Using R for customer segmentation

Using R for Customer Segmentation
useR! 2008
Dortmund, Germany
August, 2008
Jim Porzak,
Senior Director of Analytics
Responsys, Inc.
San Francisco, California

11Aug08 userR! 08 - Porzak, Customer Segmentation 2
Outline
● Two main case study examples
– Customer purchase behavior data
● Goal: actionable segments to improve LTV of customer
base
– Prospect intent & interest survey data
● Goal: actionable segments to better target messaging
content and tactics
● Real data from real clients (sanitized)
● Workshop format
– Hands on
– Discussion heavy

Introduction

Why Segment?
● Better communication with customers and
prospects
– Recipient should feel that we understand him or her
as an individual
– “Send the right message to the right person at the
right time”
● Challenges:
– Widely applicable
● General rules based on readily available data
● A new contact can be placed in their segment easily
– Usable
● Marketing can relate
● Technology can deliver

Segmentation in Practice

Behavioral Segmentation

What's Behavioral Segmentation?
● Based on what people actually do
– Not on what that say they do
● Purchase behavior
– Discuss examples...
● Usage behavior
– Discuss examples...

Why do Behavioral Segmentation?
● All comes down to interacting with your
customer or prospect in the appropriate way
– From customers perspective, not yours!
● Ideally a “one-to-one” interaction
– Not practical in today's world
– Goal: perceived by customer as “one-to-one”

Today's Purchase Behavior Data Set
● Actual web & phone sales records (sanitized)
– 541k order detail lines
– 135k Customers
– Over 2 ½ years
– Of ~900 different products
– In 5 product categories
● Conventional wisdom
– Strong seasonality
– Have a loyal customer base
– But, have retention problem

What we know
Date: 10/10/07 Order #: 12345
Customer: 3894832
Sue Smith
1 Short Street
Qty SKU Description Unit Price Ext Price
1 123 1.50 1.50
3 345 White Widget 2.00 6.00
Total 7.50
Tax 0.60
Shipping 2.00
Grand Total 10.10
Smallville, ND, 39248
Green Gizzmo
Imagine a customer order form:
We get the highlighted data.
Plus: order channel and product (SKU) category

Preloaded as “orders” data frame
> load("BehavioralDataSet.Rda")
> str(orders)
'data.frame': 541101 obs. of 9 variables:
$ SKU_ID : int 459 459 459 459 459 459 459 459 459 459 ...
$ ChannelID: int 3 4 3 3 3 3 4 3 3 3 ...
$ CustID : int 134945 212174 39861 11227 137271 60982 ...
$ OrderID : int 326324 109305 172669 132642 20449 40826 ...
$ OrderDate:Class 'Date' num [1:541101] 13211 13649 13670 ...
$ Quantity : int 1 2 1 3 1 1 1 1 1 1 ...
$ Amount : num 18 36 18 54 18 18 18 18 18 18 ...
$ Channel : Factor w/ 4 levels "phone1","phone2",..: 3 4 3
3 ...
$ Category : Factor w/ 7 levels "*","C","G","I",..: 3 3 3 3
3 ...

orders summary
> summary(orders[-(1:2)])
CustID OrderID OrderDate Quantity
Min. : 2 Min. : 2 Min. :2005-09-01 Min. : 0.000
1st Qu.: 62221 1st Qu.:105292 1st Qu.:2006-07-18 1st Qu.: 1.000
Median :124343 Median :210908 Median :2007-02-14 Median : 1.000
Mean :152974 Mean :207535 Mean :2007-03-11 Mean : 1.113
3rd Qu.:185119 3rd Qu.:315711 3rd Qu.:2007-12-04 3rd Qu.: 1.000
Max. :506929 Max. :388319 Max. :2008-07-14 Max. :275.000
NA's : 4
Amount Channel Category
Min. : 0.01 phone1: 14303 *: 0
1st Qu.: 20.00 phone2: 90 C:142147
Median : 30.00 web1 :451354 G:114300
Mean : 31.81 web2 : 75354 I: 14961
3rd Qu.: 35.00 N: 50385
Max. :4577.00 T:199354
X: 19954

Goal of this exercise?
● Marketers need to come up with a
communication strategy & associated tactics
which will entice customers to exhibit higher
LTV – Long Term Value.
● Segment by past purchase behavior to provide
actionable subsets of customers
– When marketers use our subsets, they get
measurably better results than previous “one size
fits all” method.

