Download Applying Customer Attitudinal Segmentation to Improve Marketing Campaigns

NESUG 2012
Statistics, Modeling and Analysis
Applying Customer Attitudinal Segmentation to Improve
Marketing Campaigns
Wenhong Wang, Deluxe Corporation
Mark Antiel, Deluxe Corporation
ABSTRACT
Customer segmentation is fundamental for successful marketing ad campaigns. Current
segmentation strategies are primarily based on customers purchase history and their
demographics. In this paper, we will present customer attitudinal segmentation as a new method
on driving incremental sales in B2B marketing campaigns. We will also illustrate how SAS®
software may be used to analyze customer survey data, develop classification models and
classify customers into multi-level attitudinal segments.
INTRODUCTION
Successful advertisement campaigns require marketers to understand customer needs and how
to send meaningful information. It is essential for marketers to know who their customers are and
what their customers want and need so that they can provide the right product or service with the
most relevant offer. With countless numbers of customers to market, segmentation provides a
solution to assign customers into groups that minimize variability within and maximize distinctions
between groups. Thus, for each customer segment, marketers can use appropriate channels and
creative ads to deliver more impactful messages.
Today, the common segmentation strategies in business-to-business (B2B) marketing include:

Customer Value Segmentation. The method primarily uses the transactional data of
business customers purchase history. For example, segments are based on customers
past year’s actual spending amount and ranked into high, medium and low value
segments. More sophisticated value segmentation can be developed on customers’
predictive future value derived from predictive models. Customer value segmentation can
help marketing campaigns prioritize the focus ensuring advertisement spend is on the top
performing customers.

Customer Firmographic Segmentation. This method places business customers into
different groups according to their firmographic characteristics such as line of business,
employee size, years in the business, and annual sales volumes. The business
customers with similar firmographic characteristics are clustered into the same segment
and will be marketed as a single group. Customer firmographic models can be combined
with value segmentation to fine tune marketing strategies for existing customers.

Customer Attitudinal Segmentation. This method focuses on aligning businesses
according to how they perceive themselves and how they run their business. To
understand a business’ attitudes, surveys and interviews are required to collect
customers’ psychographic and attitudinal information. Based on customers’ responses,
businesses are classified into different market segments. Attitudinal segmentation is a
more customer-centric tool that enables marketers to communicate with business
customers using personalized messaging and targeted offerings.
This paper focuses on the attitudinal segmentation strategy.
BACKGROUND
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Statistics, Modeling and Analysis
Deluxe Corporation is the indispensable partner for unleashing the growth potential of small
businesses and financial institutions. Today’s Deluxe has 4 million active small business
customers. Historically, Deluxe marketing campaigns relied on customer value segmentation.
Based on predicted future value, the customers were categorized into high, medium, and low
value tiers. Customers per tier level received various offers and general creative ads. Naturally,
high-value customers became a hot spot on which most of marketing efforts had been
concentrated with low-value tier customers receiving much less attentions. To meet customer
needs it became apparent that solely applying the value segmentation strategy was not enough.
The final goal of Deluxe marketing campaigns is to generate incremental sales to maximize ROI.
Marketers need to know more than customer value to achieve the goal.
Deluxe set to develop classification models to assign customers into different attitudinal segments
in order to drive incremental revenue. First, based on extensive market research and series of
customer surveys and interviews, the survey and interview responders were classified into eight
distinct segments. Next, using SAS® software and StatSoft® data mining tool, Deluxe developed
classification tree models and assigned the 4 million small business customers into the above
eight different attitudinal segments. This strategy enables more salient, targeted offerings and
marketing programs.
METHODOLOGY
The attitudinal segmentation includes 5 stages:
1. Market Research
2. Customer Attitude Survey
3. Data Preparation
4. Classification Tree Model Development
5. Model Implementation
1. Market Research
In this stage, working with an outside market research company, Deluxe performed extensive
research on the US market of small businesses (SBs) as well as Deluxe’s position in the
marketplace. About 7.2 million US small businesses (SBs) with employee size from 1 to 19 had
been studied carefully. Use case classification method was used to identify the primary customer
business needs. The method split these SBs into two primary use cases: 1) customer acquisition
and promotion; 2) customer retention and loyalty. The two use cases were mutually exclusive,
with SBs having a primary orientation toward acquisition and retention. In addition, three types of
available data were analyzed: a) Attitudinal data such as SBs’ orientation toward growth and
technology adoption; b) Behavioral data such as purchase patterns; c) Firmographic data such as
vertical market and SBs lifecycle stage. Hence, the two primary use cases were further split into
nine secondary use cases, which formed the preliminary attitudinal segments that would be
evaluated in the next stage. Figure 1 illustrates the market research result.
