A full 51% of the overall respondents to the 1998 Catalog Age Benchmark Report on Lists and Databases (see p. 63) don’t perform any sort of database modeling. And among respondents with annual sales of less than $10 million, the figure climbs to 68%.
One reason so few catalogers take advantage of their databases could be that they’re confused about their options. All around them, database experts are proselytizing about the benefits of predictive modeling and the wonders of RFMP without defining the terms. So to get you started on exploring the possibilities of database marketing, Catalog Age is providing a primer of terms.
STATISTICAL RESPONSE MODELING Also known as predictive modeling, this method of analysis enables you to predict a response by looking at a number of factors together. “Using the variables simultaneously allows you to pick up the true value of a factor in context,” says Dan Steinberg, president of database firm Salford Systems. For instance, if you rate your customers solely by age, you could find that your older customers are your best customers. But if you add another variable, such as household income, you may discover that most of your older customers are also your wealthiest-but that these wealthy older buyers actually spend less than younger buyers who earn the same amount of money.
PROFILING If statistical modeling is predictive, profiling is descriptive-it’s an umbrella phrase for any of several methods of analyzing your database so that you can describe the characteristics of buyer segments. For instance, profiling could show you that your buyers with the greatest lifetime value are semi-industrial suppliers with 50-75 employees located in midsize towns, or that your most responsive customers are single women ages 25-34 with an average income of $25,000 who buy primarily around the holidays.
RFM An acronym for recency, frequency, monetary value, RFM is “a low-cost, effective way to get into modeling without a lot of expertise and statistical background,” says Eric Ruf, vice president of database marketing consultancy Ruf Strategic Solutions. With RFM, you score customers in terms of how recently they bought from you, how frequently they’ve bought from you in a given time period, and how much they spent with you in that same period. A simplified example:
RECENCY customers who bought in the past month = 3 points
customers who bought in the past six months = 2 points
customers who haven’t bought in at least six months = 1 point
FREQUENCY customers who made at least three purchases in the past year = 3 points
customers who made one or two purchases in the past year = 2 points
customers who didn’t make a purchase in the past year = 1 point
MONETARY VALUE customers who spent more than $300 in the past year = 3 points
customers who spent $100-$299 in the past year = 2 points
customers who spent less than $100 in the past year = 1 point
Your best customers would score a total of 9 points; your least productive customers would score just 3 points. Then when you planned your next mailing, you could decide if you wanted to mail to just your 20,000 highest-scoring customers. Or for certain specials, you might decide to focus more on bigger spenders, even if they didn’t score so well on frequency.
Take RFM a step further, and you have RFMP: recency, frequency, monetary value, product. If you sell several categories of product or mail several titles, adding the product variable to your RFM scores can prevent you from, say, mailing an offer of PC software to Macintosh users.
REGRESSION ANALYSIS Like RFM, this modeling method uses scoring to predict results, but it takes into account more historical and demographic variables and relies on statistical calculations to weigh the variables. Certain standard industrial classifications (SICs), for instance, might be assigned a higher value than others; household income might be factored by a number so that wealthier buyers are ranked as proportionately more valuable than less-wealthy customers.
TREE ANALYSIS Using flow charts known as decision trees, this method breaks down your database by variables. Let’s say you want to determine the characteristics of your best buyers. First, you might sort your house file by age groups; then you might want to sort those age groups by whether they own their homes; then you could separate those who have children from those who don’t. A very simplified version of the resulting decision tree could look like this:
HOUSE FILE You could then compare response among each of these categories, or branches, to determine which group-homeowners under the age of 40 with children, perhaps, or nonhomeowners at least 40 years old without children-has the highest lifetime value (a descriptive function), or which group is most likely to respond best to your next offer (a predictive function). CHAID (Chi-Square Automatic Interaction Detector) and CART (Classification and Regression Trees) are two of the more common types of tree analysis.
CLUSTERING This method of profiling is “very good for taking the mass of information you may have about customers,” Ruf says, “and grouping market segments together that you can then develop strategies around.” You can cluster customers by age group, SIC, income or sales level, geography, and other variables to create a demographic or psychographic profile of your most sizable segments of customers. “Ideally you want to market to each customer individually, but the economics aren’t there,” Ruf says. “Clustering allows you to segment your market so that you can treat each segment as a distinct audience.”