How are we going to do this?
(Discussion)

Hints
● Live Stage
● Value
● Engagement
● Favorite Products
● Timing

Recency, Frequency, & Monetary Metrics
● Recency
– How long ago was last purchase? (days)
– Measured for “As Of Date” of data set
● Frequency
– How many orders in analysis period (2 ½ years)
– Attempting to measure engagement
● Monetary
– What is total $ value of all orders in analysis period
Question: Do you expect these three to be uncorrelated?

An Aside: Classical RFM
● Invented by direct marketers in 1950's as a way to
model response rates (before good stat software was
readily available)
● One typical method
– R, F, & M each scored in quantile (typically 5)
– Combined score for each recipient was
concatenation of the three digits, eg “351”
– Scores ranked by empirical response rate
– Mailing then done to top xx% of list
● Today we use, lm, glm, randomForest, ...
● But, concepts still valid as conceptional model
● And, R & F measures typically very important in any
predictive model

I also typically include...
● Breadth
– How many different SKUs purchased?
● Tenure
– How long as customer been with us?

Next Step – Aggregate by Customer
● We need some “raw” RFM values
● Make the data frame “RFM_raw”
– CustomerID: the business key back to database
– FirstPurchaseDate: interesting for tenure metric
– LastPurchaseDate: basis of Recency
– NumberOrders: basis of Frequency
– NumberSKUs: basis of Breadth (engagement metric)
– TotalAmount: basis of Monetary
● Also calculate
– AsOfDate <- max(LastPurchaseDate)

Building the RFM_raw data frame
## for performance, make OrderDate an integer during aggregation
orders_n <- orders
orders_n$OrderDate <- as.integer(orders_n$OrderDate)
## build up one column at a time
RFM_raw <- with(orders_n, data.frame(CustomerID = sort(unique(CustID))))
RFM_raw <- cbind(RFM_raw, FirstPurchaseDate = with(orders_n,
as.Date(as.integer(by(OrderDate, CustID, min)), "1970-01-01")))
RFM_raw <- cbind(RFM_raw, LastPurchaseDate = with(orders_n,
as.Date(as.integer(by(OrderDate, CustID, max)), "1970-01-01")))
RFM_raw <- cbind(RFM_raw, NumberOrders = with(orders_n,
as.numeric(by(OrderID, CustID, function(x) length(unique(x))))))
RFM_raw <- cbind(RFM_raw, NumberSKUs = with(orders_n,
as.numeric(by(SKU_ID, CustID, function(x) length(unique(x))))))
RFM_raw <- cbind(RFM_raw, TotalAmount = with(orders_n,
as.numeric(by(Amount, CustID, sum))))
AsOfDate <- max(RFM_raw$LastPurchaseDate)
save(RFM_raw, AsOfDate, file = "RFM_raw.Rda")
This take a while (1 ½ minutes on my laptop). You may want to download RFM_raw.Rda

Do some RMF EDA
## Jim's miscellaneous DMA functions
source("dma_misc.R")
## for interactive games:
attach(RFM_raw)
## EDA plots using base graphics
rfm.plot(as.numeric(AsOfDate - LastPurchaseDate) %/% 7, "rec")
rfm.plot(NumberOrders, "freq")
rfm.plot(TotalAmount, "mon")
rfm.plot(NumberSKUs, "breadth")
## EDA plots using iPlots
ihist(as.numeric(AsOfDate - LastPurchaseDate) %/% 7, title = "Recency")
ihist(NumberOrders, title = "Frequency")
ihist(TotalAmount, title = "Monetary")
ihist(NumberSKUs, title = "Breadth")

RFM EDA Plots
In all cases, “best is left.”

Assign reasonable RFM breaks
● Recency:
– Breaks (weeks <=): 25, 51, 77, 103, <else>
– levels = c("0-5", "6-11", "12-17", "18-23", "24-29"))
● Note levels labeled in months, not weeks
● Frequency:
– Breaks (count <=): 1, 3, 7, <else>
– levels = c("8+", "7-4", "3-2", "1"))
● Note ordering for best is left.
● Monetary:
– Breaks (value <=): 50, 100, 200, 400, <else>
– levels = c("401+", "400-201", "200-101", "100-51", "50-0"))
● Again ordering is best is left.