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Statistics, Modeling and Analysis
Customer Acquisition and
Promotion
Active
SelfPromoters
Mastering
the
Complex
Sale
Word of
Mouth
High consideration,
Large ticket products
and services
Filling a
Leaky
Bucket
SB constantly
looking for next
customer – high
churn
Keeping It
Fresh
SB actively
seeking ideas
High focus on
referrals
Customer Retention and
Loyalty
Build It
and They
Will Come
Outstanding
product or
service that
generates own
“buzz”
Prestige
and Image
Focus on
image and
customer
experience
Steady
State
“Retention
Mode”
High focus
on customer
satisfaction
Right
Time,
Right
Place
Infrequent but
urgent purchase
decision
Figure 1. Primary and Secondary Use Cases
2. Customer Attitude Survey
To evaluate the hypothesized segment structure developed from the previous stage, an online
survey was conducted on a sample representing US SBs including Deluxe SBs customers. The
questionnaire covered the below 3 sections:
 Section 1: Assess dimensions to define business use cases. The questions in this section
would help pinpoint whether a business was acquisition focused or retention focused.
 Section 2: Assess attitudinal dimensions that define segments. The attitudinal dimensions
include:
o Growth orientation – attitudes about growing business versus maintaining steady
status.
o Marketing sophistication – attitudes about the role and importance of marketing.
o Technology usage – attitudes about the role of technology in managing and
promoting the business.
 Section 3: Assess behavioral factors. For example, the questions about purchasing
propensity would be used to profile segments.
The survey results validated and refined the preliminary nine segments. The survey responders
were grouped into four clear and distinct attitudinal clusters. The previous nine use cases were
collapsed to six based on direct feedback from small businesses. Combining the six new use
cases and four attitudinal groupings, a more precise structure with eight segments was
generated.
The eight segments are: Status Quo Keepers, Marketing Pros, Loyalty Builders, Brand Builders,
Self- Promoters, Active Relationship Marketers, Local Market Saturators, and Traditional Brand
Builders. Each of the 8 segments has its own characteristics in terms of customer profile,
business needs, preferred products and services, buying patterns, and potential opportunities
with Deluxe.
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Small Business Use Cases (6 cases)
Attitudinal Groupings (4 clusters)
1) Status Quo Keepers
2) Online Marketing Pros
3) Offline Loyalty Builders
4) Digital Brand Builders
5) Online Self-Promoters
6) Digitally Active Relationship Marketers
7) Local Market Saturators
8) Offline Brand Builders
Figure 2. Eight Segments Derived from Customer Attitude Survey
Each of the 8 attitudinal segments has its own characteristics. Figure 3 illustrates typical
examples of segment profile.
Figure 3. Attitudinal Segmentation Personas
Based on the US SBs survey learning, Deluxe designed a similar but shorter survey
questionnaire for Deluxe active customers. The survey questions were designed in a way to
enable our market researchers to assign the responders to one of the eight known segments
described earlier. Below is an example of the short summary of the important questions in the
survey.





Is growing and expanding business a top priority for small business customers?
Are small business customers confident in using internet to promote their businesses?
How do small business customers view their capabilities in marketing?
Do small business customers feel confident in their marketing effectiveness?
How do small business customers drive their businesses? Through promotions or
reputation and mouth of word?
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Statistics, Modeling and Analysis
Deluxe randomly selected approximately ten thousand small business customers and performed
surveys using inbound and outbound calls, internet, email, and in-person interviews. With a 45%
response rate, four thousand and five hundred customers responded. According to their answers
to the survey questions, each of the 4,509 responders was assigned to one of the eight segments
described previously. Thus, after this step, we constructed a modeling sample of 4,509
customers. The dependent variable SEGMENT has 8 categorical values: Status Quo Keepers,
Marketing Pros, Loyalty Builders, Brand Builders, Self- Promoters, Active Relationship Marketers,
Local Market Saturators, and Traditional Brand Builders.
3. Data Preparation
Businesses were matched to corporate databases to obtain historical transactional and
demographic data and appended for modeling development. More than a thousand of
transactional behavior variables and hundreds of demographic variables were created and
compromised the potential pool of independent variables.
To eliminate the variable redundancy and irrelevance, variable reduction techniques were applied
to generate a more manageable and powerful list of predictors. PROC FACTOR and PROC
VARCLUS were combined to reduce the number of variables. PROC FACTOR was used to
perform principal component analysis. It extracts a set of orthogonal factors that accounts for the
maximum portion of the variance present in the original set of variables. Kaiser-Guttman rule, or
the “eigenvalue greater than one” rule, was used to decide the number of factors to be extracted.
