The data being used can be gathered using point-of-sale systems, mobile devices, cameras, microphones, internet searches, and online tracking technologies. One example is the detailed transactions that are saved by retailers such as Wal-Mart and Target. Another example is "likes" and "shares" on Facebook. Even your searches in Google are saved. Statistics, modeling, and data mining are only some of the tools being used to analyze the huge amount of data that people in the US give off every single day. Targeting customers is the primary objective of this analysis.
Target is very popular targeting customers, especially new moms. A story about this was discussed in an earlier blog post. Another application is dynamic pricing. Prices can be changed based on a algorithm that estimates customers' willingness to pay. This is especially useful and accurate to online retailers who have more personal information, purchasing information, and how many times a person has looked at a product. This gives retailers a much better idea of how much customers are willing to pay for certain items.
Some examples of patterns and correlations discovered by dig data are:
- Facebook "likes" revealing political and religious views, drug use, marital status, and sexual orientation
- Blue Cross/Blue Shield buys shopping data.
- If a person buys plus-size clothing, the plan could flag them for potential obesity and then even higher healthcare costs
- President Obama's 2012 campaign used datasets to identify Republican- leaning voters who might be persuaded by specific issues.
What makes all of this so interesting is that there aren't very many regulations so who knows where this could go if the government and businesses continue to be unregulated in what they can and cannot collect.
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