Build RFM_segs data frame
RFM_segs <- data.frame(Recency_weeks = as.numeric(AsOfDate - RFM_raw$LastPurchaseDate) %/% 7)
row.names(RFM_segs) <- row.names(RFM_raw)
## now label levels with months rather than weeks
RFM_segs$Recency <- ordered(ifelse(RFM_segs$Recency_weeks <= 25, "0-5",
ifelse(RFM_segs$Recency_weeks <= 51, "6-11",
ifelse(RFM_segs$Recency_weeks <= 77, "12-17",
ifelse(RFM_segs$Recency_weeks <= 103, "18-23", "24-29")))),
levels = c("0-5", "6-11", "12-17", "18-23", "24-29"))
RFM_segs$Frequency_count <- RFM_raw$NumberOrders
RFM_segs$Frequency <- ordered(ifelse(RFM_segs$Frequency_count == 1, "1",
ifelse(RFM_segs$Frequency_count <= 3, "3-2",
ifelse(RFM_segs$Frequency_count <= 7, "7-4", "8+"))),
levels = c("8+", "7-4", "3-2", "1"))
RFM_segs$Monetary_value <- RFM_raw$TotalAmount
RFM_segs$Monetary <- ordered(ifelse(RFM_segs$Monetary_value <= 50, "50-0",
ifelse(RFM_segs$Monetary_value <= 100, "100-51",
ifelse(RFM_segs$Monetary_value <= 200, "200-101",
ifelse(RFM_segs$Monetary_value <= 400, "400-201", "401+")))),
levels = c("401+", "400-201", "200-101", "100-51", "50-0"))

We typically also add Breadth & Tenure:
RFM_segs$Breadth_count <- RFM_raw$NumberSKUs
RFM_segs$Breadth <- ordered(ifelse(RFM_segs$Breadth_count == 1, "1",
ifelse(RFM_segs$Breadth_count == 2, "2",
ifelse(RFM_segs$Breadth_count <= 4, "4-3",
ifelse(RFM_segs$Breadth_count <= 9, "9-5", "10+")))),
levels = c("10+", "9-5", "4-3", "2", "1"))
RFM_segs$Tenure_weeks <- as.numeric(AsOfDate - FirstPurchaseDate) %/% 7
RFM_segs$Tenure <- ordered(ifelse(RFM_segs$Tenure_weeks <= 12, "0-12",
ifelse(RFM_segs$Tenure_weeks <= 25, "13-25",
"104+")))))))),
levels = c("104+", "91-103", "78-90", "65-77", "52-64", "39-51",
"26-38", "13-25", "0-12"))
save(RFM_segs, file = "RFM_segs.Rda")

How do customers look in RFM space?
● I like mosaic plots (& especially vcd* package!)
● Set up a “structure table” with assignments:
● And a convenience function for mosaic:
require(vcd)
RFM_st <- structable(~ Recency + Frequency + Monetary + Breadth,
data = RFM_segs)
mm <- function(f) {
mosaic(f, data = RFM_st,
shade = TRUE,
labeling_args = list(rot_labels = c(left = 90, top = 45),
just_labels = c(left = "left",
top = "center")),
spacing = spacing_dimequal(unit(c(0.5, 0.8), "lines")),
keep_aspect_ratio = FALSE
)
}
* To learn more, attend: The strucplot framework for Visualizing Categorical Data. Wed, 11:30. E29

mm(~ Recency + Frequency)

mm(~ Frequency + Monetary)

mm(~ Recency + Monetary)

mm(~ Breadth + Monetary)

To really show off vcd!
pairs(RFM_st, lower_panel = pairs_assoc, shade = TRUE)

Time to get real – remember goal?

Actionable for Marketers
The big two concepts:
1. Lifestage
2. Value
Turns out we can do both with Recency &
Frequency!

Use Balloon Plots to Communicate
require(gplots)
# Recency by Frequence - Counts
RxF <- as.data.frame(table(RFM_segs$Recency, RFM_segs$Frequency,
dnn = c("Recency", "Frequency")),
responseName = "Number_Customers")
with(RxF, balloonplot(Recency, Frequency, Number_Customers, zlab = "#
Customers"))
# Recency by Frequency - Annual Value (total annual sales to segment)
VbyRxF <- (aggregate(RFM_segs$Monetary_value,
by = list(Recency = factor(RFM_segs$Recency),
Frequency = RFM_segs$Frequency),
sum))
names(VbyRxF)[3] <- "Annual_Sales"
VbyRxF$Annual_Sales <- VbyRxF$Annual_Sales / (28/12) ## normalize to
annual revnue
with(VbyRxF, balloonplot(Recency, Frequency, Annual_Sales / 1000, zlab =
"Annual Sales (000)"))

