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Trading Places International, RCI, Interval International or San Francisco Exchange Company). Ever since the beginning of Business Intelligence, people have been creating reports on how their company is doing.
Reporting on data from the past means there is nothing that you can change about it anymore. The graph below depicts the competitive advantage a company can gain as its business intelligence and analytics mature. By the use of past information, models are derived and analysis are performed to project future outcomes. Some time ago, Wal-Mart decided to combine the data from its loyalty card system with that from its point of sale systems. On Friday afternoons, young American males who buy diapers also have a predisposition to buy beer. From these reports all kinds of information can be read, from stock to quality assurance, and from financial facts to production line specifics. Basically, predictive analytics is a variety of techniques such as statistics, modeling, machine learning and data mining.
This allows us to learn from past mistakes and successes and lets us understand what to change and what to keep.

The former provided Wal-Mart with demographic data about its customers, the latter told it where, when and what those customers bought. No one had predicted that result, so no one would ever have even asked the question in the first place. He knows there is no way that he is going to get out of the house to join his mates at the bar. For example on Amazon, when you search for, let’s say, an IPhone 5, you get the ‘frequently bought together’ and ‘customers who bought this item also bought’ list.
This can be done with a couple of methods, such as ARIMA (Auto Regressive Integrated Moving Average). This would give them a big advantage over their competitors and give their company the edge it needs to be successful. Hence, this is an excellent example of the difference between data mining and querying, as you will be able to read later on.
This is a very important question to ask yourself when you are about to make a decision based upon those reports. It could start using the information it has from its customers’ cards, and run them through an algorithm.
When you understand this, you are better equipped to steer your company in the right direction.

After seeing the results of the data mining, Wal-Mart moved the beer next to the diapers and beer sales went up.
If it is smaller than 265 minutes, we want to know whether the number of customer service calls is smaller than 3.500.
After this, a couple of classes appear such as Leading edge shopper, seasonal shopper, big brand shopper, value conscious shopper, etc.
In the case of product A, it would seem that revenue is very likely to rise, all be it slightly, while on the graph of product C we see that it is most likely to decline. If it is, and they do not have an international plan, it would seem that they are 95% certain to churn. Now the store knows which customer belongs in which class, they can focus different kinds of adds to these customers, making them more likely to visit and purchase something.

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