Week 1a Maker Of Energy Drinks Is Considering Abandon 092090

Week 1a Maker Of Energy Drinks Is Considering Abandoning Can Container

A maker of energy drinks is considering abandoning can containers and going exclusively to bottles because the sales manager believes customers prefer drinking from bottles. However, the vice president in charge of marketing is not convinced the sales manager is correct. Investigate this issue using statistical analysis. 1. Explain which data collection method you would use and what procedures you would follow to apply this method to this situation. 2. Propose which level of data measurement applies to the data collected. Justify your answer. 3. Determine whether the data is qualitative or quantitative.

Paper For Above instruction

The decision to transition from cans to bottles for energy drinks hinges significantly on customer preferences, making data-driven insights essential. Implementing an effective data collection strategy is crucial in understanding consumer preferences comprehensively. Surveys and questionnaires emerge as the most suitable data collection method in this context due to their capacity to gather direct consumer opinions efficiently and cost-effectively. These tools can be distributed through multiple channels, including online platforms, retail outlets, and sampling events, to reach a broad and representative sample of the target market.

To apply this method, the company should first define the specific questions that will illuminate customer preferences, such as whether consumers favor bottles or cans, their reasons for preferences, and contextual factors influencing their choices. A well-designed questionnaire should include both closed-ended questions for quantitative data—such as rating scales or multiple-choice questions—and open-ended questions to explore qualitative insights. After developing the instrument, a sampling strategy must be adopted to ensure representativeness, possibly using stratified random sampling to include different demographic segments like age, gender, and geographical location.

When deploying the survey, the company should ensure confidentiality to elicit honest responses, and randomize the order of questions to minimize bias. Collecting data from a sufficiently large sample—say, at least 200 respondents—will enhance the reliability of the findings. Once data is gathered, statistical analysis can compare preferences across demographic groups, identify significant patterns, and estimate the proportion of customers favoring bottles over cans. Such insights will enable informed decisions aligning with actual consumer behavior rather than assumptions.

The level of data measurement applicable to the collected data is primarily nominal and ordinal. Nominal measurement pertains to categorical data such as preference categories (bottle or can), which lack intrinsic order but classify respondents. Ordinal data may emerge from scaled questions—such as rating how much they prefer one container over the other—reflecting an ordered degree of preference. Justifying this, customer preference categories are inherently nominal, but when responses involve ranking or scaled ratings, the measurement shifts to ordinal. This categorization allows for appropriate statistical tests, such as chi-square for nominal data or Mann-Whitney for ordinal data, ensuring rigorous analysis of consumer preferences.

In terms of data type, the collected data is predominantly qualitative because it captures consumers’ preferences, opinions, and reasons, which are inherently descriptive. While some quantitative data may be derived from scaled responses—such as satisfaction ratings—the primary focus remains on understanding attitudes rather than precise numerical measures. Qualitative data offers rich insights into consumer motivations, which are essential when evaluating trends and making strategic decisions about packaging formats.

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