Recency by Frequency - Counts

Recency by Frequency - Value

Exercise – Assign Segments
● Lifestage “dimension”
– New
– Active
– Lapsed
– Lost
● Value “dimension”
– Gold
– Silver
– Bronze
● Combined as
– High Value, Repeat, New, One-time, Lapsed, & Lost

Color & Label Segment Cells
# a matrix of segment codes
RF_segs0 <- matrix("", nrow = 4, ncol = 5)
# manually make assignments
object.browser() ## Fill in H, R, N, L, or O. Save as RF_segs.txt
# get back into R
RF_segs <- as.matrix(read.delim("RF_segs.txt", sep = "t",
na.strings = ""))
RF_segs[is.na(RF_segs)] <- "X" ## N/A's become “Lost”
# add colors and labels to balloon plot
# Magic values for balloon cell centers
RF_x <- matrix(2:6 + 0.25, nrow = 4, ncol = 5, byrow = TRUE)
RF_y <- matrix(4:1, nrow = 4, ncol = 5, byrow = FALSE)
RF_cols <- sapply(RF_segs, function(x) switch(x, H="gold",
R="slategray2", N="green",
L="yellow", O="darkgreen", "red"))
points(RF_x, RF_y, col = RF_cols, pch = 16, cex = 12)
text(RF_x, RF_y, RF_segs, cex = 2)

Final Segments for Marketers

Conceptual RF Segments

Break Time!

Attitudinal Segmentation

Marketing Challenge
● Our client offers free download of software with
high perceived value, but
● First asks user to fill out a simple survey
● Challenge is to come up with a “few” segments
that will be used by segment to:
– Prioritize contact strategy
– Craft marketing messages based on profile

Sample Data
● Surveys from 20k respondents
● All within same time frame (a number of weeks)
● All requested the software download

Survey Description
● 35 check boxes or radio buttons
– None required. Coded as binary responses
● Arranged in 5 sections
– License: W and/or X
– Role: one of D, SA, ITM, ITA, Str, Oth (radio
buttons)
– System: any of S, T, A, B, C, D, O (check boxes)
– Interest: any of M, O Pl, Pr, Sup, 64, Con, Per, DT,
Z, Oth. (check boxes)
– Application: any of Web, Inf, Col, Db, J2, Top, Dev,
Per, Other (check boxes)

Data Set
Provided as data frame csb, in
InterestPreferenceSurvey.Rda
# Getting started
setwd("C:/Data/useR08/R")
require(lattice)
require(grDevices)
require(vcd)
require(flexclust)
load(file = "InterestPreferenceSurvey.Rda")
str(csb)
'data.frame': 20000 obs. of 35 variables:
$ Lic_W : int 0 0 0 0 0 0 0 0 0 0 ...
$ Lic_X : int 1 1 1 0 1 1 1 1 1 1 ...
$ Role_D : int 0 0 0 0 0 0 0 0 1 0 ...
$ Role_SA : int 0 0 1 0 1 0 0 1 0 0 ...
$ Role_ITM: int 0 0 0 1 0 0 0 0 0 0 ...
$ Role_ITA: int 0 0 0 0 0 0 0 0 0 0 ...

Proportion Responders by Question
> mean(csb)
Lic_W Lic_X Role_D Role_SA Role_ITM Role_ITA Role_Stu Role_Oth
0.16040 0.90980 0.19905 0.32910 0.06905 0.08465 0.21080 0.05090
Sys_S Sys_T Sys_A Sys_B Sys_C Sys_D Sys_O Int_M
0.17780 0.39720 0.17020 0.13975 0.09325 0.03510 0.19260 0.36960
Int_O Int_Pl Int_Pr Int_Sup Int_64 Int_Con Int_Per Int_DT
0.46810 0.09395 0.10055 0.08985 0.23445 0.21235 0.31420 0.11790
Int_Z Int_Oth Ap_Web Ap_Inf Ap_Col Ap_Db Ap_J2 Ap_Top
0.23450 0.05995 0.39640 0.19125 0.18365 0.30125 0.19455 0.30145
Ap_Dev Ap_Per Ap_Other
0.18960 0.20050 0.03735

Clustering Strategy
● flexclust package by Fritz Leisch
● See his 2006 paper (on his personal page):
A Toolbox for K-Centroids Cluster Analysis
● This is (mostly) an optional response type
survey
– 1 = “yes” is significant
– 0 is just absence not really a “no”
– Respondents checking Role_SA have much more
in common than those not checking Role_SA
● Following Fritz's argument we use the
expectation based Jaccard distance measure.

