Cluster Sampling In Term Of Marketing
A sampling method that allows the researcher to create multiple clusters of people from a population in which they all have equal chance of getting selected and they all have equal characteristics is known as cluster sampling. This sampling technique is utilized in a geographical or area cluster sampling for market research. For the purpose of marketing, it is necessary to conduct survey for a broad geographic area. However, it can be expensive. Here, cluster sampling helps to conduct the survey through sending it to clusters that are divided on the basis of region (Latpate and Kshirsagar 2019).
This sampling techniques mainly implemented for the similar but diverse groups that forms a statistical population. The bifurcation of data into more productive and small groups can be done through the use of cluster sampling by the researchers. The wide use of this sampling technique makes it popular among market researchers and statisticians. Statisticians prefers to use the cluster sampling technique in order to produce more accurate result for their important for student research. First the sample must be created. Then the evaluation and creation of sampling frame is necessary. Further the group should be determined. Another step is to select the clusters. Finally, sub types must be created.
Types of cluster sampling in term of marketing
Cluster sampling technique is classified in various ways such as single stage cluster sampling, multiple stage cluster sampling and two stage cluster sampling. The number of stages to be followed in order to achieve the cluster sample indicates one way of cluster sampling. Another way of cluster sampling is to representation of the groups in the whole cluster. The step taken to get to the desired sample is referred to as a stage (Alimohamadi and Sepandi 2019).
In the case of the single stage cluster sampling, sampling is conducted for one time. In the case of the two-stage cluster sampling, it is avoided to select all the elements of a cluster. A simple random sampling or systematic random sampling is implemented in order to select a handful of members from each group (Shi and Chen 2021). There are few more steps or one more step for conducting the multiple stage cluster sampling. When the market research for marketing purposes need to conduct for multiple geographies, then it is necessary to rely on the complicated clusters that can be obtained through the implementation of the multiple stage sampling techniques.
Why would you use cluster sampling?
The application cluster sampling is wide in market research. The researcher mainly undertakes the cluster sampling when they cannot avail information about the population as a whole. As getting information about the clusters are easier for the researcher, they employ cluster sampling (Bowering et al. 2018). The presence of the reasonable number of clusters in comparison with the entire population makes the cluster sampling work better for the researcher.
As a result, the managers of the companies prefer to use cluster sampling in their marketing decision making. In this case, it is necessary to mention that the advantages of using cluster sampling over the simple random sampling makes it useful for the market researcher. Here, clusters are selected from the sampling. Moreover, some or all elements from selected clusters comprise the sample. Researchers examine a sample that consists of multiple sample parameters with the assistance of cluster sampling. Examples of such parameters include habits, backgrounds and demographics. There are several other write my assignment professional attributes that can be taken into consideration while conducting cluster sampling. These attributes may be the focus of conducted research.
Applications of cluster sampling in term of marketing
This sampling technique is widely used in marketing where the researcher cannot gather data from the entire population as a whole. In order to conduct market research, it is the most practical and economical solution for field of marketing (Humm et al. 2017). For example, a company that sells smartphone in the market is trying to understand the usage of smartphone in a particular country. Therefore, it will go for dividing the population of the whole country into cities, which is equivalent to clusters.
Then, it may choose cities with the highest population. Finally, it uses mobile devices in order to filter those. In this way, cluster sampling will enable the market research for the company successful. It will help the do my homework company to take marketing decision through examining the performance of smartphones across the selected country (Rapp, Peters and Dachsbacher 2019). There will no requirement of sampling frame for the researchers. As a result, the cluster sampling for the marketing can be conducted for all the elements for the whole population without the use of a sampling frame.
What are the advantages of clustering?
There are several advantages of cluster sampling, which makes it useful and effective. It lowers the cost of sampling significantly. Moreover, it also consumes less time to create a sample (Makela, Si and Gelman 2018). When sampling is conducted with the geographically divided groups, it requires less cost, work and time. Therefore, it is considered as the highly economical method to observe clusters. As a result, it can easily help to avoid the random sampling for a specific region through allocating a restricted number of resources to those chosen clusters. The access to this sampling technique is convenient. This sampling technique assists researchers to select large sample. As a result, it increases the accessibility to several clusters.
Hence, it helps in conducting market research easily (Attia, Moosavi and Sandu 2018). Information from several groups and areas are facilitated by the cluster sampling. Therefore, the application of the cluster sampling is easier for the researchers in practical situations in comparison with the other probability sampling methods. Each cluster may contain large samples in case of the cluster sampling. Thus, it helps to compensate the loss of accuracy in information per individual. The characteristics of a group including population can be easily determined with the help of this technique in comparison with the simple random sampling.