Data Mining Cookbook CD-Rom: Modeling Data for Marketing, Risk and Customer Relationship Management


Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining ...

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Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

"This CD-ROM provides actual, tested models of the most commonly asked data mining questions for marketing, sales, risk analysis, customer retention and support, and other key business applications"--Container.

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Product Details

  • ISBN-13: 9780471388296
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 12/15/2000
  • Edition description: CD-ROM
  • Edition number: 1
  • Pages: 1
  • Product dimensions: 7.60 (w) x 9.25 (h) x 0.22 (d)

Meet the Author

OLIVIA parr rud ( is Executive Vice President of Data Square, LLC, a leading database marketing consulting firm. She has over 22 years' experience in data mining, predictive modeling, and segmentation for a variety of industries, including credit card, insurance, high-tech, telecommunications, and catalog industries. She provides analysis and solutions for her clients in the areas of acquisition, retention, risk, and overall profitability for direct mail, telemarketing, broadcast marketing, and the Internet.
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First Chapter

Note: The Figures and/or Tables mentioned in this sample chapter do not appear on the Web.

Understanding Your Customer: Profiling and Segmentation

The methodologies discussed in this chapter could have easily been included at the beginning of this book, but because they don't really fit into the realm of predictive modeling, I decided to write a separate chapter. The cases in this chapter describe several techniques and applications for understanding your customer. Common sense tells us that it's a good first step to successful customer relationship management. It is also an important step for effective prospecting. In other words, once you know what customer attributes and behaviors are currently driving your profitability, you can use these to direct your prospecting efforts as well. (In fact, when I decided on a title for this chapter, I was hesitant to limit it to just "customer.") The first step in effective prospecting is learning how to find prospects that look like your customers. It is also useful to segment and profile your prospect base to assist acquisition efforts. The goal in both cases is to identify what drives customer profitability.

This chapter begins by defining profiling and segmentation and discussing some of the types and uses of these techniques. Some typical applications are discussed with references to the data types mentioned in chapter 2. The second half of the chapter details the process using three case studies. The first is from the catalog industry, in which I perform some simple profile and penetration analyses. Next, I develop a customer value matrix for a credit card customer database. The final case study illustrates the use of cluster analysis to discover segments.

What Is the Importance of Understanding Your Customer?

This sounds like a dumb question, doesn't it? You would be amazed, though, at how many companies operate for years--pumping out offers for products and services--without a clue of what their best customer looks like. For every company in every industry, this is the most important first step to profitable marketing.

Similar to modeling, before you begin any profiling or segmentation project, it is important to establish your objective. This is crucial because it will affect the way you approach the task. The objective can be explained by reviewing the definitions of profiling and segmentation.

Profiling is exactly what it implies: the act of using data to describe or profile a group of customers or prospects. It can be performed on an entire database or distinct sections of the database. The distinct sections are known as segments. Typically they are mutually exclusive, which means no one can be a member of more than one segment.

Segmentation is the act of splitting a database into distinct sections or segments. There are two basic approaches to segmentation: market driven and data driven. Market-driven approaches allow you to use characteristics that you determine to be important drivers of your business. In other words, you preselect the characteristics that define the segments. This is why defining your objective is so critical. The ultimate plans for using the segments will determine the best method for creating them. On the other hand, data-driven approaches use techniques such as cluster analysis or factor analysis to find homogenous groups. This might be useful if you are working with data about which you have little knowledge.

Types of Profiling and Segmentation

If you've never done any segmentation or modeling, your customer base may seem like a big blob that behaves a certain way, depending on the latest stimulus. If you do a little digging, you will find a variety of demographic and psychographic characteristics as well as a multitude of buying behaviors, risk patterns, and levels of profitability among the members of your database. This is the beauty of segmentation and profiling. Once you understand the distinct groups within the database, you can use this knowledge for product development, customer service customization, media and channel selection, and targeting selection.

RFM: Recency, Frequency, Monetary Value

One of the most common types of profiling originated in the catalog industry. Commonly called RFM, it is a method of segmenting customers on their buying behavior. Its use is primarily for improving the efficiency of marketing efforts to existing customers. It is a very powerful tool that involves little more than creating segments from the three groups.

Recency. This value is the number of months since the last purchase. It is typically the most powerful of the three characteristics for predicting response to a subsequent offer. This seems quite logical. It says that if you've recently purchased something from a company, you are more likely to make another purchase than someone who did not recently make a purchase.

