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Improving E-Mail Response Rate
Based on the GLM analysis, the company can conclude that emails sent with detailed headers and text bodies are opened with greater frequency than emails sent with generic headings and HTML bodies. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.
General Linear Model
Between-Subjects Factors
HEADING
Detailed
Generic
OPENED
HTML
Tests of Between-Subjects Effects
Dependent Variable
Type III Sum of Squares
df
Mean Square
F
Sig.
Partial ETA Squared
Corrected Model
TRIAL1
TRIAL2
Intercept
TRIAL1
TRIAL2
HEADING
TRIAL1
TRIAL2
OPENED
TRIAL1
TRIAL2
TRIAL1
TRIAL2
HEADING * OPENED
TRIAL1
TRIAL2
HEADING * BODY
TRIAL1
TRIAL2
4.500
1
4.500
1.000
OPENED * BODY
TRIAL1
1
1.000
TRIAL2
1
1.000
HEADING * OPENED * BODY
TRIAL1
66.125
1
66.125
1.000
TRIAL2
32.000
1
32.000
1.000
Total
TRIAL1
12943.000
TRIAL2
16140.000
Corrected Total
TRIAL1
TRIAL2
7
a. R Squared = 1.000 (Adjusted R. Squared = .)
Estimated Marginal Means
1. HEADING
Estimates
Dependent Variable
HEADING
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
Detailed
35.750
Generic
37.000
TRIAL2
Detailed
45.750
Generic
36.750
Pairwise Comparisons
Dependent Variable
(I) HEADING
(J) HEADING
Mean Difference (I-J)
Std. Error
Sig.a
95% Confidence Interval for Differencea
Lower Bound
Upper Bound
TRIAL1
Detailed
Generic
-1.250
Generic
Detailed
1.250
TRIAL2
Detailed
Generic
9.000
Generic
Detailed
-9.000
Based on estimated marginal means
a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Univariate Tests
Dependent Variable
Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
TRIAL1
Contrast
3.125
1
3.125
1.000
Error
.000
0
TRIAL2
Contrast
1
1.000
Error
.000
0
The F tests the effect of HEADING. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.
2. OPENED
Estimates
Dependent Variable
OPENED
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
No
31.750
Yes
41.000
TRIAL2
No
33.750
Yes
48.750
Pairwise Comparisons
Dependent Variable
(I) OPENED
(J) OPENED
Mean Difference (I-J)
Std. Error
Sig.a
95% Confidence Interval for Differencea
Lower Bound
Upper Bound
TRIAL1
No
Yes
-9.250
Yes
No
9.250
TRIAL2
No
Yes
-15.000
Yes
No
15.000
Based on estimated marginal means
a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Univariate Tests
Dependent Variable
Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
TRIAL1
Contrast
1
1.000
Error
.000
0
TRIAL2
Contrast
1
1.000
Error
.000
0
The F tests the effect of OPENED. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.
3. BODY
Estimates
Dependent Variable
BODY
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
HTML
21.750
Text
51.000
TRIAL2
HTML
28.750
Text
53.750
Pairwise Comparisons
Dependent Variable
(I) BODY
(J) BODY
Mean Difference (I-J)
Std. Error
Sig.a
95% Confidence Interval for Differencea
Lower Bound
Upper Bound
TRIAL1
HTML
Text
-29.250
Text
HTML
29.250
TRIAL2
HTML
Text
-25.000
Text
HTML
25.000
a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
4. HEADING * OPENED
Dependent Variable
HEADING
OPENED
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
Detailed
No
28.000
Yes
43.500
Generic
No
35.500
Yes
38.500
TRIAL2
Detailed
No
35.000
Yes
56.500
Generic
No
32.500
Yes
41.000
5. HEADING * BODY
Dependent Variable
HEADING
BODY
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
Detailed
HTML
20.500
Text
51.000
Generic
HTML
23.000
Text
51.000
TRIAL2
Detailed
HTML
32.500
Text
59.000
Generic
HTML
25.000
Text
48.500
6. OPENED * BODY
Dependent Variable
OPENED
BODY
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
No
HTML
23.500
Text
40.000
Yes
HTML
20.000
Text
62.000
TRIAL2
No
HTML
29.500
Text
38.000
Yes
HTML
28.000
Text
69.500
7. HEADING * OPENED * BODY
Dependent Variable
HEADING
OPENED
BODY
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
TRIAL1
Detailed
No
HTML
22.000
Text
34.000
Yes
HTML
19.000
Text
68.000
Generic
No
HTML
25.000
Text
46.000
Yes
HTML
21.000
Text
56.000
TRIAL2
Detailed
No
HTML
32.000
Text
38.000
Yes
HTML
33.000
Text
80.000
Generic
No
HTML
27.000
Text
38.000
Yes
HTML
23.000
Text
59.000
1
2. The graphical display charts for this model are estimated marginal means of the two trials runs; the plots are included below. The profile plots show the relationship between the email body type (HTML or text) and the email heading type (generic or detailed). There is a clean distinction between the estimated marginal means of the two style choices for the emails.
TRIAL1
HEADING * BODY * OPENED
TRIAL2
HEADING * BODY * OPENED
The emails with detailed headers are opened at greater rates than the emails with generic headers. Moreover, the emails with text bodies are opened at greater rates than the emails with HTML bodies.
3. The main action that the company should take is to ensure the database they use for sending emails is segmented for their target market. Sending emails for direct marketing can produce some brand and revenue lift across all channels, which means that email engagement is reflected in metrics beyond those limited to conventional open and click measures. Indeed, emails can encourage consumers to take action on other channels, but unless the company has a handle on those types of consumer activities, they will not recognize the overarching influence of their email campaigns. By tightening the relation between the emails generated and the company's target market, the impact of the emails will be greater -- and this will be true across channels and not just for emails. This means that, since the open rates for emails overall has declined as the channel matured, the company will not be in a position to be measuring attributes that no longer have much relevance. By improving target market segmentation, the company will be in a better position to focus on conversions and to connect consumer engagement to multi-channel revenue generation.
4. The company should consider a model that explores more variables than they have considered in their initial testing. Below is an example configuration that illustrates a more comprehensive consideration of relevant variables. It is important to understand the base, which is essentially the number of emails that were actually sent. Further, the number of emails delivered is an important variable and this factor should also be represented by a bounce back rate. In addition to seeing the number of emails that were opened, assuming there is a call to action, the click through rate to the landing page should be included in the analysis. Furthermore, the click through rates for any additional webpages, if they are part of the model, should be considered as this is pegged to the number of leads and, eventually, to the conversion rate.
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