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Using Design Of Experiment Procedures For Direct Marketing Case Study

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Designs Market Segmentation Engagement Target Marketing
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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.

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).

This test is based on the linearly independent pairwise comparisons among the estimated marginal means.
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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