1] 1


$bTOa
$bTOa$`1`
[1] 3

$bTOa$`2`
[1] 2

$bTOa$`3`
[1] 1

$bTOa$`4`
[1] 1

The adjusted Rand index is the degree of agreement between two class assignment vectors, where 1.0 indicates perfect agreement:

adjustedRandIndex(seg.LCA3$predclass, seg.LCA4$predclass)

[1] 0.7288822

We could compare this to purely random assignment:

set.seed(11021)
random.data <- sample(4, length(seg.LCA4$predclass), replace=TRUE)
adjustedRandIndex(random.data, seg.LCA4$predclass)

[1] 0.002292031

… and to known segments from the original data:

adjustedRandIndex(seg.raw$Segment, seg.LCA4$predclass)

[1] 0.3513031

Next we'll examine classification methods. Whereas clustering has a lot of “art” in determining when a solution is useful, classification tends to emphasize the “science” of predicting and generalizing.

Classification concerns assigning observations to a predefined set of groups. For example, we might wish to assign potential customers to higher- or lower-value prospects … or more generally, assign members of the overall population to a set of customer segments.

Classification can also assign customers to categories reflecting observed behavior, such as “purchasers.” It is one form of predictive modeling.

General steps in classification are:

1. Collect data with predictors & outcome
   
2. Divide the observations into training and test cases
   
3. Use training cases to fit a model predicting the outcomes
   
4. Confirm that the model works well for test cases
   
5. Apply the model to new data to obtain predictions
   

The naive Bayes method is simple yet powerful. It uses Bayes's Rule to find the odds of class membership based on the conditional probabilities of predictor variables (assumed to be independent, thus “naive”).

More specifically, it computes conditional probabilities in the training data. The resulting model can then predict group membership probabilities for new observations (given the same predictors).

First we divide data into training and test cases:

set.seed(04625)          # make it repeatable
train.prop   <- 0.65     # train on 65% of data. Hold 35% for testing
train.cases  <- sample(nrow(seg.raw), nrow(seg.raw)*train.prop)
seg.df.train <- seg.raw[ train.cases, ]
seg.df.test  <- seg.raw[-train.cases, ]

We fit the model to training data:

library(e1071)
seg.nb <- naiveBayes(Segment ~ ., data=seg.df.train)

… and predict membership expected in the test (holdout) data:

seg.nb.class <- predict(seg.nb, seg.df.test)

prop.table(table(seg.nb.class))

seg.nb.class
 Moving up Suburb mix  Travelers  Urban hip 
 0.2285714  0.3047619  0.3428571  0.1238095 

We could compare the proportion of correct assignments:

mean(seg.df.test$Segment==seg.nb.class)   # raw correct proportion

[1] 0.8

… but better is to assess vs. chance assignment. (If one group is very large, we can't say a model is good just because it mostly predicts that group.)

library(mclust)
adjustedRandIndex(seg.nb.class, seg.df.test$Segment)

[1] 0.5626787

Even better would be a weighted payoff matrix, taking into account the value of correct positive and negative predictions. That depends on the business application (such as cost of targeting vs. margin from a success).

Another way to do classification is with a decision tree: find an optimal decision path to predict the outcomes. A random forest model generalizes this to use many trees – fitting different predictors and observations – that “vote” on classification.

Like naive Bayes classifiers, Random forests are simple and easy to use, yet often perform well.

We fit a random forest with 3000 individual trees, on the training data:

library(randomForest)
set.seed(98040)
seg.rf <- randomForest(Segment ~ ., data=seg.df.train, ntree=3000)

A simple inspection of the model shows key results:

seg.rf

Call:
 randomForest(formula = Segment ~ ., data = seg.df.train, ntree = 3000) 
               Type of random forest: classification
                     Number of trees: 3000
No. of variables tried at each split: 2

        OOB estimate of  error rate: 24.1%
Confusion matrix:
           Moving up Suburb mix Travelers Urban hip class.error
Moving up         29         19         0         1  0.40816327
Suburb mix        20         35         3         1  0.40677966
Travelers          0          3        48         0  0.05882353
Urban hip          0          0         0        36  0.00000000

It includes initial performance tests on holout data (from the training data), and was correct 76% of the time. It was best for Urban hip, with more error for Moving up & Suburb mix.

Concern about error rates depends on interest in the segments.

random forest

The predictions optionally include odds for each observation:

seg.rf.class.all <- predict(seg.rf, seg.df.test, predict.all=TRUE)

# odds for first five test cases (divide votes by 3000 trees)
apply(seg.rf.class.all$individual[1:5, ], 1, table) / 3000

                    2           3         4         6     7
Moving up  0.42066667 0.076333333 0.1886667 0.1223333 0.217
Suburb mix 0.47266667 0.485000000 0.6930000 0.8526667 0.340
Travelers  0.02966667 0.436333333 0.1173333 0.0240000 0.050
Urban hip  0.07700000 0.002333333 0.0010000 0.0010000 0.393

Suppose you are interested to target one segment. If targeting cost is high, you might target only the subset with the highest odds.

OTOH, if targeting cost is low (relative to payoff), you might target customers assigned to other segments, who have some chance of being in the target segment. For example, if you're targeting Moving up, you might want to target respondent #2, even though she is slightly more likely to be Suburb mix.

Who is most likely to subscribe to our service? Can we predict that, given other information? Can we find out which individuals are best to target?