Frequency. This value is the number of purchases. It can be the total of purchases within a specific time frame or include all purchases. This characteristic is second to recency in predictive power for response. Again, it is quite intuitive as to why it relates to future purchases.

Monetary value. This value is the total dollar amount. Similar to frequency, it can be within a specific time frame or include all purchases. Of the three, this characteristic is the least powerful when it comes to predicting response. But when used in combination, it can add another dimension of understanding.

These three characteristics can be used alone or in combination with other characteristics to assist in CRM efforts. Arthur M. Hughes, in his book Strategic Database Marketing (Probus, 1994), describes a number of excellent applications for RFM analysis. In the second half of the chapter, I will work through a case study in which I calculate RFM for a catalog company.


Have you ever seen the ad that shows a 60's flower child living in a conservative neighborhood? The emphasis is on finding the individual who may not fit the local demographic profile. In reality, though, many people who live in the same area behave in a similar fashion.

As I mentioned in chapter 2, there are many sources of demographic data. Many sources are collected at the individual level with enhancements from the demographics of the surrounding geographic area. Segmenting by values such as age, gender, income, and marital status can assist in product development, creative design, and targeting.

There are several methods for using demographics to segment your database and/or build customer profiles. Later on in the chapter, I will create a customer value matrix using a combination of demographic and performance measures for a database of credit card customers.

Life Stage

Whether we like it or not, we are all aging! And with few exceptions, our lives follow patterns that change over time to meet our needs. These patterns are clustered into groups defined by demographics like age, gender, marital status, and presence of children to form life stage segments.

Life stage segments are typically broken into young singles; couples or families; middle-aged singles, couples, or families; and older singles or couples. Addiional enhancements can be achieved by overlaying financial, behavioral, and psychographic data to create well-defined homogeneous segments. Understanding these segments provides opportunities for businesses to develop relevant products and fine-tune their marketing strategies.

At this point, I've spent quite a bit of time explaining and stressing the importance of profiling and segmentation. You can see that the methodologies vary depending on the application. Before I get into our case studies, it is worthwhile to stress the importance of setting an objective and developing a plan. See the accompanying sidebar for a discussion from Ron Mazursky on the keys to market segmentation.

Ron Mazursky, a consultant with many years' experience in segmentation for the credit card industry and president of Card Associates, Inc., shares his wisdom on market segmentation. Notice the many parallels to basic data modeling best practices.

Pat was in the office early to develop the budget and plans for the coming year when Sal came in.

"Good morning, Pat. Remember the meeting we had last week? Well, I need you to make up for the shortfall in income we discussed. Come up with a plan to make this happen. Let's discuss it on Friday." Sal left the office after a few pleasantries. Pat thought to himself, "Not much more to say. Lots to think about. Where do I start?"

If this hasn't happened to you yet, it will. Senior management tends to oversee corporate goals and objectives. Unfortunately, more often than not, clear and precise business objectives are not agreed to and managed carefully. As a result, business lines may end up with contradictory goals and strategies, leading to unintended outcomes.

We need to manage business lines by specific objectives. These objectives should be targeted and measurable. By targeted, we mean well defined by identifying criteria, such as demographic, geographic, psychographic, profitability, or behavioral. By measurable, we mean that all objectives should have a quantitative component, such as dollars, percents, or other numbers-based measures.

In determining our strategy to improve performance, we typically need to identify a process for segmenting our customer or prospect universe to focus our efforts. Market segmentation frequently involves classifying a population into identifiable units based on similarities in variables. If we look at the credit card universe (where Sal and Pat work), we can identify segments based on behavioral tendencies (such as spending, credit revolving, credit score), profitability tendencies (such as high, medium, low), psychographic tendencies (such as value-added drivers like rewards, discounts, insurance components--core features and benefit drivers like lower rates, lower or no fees, balance transfer offers, Internet access--and affinity drivers like membership in clubs, alumni organizations, charities), and more.

The process of market segmentation can be pursued through various models. We will present but one approach. Modify it as you develop your segmentation skills. You can be assured that this approach is not "cast in stone." With different clients and in different scenarios, I always adjust my approach as I evaluate a specific situation.

Ten Keys to Market Segmentation

1. Define your business objectives. At the start of any segmentation process, agree on and clearly state your goals using language that reflects targeting and measurement. Business objectives can be (1) new account, sales, or usage driven; (2) new product driven; (3) profitability driven; or (4) product or service positioning driven.

