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The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome
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he accuracy and usability of results will depend greatly on
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the quality of assumptions
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predictive models that focus on predicting a single customer behavior
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to classify
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Decision models
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hese models can be used in optimization, maximizing certain outcomes while minimizing others.
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Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) as exploiting hidden relationships in the data can provide a competitive advantage. For an organization that offers multiple products, predictive analytics can help analyze customers' spending, usage and other behavior, leading to efficient cross sales
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The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques
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Regression models are the mainstay of predictive analytics
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k-nearest neighbours
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Geospatial predictive modeling
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RapidMiner
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jmkeuning"Predictive analytics encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events."
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Predictive analytics encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.
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One of the most well known applications is credit scoring,
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A predictive model can help weed out the "bads" and reduce a business's exposure to fraud.
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identify high-risk fraud candidates
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Big data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools. The volume, variety and velocity of big data have introduced challenges across the board for capture, storage, search, sharing, analysis, and visualization
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Regression models are the mainstay of predictive analytics
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interactions between the different variables in consideration
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Multivariate regression (above) is generally used when the response variable is continuous and has an unbounded range.
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Time series models are used for predicting or forecasting the future behavior of variables.
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standard regression techniques cannot be applied to time series data and methodology has been developed to decompose the trend, seasonal and cyclical component of the series
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Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training.
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As more organizations adopt predictive analytics into decision-making processes and integrate it into their operations, they are creating a shift in the market toward business users as the primary consumers of the information.
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Vendors are responding by creating new software that removes the mathematical complexity, provides user-friendly graphic interfaces and/or builds in short cuts that can, for example, recognize the kind of data available and suggest an appropriate predictive model
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23 Sep 13
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Applications
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Technology and big data influences
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Examples of big data sources include web logs, RFID and sensor data, social networks, Internet search indexing, call detail records, military surveillance, and complex data in astronomic, biogeochemical, genomics, and atmospheric sciences
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Neural networks[edit source | editbeta]
Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. They can be applied to problems of prediction, classification or control in a wide spectrum of fields such as finance, cognitive psychology/neuroscience, medicine, engineering, and physics.
Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training in neural networks used by different networks, supervised and unsupervised training, reinforcement learning, with supervised being the most common one.
Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc.
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Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks 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 is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
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Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.
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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. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision.
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Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products.
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Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision and the forecast results of the decision — in order to predict the results of decisions involving many variables.
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analyze current and historical facts to make predictions about future events.
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22 Dec 11
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03 Oct 11
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Predictive analytics encompasses a variety of statistical techniques from modeling, data mining and game theory that analyze current and historical facts to make predictions about future events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities
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Customer retention
With the number of competing services available, businesses need to focus efforts on maintaining continuous consumer satisfaction. In such a competitive scenario, consumer loyalty needs to be rewarded and customer attrition needs to be minimized. Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. At this stage, the chance of changing the customer’s decision is almost impossible. Proper application of predictive analytics can lead to a more proactive retention strategy. By a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate service sometime in the near future. An intervention with lucrative offers can increase the chance of retaining the customer. Silent attrition is the behavior of a customer to slowly but steadily reduce usage and is another problem faced by many companies. Predictive analytics can also predict this behavior accurately and before it occurs, so that the company can take proper actions to increase customer activity
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Direct marketing
When marketing consumer products and services there is the challenge of keeping up with competing products and consumer behavior. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer. The goal of predictive analytics is typically to lower the cost per order or cost per action
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Fraud detection
Fraud is a big problem for many businesses and can be of various types. Inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance claims are some examples of this problem. These problems plague firms all across the spectrum and some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers. A predictive model can help weed out the “bads” and reduce a business's exposure to fraud.
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Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns
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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. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision.
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CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few.
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They must analyze and understand the products in demand or have the potential for high demand, predict customer's buying habits in order to promote relevant products at multiple touch points, and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones.
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Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) and exploiting hidden relationships in the data can provide a competitive advantage to the organization. For an organization that offers multiple products, an analysis of existing customer behavior can lead to efficient cross sell of products. This directly leads to higher profitability per customer and strengthening of the customer relationship. Predictive analytics can help analyze customers’ spending, usage and other behavior, and help cross-sell the right product at the right time
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For example a retailer might be interested in predicting store level demand for inventory management purposes
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Dante-Gabryell MonsonPredictive analytics encompasses a variety of statistical techniques from modeling, data mining and game theory that analyze current and historical facts to make predictions about future events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks 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.analytics statistics datamining research predictive automenta projects
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Hall900 Hall900Predictive analytics
pattern algoritmos datamining machine_learning reference delicious
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elliot harrison robertsPredictive analytics encompasses a variety of techniques from statistics and data mining that process current and historical data in order to make “predictions” about future events
reference Wikipedia statistics research competitors analysis
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