Procedure:

1. Use random forests to predict subscription in training data
   
2. See how well the model performs in test data
   
3. Examine individual respondents' likelihoods (whom should we target?)
   

In real application, you would do #3 with new data. For demonstration purposes we'll use the holdout test data.

We form training and test (holdout) data sets:

set.seed(92118)
train.prop  <- 0.65
train.cases <- sample(nrow(seg.df), nrow(seg.df)*train.prop)
sub.df.train <- seg.raw[ train.cases, ]
sub.df.test  <- seg.raw[-train.cases, ]

summary(sub.df.train)

      age           gender        income            kids      
 Min.   :20.71   Female:105   Min.   : -5183   Min.   :0.000  
 1st Qu.:33.16   Male  : 90   1st Qu.: 40556   1st Qu.:0.000  
 Median :39.09                Median : 51413   Median :1.000  
 Mean   :41.40                Mean   : 50322   Mean   :1.308  
 3rd Qu.:47.55                3rd Qu.: 60974   3rd Qu.:2.000  
 Max.   :80.49                Max.   :106430   Max.   :7.000  
   ownHome     subscribe         Segment  
 ownNo :103   subNo :170   Moving up :51  
 ownYes: 92   subYes: 25   Suburb mix:62  
                           Travelers :54  
                           Urban hip :28  

library(randomForest)
set.seed(11954)
(sub.rf <- randomForest(subscribe ~ ., data=sub.df.train, ntree=3000))

Call:
 randomForest(formula = subscribe ~ ., data = sub.df.train, ntree = 3000) 
               Type of random forest: classification
                     Number of trees: 3000
No. of variables tried at each split: 2

        OOB estimate of  error rate: 14.87%
Confusion matrix:
       subNo subYes class.error
subNo    166      4  0.02352941
subYes    25      0  1.00000000

Error rate is low … but looking at the confusion matrix, no subscribers were correctly identified!

So, for our business question, this may not be a useful model (it predicts almost all “won't subscribe”).

With few subscribers in the training data, the algorithm achieves low error rates simply by predicting “non-subscriber”. This is known as the class imbalance problem.

We can force balanced classes by explicitly setting per-tree sample sizes for classes. Overall error goes up, but goes down for subscribers:

set.seed(11954)
(sub.rf <- randomForest(subscribe ~ ., data=sub.df.train, ntree=3000, 
                       sampsize=c(25, 25)) )   # balanced classes

Call:
 randomForest(formula = subscribe ~ ., data = sub.df.train, ntree = 3000,      sampsize = c(25, 25)) 
               Type of random forest: classification
                     Number of trees: 3000
No. of variables tried at each split: 2

        OOB estimate of  error rate: 30.77%
Confusion matrix:
       subNo subYes class.error
subNo    127     43   0.2529412
subYes    17      8   0.6800000

We get predictions for both classes using predict.all:

sub.rf.sub <- predict(sub.rf, sub.df.test, predict.all=TRUE)

# Not in book: 
#   Get the proportion within respondent for the two classes,
#     using table and prop.table apply()'d to each respondents.
#   Then get just the row with predictons for subscribers ("subYes")

sub.ind.p  <- apply(sub.rf.sub$individual, 1, 
                    function(x) prop.table(table(x)))["subYes", ]
summary(sub.ind.p)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1253  0.3050  0.4043  0.3928  0.4553  0.7853 

Finding likelihoods with a model does not answer it will work; checking with holdout data is necessary. (Ideally also check in a new sample).

Compare subscription rates in holdout data, for likelihood $\ge$ 0.5 vs. lower:

table(targeted=sub.ind.p >= 0.5, sub.df.test$subscribe)

targeted subNo subYes
   FALSE    79      9
   TRUE     11      6

chisq.test(table(sub.ind.p>=0.5, sub.df.test$subscribe))

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(sub.ind.p >= 0.5, sub.df.test$subscribe)
X-squared = 5.4073, df = 1, p-value = 0.02005

Segmentation is not a method, but a process that must focus clearly on the business need and question. Sometimes a “better” model is less useful.

Clustering can help identify potentially interesting groups in the data. Appropriateness of a solutions depends on both statistical criteria (fit) and business utility (clarity, ability to target, etc.)

If specific groups are known (e.g., segments or behaviors), classification methods find rules to predict membership in those groups. We saw how to predict likelihood-to-subscribe. Depending on cost & margin, one might target more or fewer customers based on likelihood.

Important considerations for classification include performance on holdout data, generalization to new data sets, and avoiding class imbalance problems.

James, Witten, Hastie, & Tibshirani (2013). An Introduction to Statistical Learning, with Applications in R. New York: Springer.

• An excellent overview of a wide variety of statistical approaches to learning and classification.

Kuhn & Johnson (2013). Applied Predictive Modeling. New York: Springer.

• A detailed examination of how to build regression and classification models that generalize. It is focused on practical application and overcoming many typical problems of such projects. There is a superb related package (“caret”).

Wedel & Kamakura (2000). Market Segmentation: Conceptual and Methodological Foundations. New York: Springer.

• Discusses a wide variety of approaches to segmentation for marketing applications (not specific to R).

This presentation is based on Chapter 11 of Chapman and Feit, R for Marketing Research and Analytics © 2015 Springer.

All code in the presentation is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\ Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.