2. Assemble your market segmentation team. Staff this team from within your organization and supplement it, as necessary, with outside vendors. The key areas of your organization ought to be included, such as marketing, sales, market research, database analysis, information systems, financial analysis, operations, and risk management. This will vary by organization and industry.

3. Review and evaluate your data requirements. Make sure you have considered all necessary data elements for analysis and segmentation purposes. Remember to view internal as well as external data overlays. Types of data could include survey, geo-demographic overlays, and transactional behavior. Data must be relevant to your business objectives. You are reviewing all data to determine only the necessary elements because collecting and analyzing data on all customers or prospects is very time-consuming and expensive.

4. Select the appropriate basis of analysis. Data is collected on different bases--at different times you might use individual-specific, account-level, or house-hold-level data. First understand what data is available. Then remember what is relevant to your business objective.

5. Identify a sample from the population for analysis. Who do you want to analyze for segmentation purposes? Very often the population is too large (and too expensive) to analyze as a whole. A representative sample should be chosen based on the business objective.

6. Obtain data from the various sources you've identified for the sample you've selected. The analytical database may contain transactional data, survey data, and geo-demographic data. Data will likely be delivered to you in different formats and will need to be reformatted to populate a common analytical database.

7. "Clean" the data where necessary. In some cases, records can contain data that might not be representative of the sample. These "outliers" might need to be excluded from the analysis or replaced with a representative (minimum, maximum, or average) value.

8. Select a segmentation method that is appropriate for the situation. There are three segmentation methods that could be employed: predefined segmentation, statistical segmentation, or hybrid segmentation. The predefined segmentation method allows the analyst to create the segment definitions based on prior experience and analysis. In this case, you know the data, you work with a limited number of variables, and you determine a limited number of segments. For example, in Sal and Pat's business, we've had experience working with purchase inactive segments, potential attriter segments, and potential credit usage segments. The appropriate segments will be defined and selected based on the business objective and your knowledge of the customer base.

9. The statistical method should be employed when there are many segments involved and you have little or no experience with the population being investigated. In this case, through statistical techniques (i. e., cluster analysis), you create a limited number of segments (try to keep it under 15 segments). This method could be employed if you were working on a new customer base or a list source where you had no prior experience. Hybrid segmentation allows you to combine predefined segmentation with statistical segmentation, in any order, based on your success in deriving segments. The combination of methods will yield a greater penetration of the customer base, but it will likely cost significantly more than applying only one approach.

10. Determine how well the segmentation worked. Now that we've applied the segmentation method appropriate for the situation, we need to evaluate how well the segmentation method performed. This evaluation analysis can be conducted via quantitative and qualitative steps. The analysis should determine whether all individuals within each segment are similar (profile, frequency distributions), whether each segment is different from the other segments, and whether each segment allows for a clear strategy that will meet the business objective.

Segments should pass the following RULEs in order to be tested:

  • Relevant to the business objective
  • Understandable and easy to characterize
  • Large enough to warrant a special offering
  • Easy to develop unique offerings

Apply the segmentations that have passed the above RULEs to various list sources and test the appropriate tactics. After testing, evaluate the results behaviorally and financially to determine which segmentations and offerings should be expanded to the target population. How did they perform against the business objectives?

By the time you've reached this last step, you may have what you think are a number of winning segmentations and tactics. We often fail to remember the business objectives until it is too late. It is critical that you have designed the segmentations to satisfy a business objective and that you have evaluated the market tests based on those same business objectives.

It feels great having actionable, well-defined segments, but do they achieve your original set of business objectives? If not, the fall-out could be costly on other fronts, such as lower profitability, reduced product usage, or negative changes in attitude or expectations.

By keeping your business objectives in mind throughout the development, testing, and analysis stages, you are more assured of meeting your goals, maximizing your profitability and improving your customers' long-term behavior.

Profiling and Penetration Analysis of a Catalog Company's Customers

Southern Area Merchants (SAM) is a catalog company specializing in gifts and tools for the home and garden. It has been running a successful business for more than 10 years and now has a database of 35,610 customers. But SAMs noticed that its response rates have been dropping, and so it is interested in learning some of the key drivers of response. It is also interested in expanding its customer base. It is therefore looking for ways to identify good prospects from outside list sources. The first step is to perform RFM analysis.

RFM Analysis

As mentioned earlier, recency, frequency, and monetary value are typically the strongest drivers of response for a catalog company. To discover the effects of these measures on SAM's database, I identify the variables in the database:

  • lstpurch. Months since last purchase or recency.
  • numpurch. Number of purchases in the last 36 months or frequency.
  • totpurch. Total dollar amount of purchases in the last 36 months or monetary value.