A First Cluster Run
require(flexclust)
## set up flexclust control object
fc_cont <- new("flexclustControl")
fc_cont@tolerance <- 0.1 ## this doesn't seem to work as expected
fc_cont@iter.max <- 30 ## seems to be effective convergence
##fc_cont@verbose <- 1 ## set TRUE if to see each step
my_seed <- 0
my_family <- "ejaccard"
num_clust <- 4
my_seed <- my_seed + 1
set.seed(my_seed)
cl <- kcca(csb, k = num_clust, save.data = TRUE, control = fc_cont,
family = kccaFamily(my_family))
## This takes ~ 1.5 min. on my laptop

Cluster Summary
> summary(cl)
kcca object of family 'ejaccard'
call:
kcca(x = csb, k = num_clust, family = kccaFamily(my_family),
control = fc_cont, save.data = TRUE)
cluster info:
size av_dist max_dist separation
1 5551 0.7159832 1 0.6766653
2 4577 0.7707523 1 0.7437616
3 2535 0.7482347 1 0.7038259
4 7337 0.7215583 1 0.6732479
no convergence after 200 iterations
sum of within cluster distances: 14693.00

Run Plots
pop_av_dist <- with(cl@clusinfo, sum(size*av_dist)/sum(size))
main_txt <- paste("kcca ", c1@family@name, " - ",
num_clust, " clusters (",
nsamp, "k sample, seed = ", my_seed,
")", sep = "")
# Neighborhood Graph on 1st principle components
csb.pca <- prcomp(csb)
plot(cl, data = as.matrix(csb), project = csb.pca,
main = main_txt,
sub = paste("nAv Dist = ", format(pop_av_dist, digits = 5),
", k = ", c1@k, sep = "")
)
# Activity Profiles for each segment
print(barchart(cl, main = main_txt, strip.prefix = "#",
scales = list(cex = 0.6)))

Plots (k=4, seed = 1)

Are any of these any good?
● If so, which?
● How to decide?
● Quoting Fritz (pg 15):
The actual choice of expectation-based Jaccard
with K = 6 clusters ... has been made manually by
comparing various solutions and selecting the one
which made most sense from the practitioners
point of view. This may seem unsatisfying because
the decision is subjective, but cluster analysis here
is used as a tool for exploratory data analysis and
offers simplified views of a complex data set.

Our Selection Criteria
1. Choice of k, must have mostly ~ stable
solutions, and
2. Cluster profiles must be interpretable. IOW,
what is the story you can tell about each
cluster? Will the marketers relate to it?

Your Challenge...
Do what Fritz said:
The actual choice ... has been made manually by
comparing various solutions and selecting the one
which made most sense.
Here are 4 runs for each k = 3 to 8; 24 in all.
Pick the “best” one, make up stories for each cluster,
and explain your choice to group.

For the Record. Jim's Pick:

Jim's Stories
Based on knowing a bit more about the client
than I can share with you.
#1: An “S” loyalist, high % SA's
#2: Favors name brands, high responders
#3: A “T” loyalist, broad but reduced responses
#4: Favors name brands, but otherwise low resp.
#5: Student, gray box, open source, desktop.

Finally, using predict in flexclust
Once we (analysts & marketers) have decided on
a clustering model, we want to use it to assign
new respondents to likely segment.
flexclust includes predict:
persona <- predict(cl, csb)
head(persona)
str(persona)
PersonaPredict <- as.data.frame(persona)
names(PersonaPredict) <- "cluster"
> table(PersonaPredict)
PersonaPredict
1 2 3 4 5
2313 6479 4654 2702 3852

Closing the Loop –
Tying Back to Purchase Model
Where ppBand is probability of purchase band ( 0 = 0.0 – 0.999,
1 = 0.10 – 0.199, … 9 = 0.90 – 0.999). IOW, 0 is really low & 9 is
really high probability of purchase according to the model

Conclusion

Follow up
● Slides and code will be up next week on
http://www.porzak.com/JimArchive/useR2008/
● Ping me with questions or comments:
jporzak@gmail.com
● Check out the San Francisco useR Group:
ia.meetup.com/67/
Thanks!

Appendix

section

Code slide
##

Using R for customer segmentation

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