Data Mining

Data Mining and Forecasting Services

Using data mining, companies and organizations can increase the profitability of their businesses by uncovering opportunities and detecting potential risks.

Our data mining and analysis consulting services can help you to extract valuable information out of your data by utilizing forecasting modeling (regression and time series analysis). We can analyze your data and provide you with forecasting reports that will suite your need.

One Midwest grocery chain used data mining method 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.

There are four types of relationships in marketing data:

• Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
  
  
• Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
  
  
• Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
  
  
• Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

Forecasting is a component of data mining. It is the process of estimation in unknown situationsand is commonly used in discussion of time-series data. Regression models can be best used on time series data to detect trend and seasonality (even though the models are also useful for cross section data). They can help answer questions such as "What will our sales in the next quarter be?" and "How confident are we in the prediction?" Regression models are also very good for interpolating and extrapolating data in both linear and nonlinear approaches. Our Excel consulting services can provide you with forecast reports by testing your data through various models and implement the best model after it is found.
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Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales,

customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
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One of the key issues raised by data mining technology is data integrity. Clearly, data analysis can only be as good as the data that is being analyzed. A key implementation challenge is integrating conflicting or redundant data from different sources. For example, a bank may maintain credit cards accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Data cleansing processes must translate data from one system to another and select the address most recently entered.
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