The first step is to get a distribution of the customers' general patterns. I use PROC FREQ to calculate the number customers in each subgroup of recency, frequency, and monetary value. To make it more useful, I begin by creating formats to collapse the subgroups. PROC FORMAT creates templates that can be used in various summary procedures. The following code creates the formats and produces the frequencies:

proc format;
value recency
low-1 = '0-1 Months'
2-3 = '2-3 Months'
4-7 = '4-7 Months'
8-12 = '8-12 Months'
13-high = '13+ Months'
value count
. = 'Unknown'
0-1 = '0-1'
2-4 = '2-4'
5-10 = '5-10'
11-< 21 = '11-20'
21-high = '21-30'
low-< 100 = '< $100'
101-200 = '$ 100-$ 200'
201-300 = '$ 200-$ 300'
301-400 = '$ 300-$ 400'
401-500 = '$ 400-$ 500'
500-high = '$ 500+ '
proc freq data= ch08. customer;
format lstpurch recency. numpurch count. totpurch sales.;
table lstpurch numpurch totpurch/ missing;

Figure 8.1 provides a good overview of customer buying habits for SAMs. I can see that the majority of customers haven't purchased anything for at least four months. A large percentage of customers made between two and four purchases in the last year with 85% making fewer than five purchases. The total dollar value of yearly total purchases is mainly below $100, with almost 85% below $300.

The next step is to look at the response rate from a recent catalog mailing to see how these three drivers affect response. The following code sorts the customer file by recency and creates quintiles (equal fifths of the file). By calculating the response rate for each decile, I can determine the relationship between recency and response.

proc sort data= ch08. customer;
by lstpurch;

data ch08. customer;
set ch08. customer;
rec_ ord = _n_;

proc univariate data= ch08. customer noprint;
var rec_ ord;
output out= ch08. rec_ dec pctlpts= 20 40 60 80 100 pctlpre= rec;

data freqs;
set ch08. customer;
if (_ n_ eq 1) then set ch08. rec_ dec;
retain rec20 rec40 rec60 rec80 rec100;

data freqs;
set freqs;
if rec_ ord <= rec20 then Quantile = 'Q1'; else
if rec_ ord <= rec40 then Quantile = 'Q2'; else
if rec_ ord <= rec60 then Quantile = 'Q3'; else
if rec_ ord <= rec80 then Quantile = 'Q4'; else
Quantile = 'Q5';
label Quantile= 'Recency Quantile';

proc tabulate data= freqs;
class quantile;
var respond;
table quantile= 'Quantile'* respond= ' '* mean= ' '* f= 10.3, all= 'Response
Rate'/ rts= 12 row= float box= 'Recency';

This process is repeated for frequency and monetary value. PROC TABULATE displays the response rate for each quintile. The results for all three measuresare shown in Figure 8.2. We can see that the measure with the strongest relationship to response is recency.

Figure 8.3 compares recency, frequency, and monetary value as they relate to response. Again, we can see that the recency of purchase is the strongest driver. This is a valuable piece of information and can be used to target the next catalog. In fact, many catalog companies include a new catalog in every order. This is a very inexpensive way to take advantage of recent purchase activity.

Penetration Analysis

As I said earlier, SAM wants to explore cost-effective techniques for acquiring new customers. Penetration analysis is an effective method for comparing the distribution of the customer base to the general population. As I mentioned in chapter 2, many companies sell lists that cover a broad base of the population.

The methodology is simple. You begin with a frequency distribution of some basic demographic variables. In our case, I select age, gender, length of residence, income, population density, education level, homeowner status, family size, child indicator.

proc format;
value age
0-29 = ' < 30'
30-34 = '30-34'
35-39 = '35-39'
40-44 = '40-44'
45-49 = '45-49'
50-54 = '50-54'
55-64 = '55-64'
65-high = '65+ '
value $gender
' ' = 'Unknown'
'M' = 'Male'
'F' = 'Female'
value count
. = 'Unknown'
0-2 = '0-2'
3-5 = '3-5'
6-10 = '6-10'
11-< 21 = '11-20'
21-high = '21-30'
proc freq data= ch08. customer;
format age age. length count.;
table age length /missing;

Figure 8.4 shows the output from PROC FREQ for the first two variables. This gives us information about the distribution of our customers. Notice how 33% of the customers are between the ages of 45 and 50. In order to make use of this information for new acquisition marketing, we need to compare this finding to the general population. The next PROC FREQ creates similar profiles for the general population:

proc freq data= ch08. pop;
format age age. length count.;
table age length /missing;

Notice how Figure 8.5 displays the same distributions as Figure 8.4 except this time they are on the general population. Figure 8.6 shows a market comparison graph of age. Table 8.1 brings the information from the two analyses together and creates a measure called a penetration index. This is derived by dividing the customer percentage by the market percentage for each group and multiplying by 100.

