Week 5.4 Data Collection, Analysis, and Conclusions
Collecting and Analyzing Data
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We have covered the first two stages of marketing research so far.
In stage three, we collect and analyze the data. The data collection and analysis stage is the stage in which the researcher executes the research design that was set in stage two. The data is obtained from the identified sources or respondents in this stage. Researchers pay special attention to the data collection stage since any error in data collection will lead to inaccurate results in the next stage. Possible errors range from failure to select the right respondents to incorrect recording of observations. In analyzing the data, the researcher tries to identify patterns in the data.
Conclusions and Report
This is the last stage of the marketing research process. Here, the researcher identifies the conclusion from the analysis of the data. This stage involves interpreting the information extracted from the analysis of the data. The researcher also prepares a report and makes suggestions for possible actions that might be taken by the organization. A team from the organization works with the researcher to make sure that a solution to the marketing problem gets implemented. After implementation, the situation is monitored to ensure that the expected results are obtained.
Data, Information Technology, and Marketing Actions
Businesses have the opportunity to collect data from many sources such as usage history on your phone, barcode scanners, and software for tracking the online footprint of consumers. You can imagine how the concept of big data was created, which refers to large amounts of data collected from a variety of sources.
The figure below shows how the data is obtained and stored in the data warehouse of the organization which is accessed by marketing researchers for research studies. Marketers convert the data into useful information by using analytics. Marketers often use sensitivity analysis to answer "What if?" questions. Sensitivity analysis involves hypothetically asking a question and getting an answer from the data set. For example, how a hypothetical change in the advertising budget would affect revenues.
In sensitivity analysis and in typical traditional marketing research, we try to find an answer to a (hypothetical) question using the data. The opposite could be done with data mining.
Data Mining: working with data to extract hidden information without having a specific research question in mind.
Data mining is a search through large databases to identify a meaningful relationship from the data which could be used to form appropriate marketing strategies.
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Example: A large retailer looked at checkout data from their cashiers’ scanners. Their data mining revealed that when men buy diapers in the evening hours, sometimes they buy a six-pack of beer as well. This is a trend whose existence was unknown before. It surfaced as a result of data mining activity. In order to take advantage of such a relationship, they placed diapers and beer close to each other. Moreover, they placed potato chips in between. Their sales of all three items increased as a result of this strategy.