Regression analysis is a statistical tool for comparing variables. In the past, mailer's analysis 

was most often based on penetration reports and indexing various demographics separately, 

referred to as univariate analysis. A strong advantage of regression is that it takes into 

consideration the fact that index scores can quickly change when combined with other 

qualifiers/variables.

 

For example, an univariate study may show a high index score for "x" variable. However, a 

Regression would observe that "x" is a good prospect "only" if it meets a certain criteria.

 

The regression model can predict variables that are most strongly related to your objective, 

such as: higher response, higher profit, or greater purchase frequency.

 

The regression model will also stratify your file into several categories in relationship to sample 

size requirements and/or profit/cost restrictions. The below GAINS CHART is an example. From this 

chart, you may decide to only mail the top percentile in order to achieve your revenue goals.

GAINS CHART SUMMARY

Node	Node: n	Node: %	Resp: n	Resp: %	Gain (%)	Index (%)		Node: n	Node: %	Resp: n	Resp: %	Gain (%)	Index (%)
2	1,327	9.6	1,327	16.1	100.0	167.0		1,327	9.6	1,327	16.1	100.0	167.0
22	635	4.6	635	7.7	100.0	167.0		1,962	14.2	1,962	23.8	100.0	167.0
30	565	4.1	565	6.8	100.0	167.0		2,527	18.3	2,527	30.6	100.0	167.0
7	443	3.2	443	5.4	100.0	167.0		2,970	21.5	2,970	36.0	100.0	167.0
13	443	3.2	443	5.4	100.0	167.0		3,413	24.7	3,413	41.3	100.0	167.0
45	107	0.8	107	1.3	100.0	167.0		3,520	25.5	3,520	42.6	100.0	167.0
32	95	0.7	94	1.1	98.9	165.2		3,615	26.2	3,614	43.7	100.0	166.9
9	114	0.8	111	1.3	97.4	162.6		3,729	27.0	3,725	45.1	99.9	166.8
23	142	1.0	134	1.6	94.4	157.6		3,871	28.1	3,859	46.7	99.7	166.4
38	244	1.8	229	2.8	93.9	156.7		4,115	29.8	4,088	49.5	99.3	165.9
3	222	1.6	208	2.5	93.7	156.4		4,337	31.4	4,296	52.0	99.1	165.4
15	143	1.0	129	1.6	90.2	150.6		4,480	32.5	4,425	53.6	98.8	164.9
10	207	1.5	172	2.1	83.1	138.7		4,687	34.0	4,597	55.6	98.1	163.8
28	206	1.5	165	2.0	80.1	133.7		4,893	35.5	4,762	57.6	97.3	162.5
16	757	5.5	548	6.6	72.4	120.9		5,650	41.0	5,310	64.3	94.0	156.9
33	185	1.3	121	1.5	65.4	109.2		5,835	42.3	5,431	65.7	93.1	155.4
11	362	2.6	234	2.8	64.6	107.9		6,197	44.9	5,665	68.6	91.4	152.6
41	244	1.8	147	1.8	60.2	100.6		6,441	46.7	5,812	70.4	90.2	150.7
19	446	3.2	266	3.2	59.6	99.6		6,887	49.9	6,078	73.6	88.3	147.3
4	672	4.9	374	4.5	55.7	92.9		7,559	54.8	6,452	78.1	85.4	142.5
37	243	1.8	134	1.6	55.1	92.1		7,802	56.6	6,586	79.7	84.4	140.9
27	545	4.0	299	3.6	54.9	91.6		8,347	60.5	6,885	83.3	82.5	137.7
8	440	3.2	203	2.5	46.1	77.0		8,787	63.7	7,088	85.8	80.7	134.7
36	234	1.7	106	1.3	45.3	75.6		9,021	65.4	7,194	87.1	79.7	133.1
25	567	4.1	256	3.1	45.1	75.4		9,588	69.5	7,450	90.2	77.7	129.7
39	751	5.4	282	3.4	37.5	62.7		10,339	75.0	7,732	93.6	74.8	124.9
47	114	0.8	41	0.5	36.0	60.0		10,453	75.8	7,773	94.1	74.4	124.2
40	444	3.2	121	1.5	27.3	45.5		10,897	79.0	7,894	95.6	72.4	120.9
6	52	0.4	13	0.2	25.0	41.7		10,949	79.4	7,907	95.7	72.2	120.6
17	216	1.6	43	0.5	19.9	33.2		11,165	80.9	7,950	96.2	71.2	118.9
43	782	5.7	147	1.8	18.8	31.4		11,947	86.6	8,097	98.0	67.8	113.2
26	833	6.0	111	1.3	13.3	22.2		12,780	92.7	8,208	99.4	64.2	107.2
14	172	1.3	16	0.2	9.3	15.5		12,952	93.9	8,224	99.5	63.5	106.0
42	168	1.2	15	0.2	8.9	14.9		13,120	95.1	8,239	99.7	62.8	104.8
46	308	2.2	13	0.2	4.2	7.0		13,428	97.4	8,252	99.9	61.5	102.6
24	101	0.7	3	0.0	3.0	5.0		13,529	98.1	8,255	99.9	61.0	101.9
20	72	0.5	2	0.0	2.8	4.6		13,601	98.6	8,257	99.9	60.7	101.4
34	193	1.4	5	0.1	2.6	4.3		13,794	100.0	8,262	100.0	59.9	100.0
	13,794	100%	8,262	100%

CUSTOMER ADDRESS SENSITIVITY

Even though there is a "Data Confidentiality Agreement," provided with Dirmark's research, 

some firms may have a corporate policy preventing the release of company data. Fortunately, 

the regression model does not need company name, address, or phone data. The regression model 

only needs a unique record ID number and all data elements that can predict the stated objective 

(response, profit, size, SIC, etc.)

Moreover, there are numerous techniques for masking data that will assure complete confidentially 

of a client's database. Thus, the regression model may state the following:

Node#14 = highest likelihood of profit

Node#14 + SIC + 23,49,58,7311,57, 59

and yellow +01,01,03

and years in business + 5+

and

red = $55

Number of "ids" within Node #14: 3,587

REGRESSION-DRAWBACK

The Regression Analysis is good in creating a profile of the ideal target market. However, because 

the profile may consist of many qualifiers, finding a sufficient mail quantity may be difficult.

ADVANTAGES

Simply, a Regression will define your most profitable segments within your customers file and 

allow you to apply these models to mail less and gain more profit.. In addition, a Regression 

will also allow you to make your prospect lists perform more like your house lists, along with 

identifying new prospect segments that were formerly unidentified.