How Do I Choose the Best Quantitative Analysis Model?
Choosing the best quantitative analysis model depends on in which field you are working and which questions you would like to ask. Financial professionals who are interested in analyzing performances of financial instruments, such as stocks and bonds, might use a technical quantitative analysis model, in which they draw conclusions based on instruments' past behaviors. Market researchers, on the other hand, might use models in which they can assign numerical values to different variables in a study, such as target demographics, regions, and prices of products and services. People who need a quantitative analysis model in a computer science context might choose one that allows them to monitor production levels of automated systems in real-time.
Knowing which questions you want answered or which outcomes you would like to reach is important when it comes to choosing the best quantitative analysis model. People who are interested in monitoring the results of systems should use a results management quantitative analysis model. With this kind of model, users define specific factors and players in a system and can visualize how they affect results. These people might use models to continually improve systems.
Outcome logic drawing often works well for making strategic decisions. With this kind of analysis model, individuals use methods such as drawing chains of results to see how certain incomes indirectly affect certain factors. For example, a person who is using a model to determine strategies for opening a store in a new geographical market might analyze how this expansion impacts cash flow in other areas of a large organization. People use outcome models to manage risk when developing strategy, determine which casualties to expect after implementing a new strategy, and to ensure that predicted outcomes are logical.
Individuals searching for a model that helps them to determine if there is a need for a certain product or practice might benefit from needs based assessments, where they can interview relevant parties. For example, before an organization introduces a new product into a market, marketing professionals might interview a significant sample of target demographics then assign numerical values to ratings or responses, as well as to different groups within demographics. Needs based models work well for any people who have a product or service to sell, but who are unsure as to if and where a product is in demand.
Before settling on a quantitative analysis model, it is important that you consult all individuals who have some stake in your analysis. It might be helpful to divide these people into two groups that you consult separately. One group might include all people who contribute to an actual analysis. The other group might include people who can benefit from an analysis.
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