Comments on: How to Increase Your Direct Mail Response Rate http://www.seohosting.com/blog/copywriting/how-to-increase-your-direct-mail-response-rate/ Discussing the latest tips, tricks, and secrets of SEO Wed, 17 Mar 2010 00:11:43 +0000 http://wordpress.org/?v=2.9.2 hourly 1 By: Eric Novak http://www.seohosting.com/blog/copywriting/how-to-increase-your-direct-mail-response-rate/comment-page-1/#comment-32374 Eric Novak Tue, 12 Jan 2010 02:22:30 +0000 http://www.seohosting.com/blog/?p=2452#comment-32374 Scoring a prospect database enables a direct marketer to significantly reduce unprofitable marketing communications. Predictive models often use regression based statistics to calculate the probability that a prospect belongs to a group, e.g., buyer vs. non-buyer. There are several statistical techniques including discriminant analysis, neural net, and logistic regression used as predictive models. The modeling technique we typically use is discriminant analysis, which is basically a type of multiple regression in which the dependent variable is categorical data representing group membership (buyer vs. non-buyer). The predictor variables can be anything: an account’s past purchases (recency of purchase, value of purchase), age, income, life-stage group, NAICS/SIC codes (for B2B), home type, ownership of similar products; recoding these variables as dummy variables (1 and 0’s to indicate if the prospect is or is not a member of the group) represents >90%of the time required to build a predictive model. (You'll need a statistical software package like SPSS or SAS to do this.) When the discriminant model is run various outputs are provided including the percentage of prospects who were correctly assigned and, most importantly, a “structure matrix” which shows which variables are correlated most highly with group membership. Because we use the analysis to create lists for direct mail, or outbound telemarketing, we are primarily interested in the probability of buying the product or service we are marketing. Separate models are created for each product. By sorting records from highest probability score to lowest and converting each decile’s average probability score to an average sales rate (based on a small pilot campaign or recent history), we can estimate the number of new buyers that will result from a direct mail, e-mail or telemarketing campaign and the acquisition cost associated with each decile. While models based on data for zip codes areas are less precise than models built on block group or household specific data, significant amounts of geo-demographic data can be downloaded FOR FREE from government sources such as the US Census bureau and NOAA (for weather data). Household specific demographic and life stage segment data from firms like Acxiom, Equifax, etc. significantly improve model accuracy but at a higher cost. For more information, I recommend you get one of David Shepard Associates' excellent books on this subject, take one of his seminars (through DMA), or visit my site at ericnovakandassocaites.com. Scoring a prospect database enables a direct marketer to significantly reduce unprofitable marketing communications. Predictive models often use regression based statistics to calculate the probability that a prospect belongs to a group, e.g., buyer vs. non-buyer. There are several statistical techniques including discriminant analysis, neural net, and logistic regression used as predictive models. The modeling technique we typically use is discriminant analysis, which is basically a type of multiple regression in which the dependent variable is categorical data representing group membership (buyer vs. non-buyer). The predictor variables can be anything: an account’s past purchases (recency of purchase, value of purchase), age, income, life-stage group, NAICS/SIC codes (for B2B), home type, ownership of similar products; recoding these variables as dummy variables (1 and 0’s to indicate if the prospect is or is not a member of the group) represents >90%of the time required to build a predictive model. (You'll need a statistical software package like SPSS or SAS to do this.)

When the discriminant model is run various outputs are provided including the percentage of prospects who were correctly assigned and, most importantly, a “structure matrix” which shows which variables are correlated most highly with group membership. Because we use the analysis to create lists for direct mail, or outbound telemarketing, we are primarily interested in the probability of buying the product or service we are marketing. Separate models are created for each product. By sorting records from highest probability score to lowest and converting each decile’s average probability score to an average sales rate (based on a small pilot campaign or recent history), we can estimate the number of new buyers that will result from a direct mail, e-mail or telemarketing campaign and the acquisition cost associated with each decile.

While models based on data for zip codes areas are less precise than models built on block group or household specific data, significant amounts of geo-demographic data can be downloaded FOR FREE from government sources such as the US Census bureau and NOAA (for weather data). Household specific demographic and life stage segment data from firms like Acxiom, Equifax, etc. significantly improve model accuracy but at a higher cost.

For more information, I recommend you get one of David Shepard Associates' excellent books on this subject, take one of his seminars (through DMA), or visit my site at ericnovakandassocaites.com.

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