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please respond to each post separately.
1.I agree that productivity is an import
please respond to each post separately.
1.I agree that productivity is an important factor for maximizing the allocation of financial and other resources among different cereal brands. It’s essential for businesses to measure their marketing approaches and evaluate their return on investment in terms of profitability or incremental sales for each channel (Zeebaree et al., 2019). As you mentioned, it’s also important to keep in mind that not all ready-to-eat cereals serve the same purpose or have the same goals. Different marketing approaches may be more effective for creating brand awareness versus stimulating short-term sales for a specific product. Therefore, it’s crucial for businesses to tailor their marketing strategies based on their specific goals and target audiences.
References
Zeebaree, S. R., Shukur, H. M., & Hussan, B. K. (2019). Human resource management systems for enterprise organizations: A review. Periodicals of Engineering and Natural Sciences, 7(2), 660-669.
2.”Simple random sampling entails selecting a sample so that every possible sample with the same number of observations has an equal chance of being chosen and it best represents a bigger population” (Anderson, Sweeney, & Williams, 2018). Since this method will make my data-gathering process unbiased, I can, therefore, be confident that the rate responses to my campaign from the random sample of 1,000 households will accurately represent the US population by ethnicity.
Based on the journal of Alexander, Halyna, & Holguín-Veras that was published in 2021, random utility theory may be applied to an online retail store when it comes to studying the consumer’s buying behavior. “The maximization likelihood method is used in this system.” With respect to our homework scenario, using the random utility theory, accurate discrete choice data may be gathered from either the customer’s instant response to the marketing campaign or their tendency to ignore the campaign. The rate responses from this experiment can certainly be categorized by ethnicity. I can analyze which ethnic group is quick to respond to my campaign and which ethnic group most likely ignores it. However, this method is best applied only in smaller populations. It is not ideal for an experiment with a large population. A large population may make the sampling very complicated to execute, and as a result, will widen the parameters required in the study which will then lengthen the “simulation time” (Rossolov, Rossolova, & Holguín-Veras, 2021).
Simple random sampling is only one of the four random sampling techniques. The other three techniques are namely, systematic, stratified, and cluster randomization according to Samuel Stratton (2021). Like the random utility theory, stratified random sampling method becomes complicated when the controlling factors are increased. However, the more controlling factors are included in the experiment, the more efficient the results become. “This is because each stratum achieves an improved estimation accuracy and each layer of the samples can decrease.” With that said, I can make the response rates even narrower by adding factors such as subdividing the Asian group into different nationalities such as Chinese, Japanese, Filipinos, Taiwanese, Malaysians, Cambodians, Vietnamese, etcetera – and further grouping the participants into the number of members within a household while also identifying their age range and gender. In doing so, I can extract a more specific response rate data comparison between children and adults, between male and female, and/or between nationalities within the same ethnic group (Wang, Yang, & Yang, 2019).
When my direct marketing campaign is expected to generate a result within a short period, I can utilize the systematic random sampling technique. Following the example in our textbook, if I urgently desire to have a sample size of 10 from the population of 1,000 households, I can randomly select one of the first 100 households from the population list of all ethnic groups. With this technique, the population set does not have to be in sequence, making it easier to gather the data. Whereas with cluster random sampling, I will have to divide the ethnic groups into individual clusters. Each cluster contains different nationalities but they all belong to the same ethnicity. This technique is best to use if each cluster is a representation of the whole population, meaning that each cluster of ethnic groups will have to contain the same number of households or the same number of nationalities. In a perfect world, if all clusters are the same, randomly selecting a sample from one cluster will mirror the other clusters as well (Anderson, Sweeney, & Williams, 2018). But we do not live in a perfect world and there is no guarantee that all these ethnic groups will contain the same number of nationalities and households.
In conclusion, I would prefer stratified random sampling technique because it provides a more efficient and accurate information concerning the response rates. Although it takes more time and may be costly to run the experiment this way, my main goal in designing the direct marketing campaign is to produce the most accurate data that I can use in making a well-informed decision that may be translatable into more revenues.
References
Rossolov, A., Rossolova, H., & Holguín-Veras, J. (2021). Online and in-store purchase behavior: shopping channel choice in a developing economy. Transportation, 48(6), 3143-3179. https://doi.org/10.1007/s11116-020-10163-3
Anderson, D., Sweeney, D., Williams, T. (2018). Modern business statistics with microsoft office excel, 6th ed. Cengage. ISBN: 9781337115186
Stratton, S. J. (2021). Population research: Convenience sampling strategies. Prehospital and Disaster Medicine, 36(4), 373-374. https://doi.org/10.1017/S1049023X21000649
Wang, R., Yang, F., & Yang, F. (2019). The sampling method and rotation strategy of the smart meter based on the stratified and staged sampling method. IOP Conference Series: Earth and Environmental Science, 252(3)https://doi.org/10.1088/1755-1315/252/3/032093
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