Research Paper Undergraduate 1,146 words

Design of Experiment to Improve Email Response Rates

~6 min read 6 sections Business · Promotion

Abstract

This paper applies a full-factorial design of experiment (DOE) to analyze cause-and-effect relationships between three email marketing factors — email heading (generic vs. detailed), email opening (yes vs. no), and email body format (text vs. HTML) — and their effect on response rates. Using coded factor levels of +1 and −1, the study calculates both main effects and interaction effects for all factor combinations. The analysis finds that all three factors significantly influence response rates, with the email body format producing the largest main effect. Interaction effects indicate that the factors are interdependent, and the optimal combination — a detailed heading, an opened email, and a text body — produces the highest response rate. The paper concludes with a practical strategy centered on improving email heading effectiveness.

Key Takeaways

Introduction and Experimental Setup: DOE setup for testing email factor effects
Data Averaging and Factor Coding: Averaging replicates and coding factors as ±1
Main Effects Calculation: Main effect slopes for EH, EO, and EB
Interaction Effects Calculation: Two-way and three-way factor interaction effects
Interactions Chart and Recommended Action: Chart interpretation and optimal factor combination
Overall Strategy and Conclusion: Business strategy based on DOE findings

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What makes this paper effective

The paper follows a clear, step-by-step quantitative methodology: raw data → averaging → coding → main effects → interaction effects → interpretation → recommendation, making the analytical logic easy to follow.
Factor coding using +1 and −1 is explicitly defined and consistently applied, grounding each calculation in transparent notation that a reader can verify independently.
The paper moves naturally from statistical findings to practical business recommendations, bridging quantitative analysis and managerial action without overstating its conclusions.

Key academic technique demonstrated

The paper demonstrates a full-factorial 2³ design of experiment, calculating both main effects (the independent contribution of each factor) and all two-way and three-way interaction effects. By comparing total response rates at each factor level and computing slope averages, the paper identifies not just which factors matter, but how they depend on one another — a more nuanced and practically useful result than main effects alone.

Structure breakdown

The paper opens by presenting raw replicated data and computing run averages. It then encodes each factor into ±1 notation and computes main effects for all three factors, followed by interaction effects for all two-way and three-way combinations. An interactions chart synthesizes the findings visually. The final two sections interpret the chart to identify the optimal factor combination and propose a concrete business strategy focused on email heading design.

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Essay 1,146 words

Introduction and Experimental Setup

This analysis applies a design of experiment (DOE) to test cause-and-effect relationships in a business email marketing process. The objective is to identify which combination of email factors maximizes the response rate for the company's email advertising campaigns. Three factors are investigated: Email Heading (EH), Email Opening (EO), and Email Body format (EB). Each factor is tested at two levels, and replicate runs are conducted to improve the reliability of the estimates.

The raw experimental data, including replicate observations, are presented in Table 1. Each run varies the levels of the three factors — generic or detailed heading, whether the email is opened (yes or no), and whether the email body uses plain text or HTML formatting — and records the corresponding response rate.

Data Averaging and Factor Coding

Taking averages of the response rates from the replicate experiments produces the condensed data shown in Table 1.1 below:

Table 1.1 — Averaged Response Rates by Factor Combination

Run 1: Generic heading, No open, Text body — Response Rate: (46 + 38) / 2 = 42
Run 2: Detailed heading, No open, Text body — Response Rate: (34 + 38) / 2 = 36
Run 3: Generic heading, Yes open, Text body — Response Rate: (56 + 59) / 2 = 57.5
Run 4: Detailed heading, Yes open, Text body — Response Rate: (68 + 80) / 2 = 74
Run 5: Generic heading, No open, HTML body — Response Rate: (25 + 27) / 2 = 26
Run 6: Detailed heading, No open, HTML body — Response Rate: (22 + 32) / 2 = 27
Run 7: Generic heading, Yes open, HTML body — Response Rate: (21 + 23) / 2 = 22
Run 8: Detailed heading, Yes open, HTML body — Response Rate: (19 + 33) / 2 = 26

The data are then coded using +1 and −1 for each factor level, as follows: for the Email Heading factor (EH), +1 represents "Generic" and −1 represents "Detailed"; for the Email Opening factor (EO), +1 represents "No" and −1 represents "Yes"; and for the Email Body factor (EB), +1 represents "Text" and −1 represents "HTML." The resulting coded data table (Table 1.2) is shown below:

Table 1.2 — Coded Factor Levels and Response Rates

Run 1: EH = +1, EO = +1, EB = +1 — Response Rate: 42
Run 2: EH = −1, EO = +1, EB = +1 — Response Rate: 36
Run 3: EH = +1, EO = −1, EB = +1 — Response Rate: 57.5
Run 4: EH = −1, EO = −1, EB = +1 — Response Rate: 74
Run 5: EH = +1, EO = +1, EB = −1 — Response Rate: 26
Run 6: EH = −1, EO = +1, EB = −1 — Response Rate: 27
Run 7: EH = +1, EO = −1, EB = −1 — Response Rate: 22
Run 8: EH = −1, EO = −1, EB = −1 — Response Rate: 26

Main Effects Calculation

The main effect of each factor is calculated by comparing the total response rates at each level and computing the slope (average difference).

Main Effect of Email Heading (EH):
Total response rate when EH = +1: 163
Total response rate when EH = −1: 189.5
Slope / Average: (163 − 189.5) / 2 = −13.25

Main Effect of Email Opening (EO):
Total response rate when EO = +1: 131
Total response rate when EO = −1: 179.5
Slope / Average: (131 − 179.5) / 2 = −24.25

Main Effect of Email Body (EB):
Total response rate when EB = +1: 209.5
Total response rate when EB = −1: 101
Slope / Average: (209.5 − 101) / 2 = +54.25

The main effect results indicate that all three factors influence the response rate. The Email Body format has the largest main effect (+54.25), suggesting that plain text emails generate substantially higher response rates than HTML emails. Both the Email Heading and Email Opening factors show negative slopes, indicating that the "Detailed" heading and the "Yes" open condition are associated with higher response rates relative to their respective +1 baselines.

3 Sections Hidden · 530 words

Interaction Effects Calculation200 words

In addition to main effects, interaction effects between factors are calculated to determine whether the influence of one factor depends on the level of another factor (Manly, 1992).

Interactions Chart and Recommended Action180 words

The fact that response rates vary with different levels of the three factors confirms that main effects are present in the population (Morris, 2010). This implies that a main effects scatter plot could be used…

Overall Strategy and Conclusion150 words

The figure shows a significant interaction among the three factors, which implies that maximizing on one factor could go a long way in increasing the total response rate. It is proposed that the company focus on the Email Heading…

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References

Manly, B. F. (1992). The design and analysis of research studies. Cambridge University Press.

Morris, M. (2010). Design of experiments: An introduction based on linear models. Taylor & Francis.

Roy, R. K. (2001). Design of experiments using the Taguchi approach: 16 steps to product and process improvement. John Wiley & Sons.

Key Concepts in This Paper

DOE Factor Coding Main Effects Interaction Effects Email Heading Response Rate Factorial Design Email Body Format Email Opening Optimization