New York — Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to anticipate future trends and behavior patterns based on a variety of techniques from statistics and data mining. The core lies in capturing relationships between explanatory variables and the predicted variables from the past occurrences and exploiting this information to predict future outcomes. In business, predictive models exploit patterns found in historical and transactional data to identify risk and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. Predictive analytics are also applied in insurance, telecommunications, retail, travel, health care, pharmaceuticals and other industries. How It Works Unlike the standard business reporting and sales forecasting methods, predictive analytics offers actionable projections for each customer. This special form of business modeling foresees each customer purchase, response or cancellation, predicting the individual behavior of existing or prospective customers under certain conditions. Naturally, per-customer predictions are a key to allocating marketing and sales resources. For example, by anticipating which product features each customer will respond to, you can appropriately target the right segment of customers. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to foresee future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender and driving record when issuing car insurance policies. Multiple predictors are combined into a predictive model which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, projections are made and the model is validated (or revised) as additional data becomes available. Predictive Models in Practice The real trick is to find the best predictive model. There are many kinds of models, such as linear formulas and business rules. And, for each kind, there are all the weights, rules or other mechanics that determine precisely how the predictors are combined. Data mining is a component of predictive analytics that entails analysis of data to identify trends, patterns or relationships among the data. This information can then be used to develop a predictive model. Predictive analytics, most predictive models and most data mining techniques rely on sophisticated statistical methods, including multivariate analysis techniques such as advanced regression or time series models. There are three steps where business expertise is needed to direct predictive analytics:
After the accuracy of the mining model was tested and showed that models were satisfactory, the model could be used to predict the likely lapses in the existing or new data sets. The results identify potential customers by indicating whether the customers’ policies are lapsed and the probability of the prediction being correct. Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. Predictive analytics also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as churn prediction, fraud detection and propensity to buy additional products and services. Predictive models often perform calculations during live transactions in order to guide a decision. However, the use of predictive analytics requires specialists who understand both the specific mathematical techniques and the business problem to be able to apply the appropriate techniques.
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