Usage of Data Analytics
Broadly, predictive analytics can be used to:
1. Description: Provide an overview and summary of the existing state of the world. For example: what is the average age of our customers?How much do they spend, on average, each time they buy? What is the distribution of amounts spent? etc.
2. Comparison: is group A different in some meaningful way from group B, and if so, in what way and by how much? Examples: Do men spend more than women? Does one advertisement work better than others?
3. Clustering / Grouping / Co-occurrence: Group together things that are “similar” according to some definition of “similar”. Example: Are there groups of customers with similar buying/purchase habits? If you know some marketing, cluster analysis is what is used to divide customers into “segments”.
4. Classification: assign a probability that something belongs to 1 of several mutually exclusive classes. Example: Is this credit card trans-action fraudulent? (A: probability Yes/No) Will this person donate to my charity? (A: probability Yes/No) Is this person suffering from a heart attack, or some other mimic condition? (A: probability of Attack)
5. Prediction: predict the most likely value of a continuous variable.Example: what will sales be next quarter? How much will this group of customers spend over the next year? What will be the market share of our new product?
Broadly, predictive analytics can be used to:
1. Description: Provide an overview and summary of the existing state of the world. For example: what is the average age of our customers?How much do they spend, on average, each time they buy? What is the distribution of amounts spent? etc.
2. Comparison: is group A different in some meaningful way from group B, and if so, in what way and by how much? Examples: Do men spend more than women? Does one advertisement work better than others?
3. Clustering / Grouping / Co-occurrence: Group together things that are “similar” according to some definition of “similar”. Example: Are there groups of customers with similar buying/purchase habits? If you know some marketing, cluster analysis is what is used to divide customers into “segments”.
4. Classification: assign a probability that something belongs to 1 of several mutually exclusive classes. Example: Is this credit card trans-action fraudulent? (A: probability Yes/No) Will this person donate to my charity? (A: probability Yes/No) Is this person suffering from a heart attack, or some other mimic condition? (A: probability of Attack)
5. Prediction: predict the most likely value of a continuous variable.Example: what will sales be next quarter? How much will this group of customers spend over the next year? What will be the market share of our new product?
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