Applying Customer Attitudinal Segmentation to Improve Marketing Campaigns Wenhong Wang, Deluxe Corporation Mark Antiel, Deluxe Corporation

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1 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 1

2 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. 2

3 Customer Acquisition and Promotion Customer Retention and Loyalty Active Self- Promoters Mastering the Complex Sale Word of Mouth Build It and They Will Come Prestige and Image Steady State Retention Mode Filling a Leaky Bucket High consideration, Large ticket products and services Keeping It Fresh High focus on referrals Right Time, Right Place Outstanding product or service that generates own buzz Focus on image and customer experience High focus on customer satisfaction SB constantly looking for next customer high churn SB actively seeking ideas 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. 3

4 Small Business Use Cases (6 cases) NESUG 2012 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? 4

5 Deluxe randomly selected approximately ten thousand small business customers and performed surveys using inbound and outbound calls, internet, , 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 5

6 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 Active Relationship Marketers Brand Builders Local Marketer Saturators Predicted Segment Loyalty Builders Marketing Pros Self- Promoters Status Quo Keepers Traditional Brand Builders Actual Distribution Actual Segment Grand Sensitivity Total Active Relationship Marketers % 4% Brand Builders % 5% Local Market Saturators % 3% Loyalty Builders % 12% Marketing Pros % 26% Self Promoters % 6% Status Quo Keepers % 42% Traditional Brand Builders % 2% Grand Total % 100% Specificity 97% 95% 99% 85% 80% 98% 77% 100% Predicted Distribution 3% 7% 1% 20% 29% 4% 36% 0.20% 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 6

7 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: 7

8 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); 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; data &eigendat(compress=no drop=_type_); set &eigendat(drop=_name_); if _type_ eq 'EIGENVAL'; 8

9 proc transpose data=&eigendat out=&eigendat(drop=_label_ rename=(col1=eigenval _name_=varnm)); data eigen; set &eigendat; if eigenval > 1; %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; data patterndat(drop=_type_); set factorstat; where _type_ eq 'PATTERN'; %macro factor; data _null_; set varlist end=eof; call symput('var' compress(_n_), compress(name)); if eof then call symput('varnr', compress(_n_)); data patterndat; set patterndat; factornr=_n_; %do i=1 %to &varnr; data tmp(rename=(&&var&i=loading)); set patterndat(keep=&&var&i factornr); absval=abs(&&var&i); proc sort data=tmp out=tmp; by descending absval; data tmp(keep=varnm loading factornr); length varnm $32; set tmp; varnm="&&var&i"; if _n_ eq 1; proc append base=loaddat data=tmp; %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; 9

10 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; 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; proc print data=varclusdat; 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; %mend Factor_Varclus; CONTACT INFORMATION Your comments and questions are valued and encouraged and can be ed to wenhong.wang@deluxe.com or mark.antiel@deluxe.com Wenhong Wang Research and Decision Science Deluxe Corporation 500 Main Street Groton, MA wenhong.wang@deluxe.com Mark Antiel Research and Decision Science Deluxe Corporation 3680 Victoria St N Shoreview, MN mark.antiel@deluxe.com 10