Once the number of factors was determined, PROC FACTOR was performed again by specifying
the option NFACTORS= to the exact number of factors. Next, PROC VARCLUS was used to
generate the same number of clusters as the number of factors in PROC FACTOR. The
variables in the same cluster should be highly correlated with themselves but highly uncorrelated
with the variables in a different cluster. Last, combining the results of PROC FACTOR and
PROC VARCLUS, we selected a much smaller set of variables. The guideline was to choose
several variables with high factor loadings from each factor and ensure the selected variables
belonging to different clusters.
There are many alternative approaches of dealing with missing values. We used the most
common and simple method “class mean substitution”. Each of our small business customers
has an industry code and belongs to a certain line of business (LOB). In total, our customers have
about 150 different LOBs. For each of variables, class mean substitution first calculates class
mean value at LOB level and then uses it to replace the missing value. This method can help to
maintain the variance of the data.
Additional data preparation steps include re-binning categorical variables, transformation and
derivation new variables from the existing ones, and treatment of outliers.
4. Classification Tree Model Development
In this paper, we prepared a modeling dataset of 4,509 customers with 126 independent
variables. The target variable SEGMENT was a nominal variable with eight categories. Using
StatSoft® STATISTICA, general classification & regression tree (C&RT) models were built. We
finally chose one of the C&RT models per the following considerations:
1. The model has the least number of important variables (29 out of 126) to split tree nodes.
This can help make implementation easier.
2. The minimum number of cases in each node is higher than the other models, which
would limit the issue of over-fitting.
3. Cross-validation result is very close to the other models that contain more variables.
4. The model shows higher sensitivity and specificity than the other models. Sensitivity and
specificity have been used to measure predictive accuracy of the classification.
Sensitivity is defined as the probability that the model correctly classifies a customer to a
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Statistics, Modeling and Analysis
segment. Specificity is defined as the probability that the model predicts that a customer
does not belong to a segment correctly. We would like the model to have high sensitivity
and specificity. However, there are tradeoffs between the two measurements. In the
C&RT model, as shown below, the sensitivity for the segment Marketing Pros was 54%,
which was the second highest among the eight segments; however its specificity was
only 77%.
Table 1. General Classification & Regression Tree Model – Classification Metrics
Predicted Segment
Actual Segment
Active Relationship Marketers
Brand Builders
Local Market Saturators
Loyalty Builders
Marketing Pros
Self Promoters
Status Quo Keepers
Traditional Brand Builders
Active
Relationship
Marketers
42
7
3
9
22
12
50
6
Brand
Builders
11
100
7
16
48
20
120
8
Local
Marketer
Saturators
2
1
11
1
4
1
15
Loyalty
Builders
Marketing
Pros
SelfPromoters
Status Quo
Keepers
21
34
29
300
160
40
275
21
42
47
47
99
657
56
361
20
3
4
11
9
25
54
50
3
53
47
29
102
262
69
1028
26
Traditional
Brand
Builders
2
1
6
Grand Total
Specificity
151
97%
330
95%
35
99%
880
85%
1329
80%
159
98%
1616
77%
9
100%
Predicted Distribution
3%
7%
1%
20%
29%
4%
36%
0.20%
Grand
Total
174
240
137
536
1180
253
1899
90
Sensitivity
Actual
Distribution
24%
42%
8%
56%
56%
21%
54%
7%
4%
5%
3%
12%
26%
6%
42%
2%
4509
49%
100%
5. Model Implementation
In this step, we applied the C&RT model and scored Deluxe’s current customers.
Figure 4. Attitudinal Segment Assignment – Deluxe Active Small Business Customers
With the developed models, Deluxe assigned all active small businesses to one of the eight
attitudinal segments.
CONCLUSION
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Statistics, Modeling and Analysis
Attitudinal segmentation helps Deluxe to focus marketing solutions to optimize messages and
timing to small business customers. In addition to campaign message optimization, attitudinal
segmentation can also be used in product marketing to guide offerings and product sequencing.
Marketing tests have shown that attitudinal segmentation provided valuable customer insights
and generated incremental revenue to Deluxe.
APPENDIX: DATA REDUCTION TECHNIQUES – FACTOR_VARCLUS MACRO
The %FACTOR_VARCLUS macro performs below tasks:
1) Create a variable list to be analyzed, including all numeric variables in the analyzed
dataset. The example shown in the appendix has 19 variables in total to be analyzed.
2) Calculate eigen values for all of the analyzed variables by running PROC FACTOR.
Here, we use NFACTOR=0 to compute eigen values but no factors are extracted.
3) Decide number of factors by specifying EIGENVAL > 1. The macro variable
FACTORNR is created, which will be used in the following PROC FACTOR and
PROC VARCLUS to define the number of factors and the number of variable
clusters.
