Predictive Data Mining combines database analysis with multivariate statistics and artificial intelligence.  In recent years, predictive data mining has become an essential tool for strategic decision making among mid-size to large corporations.  It has been proven effective in predicting future customer behavior, classifying customer segments and forecasting events. 

Right now, there are huge databases and powerful technologies working together to crunch numbers about your lifestyle and lifestyles of millions of other Americans.  They know the value of your home, the type of car you drive, the ages of your children, your credit rating and more.    This data is being mathematically processed to determine if you are the best target for the latest gadget to hit the market.

While this sounds like something from a George Orwell novel, it describes the predictive modeling power behind currently available modern data mining technology.  While data mining conducted at this magnitude is limited to certain government agencies, the price of this technology has dropped substantially due to new mathematical discoveries, lower technology costs and improved processing power.

As a result, many corporations are embracing predictive data mining to segment customers, predict customer behavior and make future projections based on historical data.  Here are a few ways that predictive data mining is being used today.

Marketing Predictions
Producing accurate forecasts is an important part of measuring your marketing strategy.  Inaccurate forecasts lead to increased inventory costs, under or over production, missed targets, improperly allocated resources and many other problems.  While tools like Microsoft Excel provide some forecasting tools, the accuracy of these tools are significantly reduced when non-linear relationships or missing data are present, which is often the case when analyzing marketing data.  In many cases, neural networks can provide superior forecasting accuracy.

Market Segmentation

When neural networks are setup appropriately, they can accurately identify people who will be most receptive to a product, promotion or advertising campaign.  Some of the most frequent methods of segmentation with neural networks combine metrics such as recency of purchase, frequency of purchases and amount spent.  Other factors include age, sex, income, location, education level, occupation and household status.  Today, neural networks are a primary method for highly predictive marketing segmentation.

Prediction and Classification

Neural networks are a proven technology for solving complex classification problems.   Credit companies often deploy neural networks to spot fraudulent credit card activity and identity theft.  Other companies deploy neural networks to identify defecting customers in order to maximize their customer retention.

The Marketing Analysts are experienced in deploying neural networks to discover marketing opportunities, segment customers and enable you to discover more complex relationships in your data. With our technology, we can develop more accurate and effective predictive models for better decision-making. Contact us today for more information.