Figure 8.6 provides a graphical display of the differences in distribution for the various age groupings. SAM would be wise to see new customers in the 35Ð 44 age group. This age range is more prominent in its customer base than in the general population.

Developing a Customer Value Matrix for a Credit Card Company

Our second case study expands the use of profiling and segmentation to a customer view that reflects behavior as well as demographics. Risk is a form of behavior that has implications in many industries. As we saw in our life insurance case study in chapters 3 through 7, the risk of claims is a strong profit driver in the insurance industry. Credit card banks are also vulnerable to the effect of risk. A slight increase in bankruptcies and charge-offs can quickly erode small profit margins.

To understand our customer base with respect to revenue and risk, I perform a customer value analysis. This allows me to segment the customer base with respect to profitability leading to improved customer relationship management.

Customer Value Analysis

Credit card profitability is achieved by balancing revenue (less costs) and risk. An effective way to segment the market is by a combination of risk and net revenue. The first step is to determine the splitting values for risk and revenue. In this case, I use $150 for revenue and 650 for risk. These values are not cast in stone. The revenue value of $150 represents some revolving activity or high transaction activity. Accounts with revenues higher than $150 are worth considering for more marketing efforts. The risk score of 650 corresponds to an average charge-off rate that the bank considers tolerable. Accounts with scores below 650 are considered high risk.

The following code uses PROC FORMAT to split the population into two groups by both revenue and risk:

proc format;
value revenue
low-< 151 = 'Low Revenue'
151-high = 'High Revenue'
value risk
low-< 651 = 'High Risk'
651-high = 'Low Risk'

Next, I use PROC TABULATE to create a customer value matrix. The procedure uses the previous formats to split the groups. The table statement crosses revenue (acctrev) by risk score (riskscr) with the number and percent of customers (records):

proc tabulate data= ch08. profit;
format acctrev revenue. riskscr risk.;
class acctrev riskscr;
var records;
table (acctrev= ' ' all= 'Total'),( riskscr= ' ' all= 'Total')
records= '# '* sum= ' '* f= comma8. records= '% '* pctsum= ' '* f= 8.2)
/rts= 15 box= 'Customer Value Matrix';

The results of the analysis are displayed in Figure 8.7. This matrix gives us an instant view of the customer database with respect to revenue and risk. We see that over 66% are considered high revenue and almost 53% are low risk. Our best customers, low risk and high revenue, make up 33% of our customer base.

The next step is to see what they look like. I will profile the customers within each segment.

The following data step creates a new variable called segment. This variable has a value for each of our four segments. Following the data step, I format the segment values for use in our profile table:

data ch08. profit;
set ch08. profit;
if riskscr < 651 then do;
if acctrev < 151 then
segment = '1';
else segment = '2';
else do;
if acctrev < 151 then
segment = '3';
else segment = '4';

proc format;
value $ segment
'1' = 'High Risk Low Revenue'
'2' = 'High Risk High Revenue'
'3' = 'Low Risk Low Revenue'
'4' = 'Low Risk High Revenue'

Using PROC TABULATE, I profile each segment by finding the average values of selected demographic and behavioral variables within each segment. The following code calculates the mean, minimum, and maximum for each selected variable:

proc tabulate data= ch08. profit;
format segment $segment.;
class segment;
var records age length numkids income male riskscr acctrev;
table records= 'Total Customers'* sum= ' '* f= comma9.
riskscr= 'Risk Score'*( mean= 'Mean'* f= comma5. min= 'Min'
*f= comma4. max= 'Max'* f= comma4.)
acctrev= 'Account Revenues'*( mean= 'Mean'* f= dollar6. min= 'Min'
*f= dollar5. max= 'Max'* f= dollar5.)
age= 'Age'*( mean= 'Mean'* f= comma5. min= 'Min'
*f= comma4. max= 'Max'* f= comma4.)
length= 'Length of Residence'*( mean= 'Mean'* f= comma5. min= 'Min'
*f= comma4. max= 'Max'* f= comma5.)
numkids= 'Number of Children'*( mean= 'Mean'* f= comma6.2 min= 'Min'
*f= comma4. max= 'Max'* f= comma4.)
income= 'Income (000) '*( mean= 'Mean'* f= dollar6. min= 'Min'
*f= dollar5. max= 'Max'* f= dollar5.)
male= 'Percent Male'*( mean= 'Mean'* f= percent6.2 min= 'Min'
*f= percent4. max= 'Max'* f= percent4.),
(segment= ' ' all= 'Total'
) /rts= 30 box= 'Customer Profiles';

In Figure 8.8, we see the variables across the rows and the segments in columns. This facilitates easy comparison of different values within the groups. I like to check the extreme values (min and max) for irregularities.