4) PROC FACTOR with the option NFACTOR=&FACTORNR. Assign each variable to
the factor with the greatest absolute value of factor loadings, as shown in below
table.
5) PROC VARCLUS with the option MINC=&FACTORNR MAXC=&FACTORNR to
create the exact same number of clusters as the number of factors in the previous
PROC FACTOR procedure. It generates a table as below:
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Statistics, Modeling and Analysis
6) Put together the outputs from PROC FACTOR and PROC VARCLUS. The final table
is shown as below. We now can choose variables using the table. We may simply
select one variable from each factor, and choose the one with larger factor loading
value and smaller cluster R-square-ratio. In this example, the original 19 variables
eventually are reduced to 5 variables selected from the 5 factors or the 5 clusters.
%macro Factor_Varclus(dataset, eigendat, factor_varclus);
/*******************************************************************/
/* macro factor_varclus parameters
*/
/* dataset = dataset to be analyzed
*/
/* eigendat = output dataset including eigen values for all */
/*
analyzed variables
*/
/* factor_varclus = final output dataset including
*/
/* factor number, factor loading, cluster number,
*/
/* own cluster R-square value, next closest value, and
*/
/* 1-R**2 Ratio
*/
/********************************************************************/
proc contents data=&dataset noprint
out=varlist(where=(type=1) keep=name type);
run;
proc sql;
create table varlist as
select name
from varlist;
quit;
proc sql noprint;
select name into :varlist separated by ' '
from varlist;
quit;
%put &varlist;
/***** PROC FACTOR *********************************************/
proc factor data=&dataset nfactors=0
outstat=&eigendat;
var &varlist;
run;
data &eigendat(compress=no drop=_type_);
set &eigendat(drop=_name_);
if _type_ eq 'EIGENVAL';
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Statistics, Modeling and Analysis
run;
proc transpose data=&eigendat
out=&eigendat(drop=_label_ rename=(col1=eigenval _name_=varnm));
run;
data eigen;
set &eigendat;
if eigenval > 1;
run;
%let dsid=%sysfunc(open(eigen));
%let factornr=%sysfunc(attrn(&dsid, nobs));
%let re=%sysfunc(close(&dsid));
proc factor data=&dataset method=prin scree nfactors=&factornr
rotate=varimax reorder
outstat=factorstat;
var &varlist;
run;
data patterndat(drop=_type_);
set factorstat;
where _type_ eq 'PATTERN';
run;
%macro factor;
data _null_;
set varlist end=eof;
call symput('var'||compress(_n_), compress(name));
if eof then call symput('varnr', compress(_n_));
run;
data patterndat;
set patterndat;
factornr=_n_;
run;
%do i=1 %to &varnr;
data tmp(rename=(&&var&i=loading));
set patterndat(keep=&&var&i factornr);
absval=abs(&&var&i);
run;
proc sort data=tmp out=tmp;
by descending absval;
run;
data tmp(keep=varnm loading factornr);
length varnm $32;
set tmp;
varnm="&&var&i";
if _n_ eq 1;
run;
proc append base=loaddat data=tmp;
run;
%end;
%mend factor;
%factor
proc sql;
create table factordat as
select a.varnm, a.factornr, a.loading
from loaddat a
order by 2, 3 desc;
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Statistics, Modeling and Analysis
quit;
/***** PROC VARCLUS *********************************************/
ods output RSquare=outdat_clus;
proc varclus data=&dataset minc=&factornr maxc=&factornr short;
var &varlist;
Run;
quit;
ods output close;
Proc print data=outdat_clus; run;
Data varclusdat;
set outdat_clus nobs=count;
cluster2=cluster;
retain lastcluster;
if _n_=1 then lastcluster=cluster;
if cluster='' then cluster=lastcluster;
else lastcluster=cluster;
length var_cluster 4.;
var_cluster=input(compress(substr(cluster,9,2)),8.);
Rename cluster=varclus_name;
drop controlvar lastcluster cluster2;
Run;
Proc sort data=varclusdat ; by var_cluster owncluster; run;
proc print data=varclusdat; run;
proc sql;
create table &factor_varclus as
select a.* , b.var_cluster, b.owncluster, b.nextclosest, b.rsquareratio
from factordat a , varclusdat b
where a.varnm=b.variable
order by a.factornr, a.loading;
quit;
proc print data=&factor_varclus; run;
%mend Factor_Varclus;
CONTACT INFORMATION
Your comments and questions are valued and encouraged and can be emailed to
[email protected] or [email protected]
Wenhong Wang
Research and Decision Science
Deluxe Corporation
500 Main Street
Groton, MA 01471
E-mail: [email protected]
Mark Antiel
Research and Decision Science
Deluxe Corporation
3680 Victoria St N
Shoreview, MN 55126
E-mail: [email protected]
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