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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.
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