Once I am comfortable with the range of the variables, I display the mean values only in a table that is useful for developing marketing strategies. In Figure 8.9, the averages for each variable are displayed, and each segment is named for its overall character.

Managers and marketers find this type of analysis very useful for developing marketing strategies. Let's look at each segment separately:

Consummate consumers. These are the most profitable customers. They are low risk and generate high revenues. Banks can use this knowledge to offer extra services and proactively offer lower rates in the face of steep competition.

Risky revenue. These are also profitable customers. Their main liability is that they are high risk. Many banks see this as a reason to reduce balances. With higher pricing, though, these customers can be the most profitable because they are less likely to attrite.

Business builders. These are the most challenging customers. They would be profitable if they carried balances, but they tend to pay their full balance every month. Some in the industry call them the "dreaded transactors." These customers can sometimes be lured to revolve (carry balances) with low rates, but this can be a losing proposition for the bank. Another option is to charge an annual fee. Some banks are successful with a creative blending of purchase incentives and temporary low rates.

Balance bombs. No one wants these customers. They are risky and do not revolve balances. Some banks identify these customers so they can reduce their credit lines and raise their interest rates to dissuade them from continuing the relationship.

Every industry has drivers that can be effectively segmented. This simple exercise can provide direction and generate ideas for improved customer profitability.

Performing Cluster Analysis to Discover Customer Segments

Cluster analysis is a family of mathematical and statistical techniques that divides data into groups with similar characteristics. Recall that in chapter 4, I used frequencies to find similar groups within variable ranges. Clustering performs a similar process but in the multivariate sense. It uses Euclidean distance to group observations together that are similar across several characteristics, while attempting to separate observations that are dissimilar across those same characteristics.

It is a process with many opportunities for guidance and interpretation. Several algorithms are used in clustering. In our case study, I use PROC FASTCLUS. This is designed for use on large data sets. It begins by randomly assigning cluster seeds or centers. The number of seeds is equal to the number of clusters requested. Each observation is assigned to the nearest seed. The seed is then reassigned to the mean in each cluster. The process is repeated until the change in the seed becomes sufficiently small.

To illustrate the methodology, I use two variables from the catalog data in our earlier case study. Before I run the cluster analysis I must standardize the variables. Because the clustering algorithm I am using depends on distance between variable values, the scales of the variables must be similar. Otherwise, the variable with the largest scale will dominate the clustering procedure. The following code standardizes the variables using PROC STANDARD:

proc standard mean= 0 std= 1 out= stan;
var age income;

The programming to create the clusters is very simple. I designate three clusters with random seeds (random= 5555). Replace= full directs the program to replace all the seeds with the cluster means at each step. I want to plot the results, so I create an output dataset call outclus.

proc fastclus data= stan maxclusters= 3 random= 5555 replace= full
out= outclus;
var age income;

In Figure 8.10, the output displays the distance from the seeds to the farthest point as well as the distances between clusters. The cluster means do show a notable difference in values for age and income. For an even better view of the clusters, I create a plot of the clusters using the following code:

proc plot;
plot age* income= cluster;

The plot in Figure 8.11 shows three distinct groups. We can now tailor our marketing campaigns to each group separately. Similar to the profile analysis, understanding the segments can improve targeting and provide insights for marketers to create relevant offers.


In any industry, the first step to finding and creating profitable customers is determining what drives profitability. This leads to better prospecting and more successful customer relationship management. You can segment and profile your customer base to uncover those profit drivers using your knowledge of your customers, products, and markets. Or you can use data-driven techniques to find natural clusters in your customer or prospect base. Whatever the method, the process will lead to knowledge and understanding that is critical to maintaining a competitive edge.

Be on the lookout for new opportunities in the use of segmentation and profiling on the Internet. In chapter 13, I will discuss some powerful uses for profiling, segmentation, and scoring on demand.

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