Final answer:
To investigate customer satisfaction in public transit, a hypothesis such as the link between satisfaction, wait times, and cleanliness is formulated. The marketing research problem is broken down to understand satisfaction factors, needing customer feedback and observational data. Various research designs, including surveys and focus groups, are considered to balance the depth and breadth of data collection.
Step-by-step explanation:
Formulate Hypothesis and Define Marketing Research Problem
To begin addressing customer satisfaction in public transit, we first formulate a hypothesis, which is essentially a prediction made on the basis of some evidence prompting further investigation. An example hypothesis could be, 'Customers are more satisfied with public transit when wait times are reduced and the interior of the transit vehicles is clean.'
Developing an Approach to the Problem
The marketing research problem involves understanding what factors contribute to customer satisfaction in public transit. This involves breaking down the problem into smaller, more manageable parts, such as service frequency, seating availability, and cleanliness. To address the marketing problem, we focus on identifying aspects of the service that can be improved to enhance customer satisfaction. The analytical framework here could utilize data regarding current customer satisfaction levels and factors influencing those levels. The information needed might include customer feedback surveys, complaint records, and observational studies.
Formulate Research Design
For the research design, quantitative methodologies such as surveys and qualitative approaches like focus groups or depth interviews can be considered. Surveys are well-suited for collecting data on a range of subjects including frequency of transit use, importance of service attributes, and overall satisfaction. However, they may have drawbacks such as limited depth of response or potential biases in sample selection. A focus group, on the other hand, could provide richer, more nuanced data about customer experiences but might not be as generalizable to the entire customer population.