Decision tree is a most important part in Decision Analysis. Please refer to this site to see what is the Decision Tree Analysis and how does it help a business to analyze data? Then give a real world example showing how to use decision tree for more intelligent Decision Analysis?
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Decision Trees are useful in many aspects of businesses because it gives that business an outlook of what they can expect. One of them that I think is useful is to use when you are hiring a new employee and you are trying to figure out what their salary should start. It would help you look at the pros and cons of what the applicant has to offer.
For instance, you would have the salary at the top. This would drop down to three categories of knowledge, experience, and education. Below knowledge you would have knowledge that applies to the specific job. Below experience you would have experience in the field for the job role. Below education you would have different degree options or high school education. These could all be factors for this person’s salary start range. It allows the company to pay what is fair and could save them money in the end.
A decision strategy “involves a sequence of decisions and chance outcomes to provide the optimal solutions to a decision problem” (Anderson, et al., 2019). It is a strategy that depends on unknow results of certain events. In this weeks’ studies I’ve learned to “first, draw a decision tree consisting of decision and chance nodes and branches that describe the sequential nature of the problem. Determine the probabilities for all chance outcomes.
Then, by working backward through the tree, compute expected values at all chance nodes and select the best decision branch at all decision nodes. The sequence of optimal decision branches determines the optimal decision strategy for the problem” (Anderson, et al., 2019). For a business that is interested in analyzing its customer base it will likely employ a data scientist. A data scientist is able target a customers’ spending habits to influence or predict what their next purchase will be.
For instance, often times when a customer searches for an item on their cell phones or computers they will start receiving targeted advertisements within the market they were searching for. This targeted marketing is most valuable to companies when they are trying to figure out a consumers’ behavior to surmise whether they will purchase an item or not.
“Based on customer attributes and past online shopping behavioral data, an online retail giant wants to predict the future purchases of customers. Here predictors can be ‘days from last purchase’, ‘brand preference’, ‘income’, ‘age’, ‘gender’, ‘website visits’, ‘location’, ‘total amount of purchase so far’ etc. As the target variable is numeric, namely the purchase amount, the regression tree can be used to predict the purchase amount by different types of customer segments” (Smarten, 2018). Therefore, before a company invests money into advertising, they will have a data scientist evaluate their target markets.
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