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 There are conservative estimations about the amount of data created by business systems doubled in every three years. So it is not surprising, that the cost of storing and processing these data with traditional methods is boosting, while effectiveness of data processing gets lower and lower. Data mining can solve this problem: transforms the mass of data into meaningful information, discovers hidden patterns with the help of artificial intelligence - much more effectively than traditional methods do.
For example, one Midwest grocery chain used the data mining capacity to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.  Data mining also in customer relationship management applications can contribute significantly to the bottom line. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict which channel and which offer an individual is most likely to respond to — across all potential offers. Finally, in cases where many people will take an action without an offer, uplift modeling can be used to determine which people will have the greatest increase in responding if given an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.
Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than one model to predict which customers will churn, a business could build a separate model for each region and customer type. Then instead of sending an offer to all people that are likely to churn, it may only want to send offers to customers that will likely take to offer. And finally, it may also want to determine which customers are going to be profitable over a window of time and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move to automated data mining. We provide you the possibility not only use structured data (stored in relational or multidimensional databases) as source for data mining activities. We can help you to analyze text, web, mail, voice (eg. recorded phone discussions) and video streams, search for patterns, hidden informations in almost any type of data. Starschema can help you on the following areas:
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