Business Statistics
Read the following case study.�
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A company wishes to improve its e-mail marketing process, as measured by an increase in the response rate to e-mail�
advertisements. The company has decided to study the process by evaluating all combinations of two (2) options of the�
three (3) key factors: E-Mail Heading (Detailed, Generic); Email Open (No, Yes); and E-Mail Body (Text, HTML). Each of�
the combinations in the design was repeated on two (2) different occasions. The factors studied and the measured response�
rates are summarized in the following table.�
Introduction
E-mail marketing process has continued to gain significant attention as one of the best, convenient and cost effective method of advertising through which targeted marketing can be carried out. As a result, e-mail marketing has over the recent past being adopted by both small and big companies an their preferred choice of marketing because of its potential to reach a wider customer base, carry out targeted marketing, and this has been facilitated by tremendous technological advancements witnessed over the last one decade.
An evaluation and rearrangement of the collected data about the three (3) key factors of e-mail advertisements considered in all two (2) combinations can be summarized in the table shown below:
Table 1: A summary of the e-mail marketing combinations
| KEY FACTORS OF E-MAIL ADVERTISEMENT | ||||||
| Replicate 1 | Email Heading | Email Open | Email Text | |||
| Generic | Detailed | No | Yes | Text | HTML | |
| Response Rate | 148 | 143 | 127 | 164 | 204 | 87 |
| Replicate 2 | Email Heading | Email Open | Email Text | |||
| Generic | Detailed | No | Yes | Text | HTML | |
| Response Rate | 147 | 183 | 135 | 195 | 215 | 115 |
| Total | 295 | 326 | 262 | 359 | 419 | 202 |
Based on the data presented in table 1 above a design of experiment (DOE) is conducted in order to test cause-and-effect relationships in business processes for the company. For instance, it can be succinctly determined that the response rate is the measure to the improvement of email advertisements. Hence, a cause-and-effect relationship model (simple linear regression model) shown below shall be used:
This implies that the response rate shall be determined by the key factors of the email advertisement whereby they can be considered all of them, but in this case the cause-and-effect relationship shall be conducted for a particular key factor of the email advertisement against response rate.
The graphical display tool to be used to present the results of the DOE conducted is a linear graph, and the linear graphs are presented for replicate 1, replicate 2 and the total of replicate 1 and 2. The rationale behind using the linear graph is that, apart from making sure that the data from the conducted study being presented visually, there is also a linear linkage of all the data points in the graph in order to succinctly show the specific performance of each key factor of email advertisement.
The linear graphs shown below are for each of the three cause-and-effect relationships considered in the design of experiment (DOE) which are conducted such as: replicate 1, replicate 2 and the total of replicate 1 and 2 are presented in the figure 1, figure 2 and figure 3 respectively.
Figure 1: Replicate 1 Response Rate
Figure 2: Replicate 2 Response Rate
Figure 3: Total Response Rate
From the critical evaluation of the data collected by the company concerning its email advertisement process, as a method of improving its marketing email response rate, it is evidently clear that there are two main actions that the company ought to implement swiftly in order to ensure that its e-mail advertising response rate is increased significantly. For instance, from the data summary table and the subsequent three linear graphs it is undoubtedly clear that the company needs to change its e-mail heading and body in order to make sure that the response rate is increased considerably. The rationale for this recommendation is due to the fact that, detailed e-mails (from e-mail heading) as well as text e-mails (from e-mail body) both got the highest levels of opening and eventually highest rates of response for both replicates.
Considering that the company adopted a simple linear regression model to determine the cause-and-effect relationships, this strategy could only compare a single key factor of e-mail advertisement to its respective response rate. However, adoption of a multiple linear regression model would be an overall strategy which could be highly appropriate for developing a process for the company because it has the potential to considerably increase the response rate of its e-mail advertising eventually leading to obtaining of effective business process. The rationale for this proposal is due to the fact that, the prediction outcome of the cause-and-effect relationship when more than one factor of email advertisement is considered is more effective than when only one factor is considered. For example, the design of experiment (DOE) conducted in order to test cause-and-effect relationships in business processes for the company indicate that the company has to adopt e-mail headings that are detailed as well as text e-mail body for increased levels of opening as well as increased rates of response. Hence, a multiple linear regression would help to simultaneously compare the effect of these factors to response rate rather than considering each of them individually. This is highly essential in making sure that the overall impact of e-mail advertising on basis of its specific key factors is evaluated at once.
Conclusion
In conclusion, it can be clearly observed that the design of experiment (DOE) can be conducted in order to test cause-and-effect relationships in business processes using appropriate models and graphical display tools. Also the company can implement various actions in order to ensure that the response rate is significantly increased.
References
Creswell, J.W. (2014). Research design: Qualitative and quantitative approaches, (3rd ed.). Thousand Oaks, California: Sage.
Sapsford, R. & Jupp, V. (2006). Data Collection and Analysis, (4th ed.). Thousand Oaks: Sage Publications Ltd.
Sauders, M., Philip, L. & Thornhill, A. (2007). Research Methods for Business Students. London: Pitman Publishing.
Sekaran, U. (2010). Research Methods for Business. Hoboken, NJ: John Wiley & Sons, Inc.
