Predictive analytics is the science that makes decisions smarter. It turns today's data into forecasts of future events, and puts that insight to use by directly guiding individual decisions.
Making the right business decision, especially when it involves customer behavior, requires the ability to navigate overwhelming complexity. When considering hundreds or even thousands of factors, and a universe of thousands or millions of customers, people just can't "connect the dots" to make the ideal decision. Predictive analytics connects the dots scientifically, guiding each decision to greater success.
Predictive analytics is a broad discipline covering many analytic technologies. Here are three of the main ways predictive analytics works to improve decisions:
Predictive models analyze past performance to "predict" how likely a customer is to exhibit a specific behavior in the future-for example, likelihood of a consumer to attrite or churn. This category also encompasses models that "detect" subtle data patterns to answer questions about customer behavior, such as fraud detection models.
Predictive models are often embedded in operational processes and activated during live transactions. The models analyze historical and transactional data to isolate patterns: what a fraudulent transaction looks like, what a risky customer looks like, what characterizes a customer likely to switch providers. These analyses weigh the relationship between hundreds of data elements to isolate each customer's risk or potential, which guides the action on that customer.
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Unlike predictive models that predict a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. Descriptive models "describe" relationships in data in a way that is often used to classify customers or prospects into groups.
For example, a descriptive model may categorize customers into various groups with different buying patterns. This may be useful in applying marketing strategies or determining price sensitivity.
Decision models predict the outcomes of complex decisions in much the same way predictive models predict customer behavior. By mapping the relationships between all the elements of a decision-the known data, the decision and the forecast results of the decision-decision models predict what will happen if a given action is taken.
Before you roll out a new offer or strategy, decision modeling also allows you to "simulate" changes to volume, response and risk-for example, "What would happen if I lower my pricing by 5%? By 10%?" You can run hundreds of these simulations within a short period, exploring many more possibilities than would be practical with live testing.
Optimization combined with decision modeling helps produce decision strategies that determine which actions to take on every customer or transaction, in order to mathematically optimize results and meet defined constraints.
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Precision
Predictive analytics replaces guesswork with science. Balancing the importance of dozens or hundreds of variables gives you more relevant and accurate insight into your customers and how your actions will impact your success.
Consistency
Predictive analytics operates consistently and dependably, relying on mathematical technique. Consistent, unbiased decisions meet the fairness test better than decisions based on human subjectivity, and give you the control you need over decisions and results.
Agility
Businesses using analytics can take decisions more confidently, and quickly assess the impact of a new strategy. They can respond more quickly to market conditions, improve customer service and profitably grow into new markets.
Speed
Analytics answers complex questions and processes transactions with empirical precision and at incredible speeds, often during "live" transactions. Decisions that used to take hours or days can be reduced to minutes or milliseconds.
Cost
With analytic insight, businesses can more accurately measure business risks and reduce losses. Models can also instantly perform analyses much faster than individuals, reducing the time your staff spends on routine decisions.
TelecommunicationsCollections, churn management, revenue assurance, network assurance, fraud detection
InsuranceMarketing, underwriting, bill review, fraud detection
HealthcareFraud, abuse and error detection, underwriting, marketing, collections and recovery
Mortgage lendingAutomated underwriting, loan qualification answers, identity theft detection, portfolio retention, marketing, best execution, collections and loss mitigation
Consumer lendingAccount origination, loan qualification answers, identity theft detection, retention, account management, marketing, online loan qualification, collections and loss mitigation
Small business lendingCredit evaluation and renewal, loan qualification answers, marketing, customer management, fraud detection, collections and loss mitigation
Credit and debit cardsOriginations, credit line management, identity theft detection, fraud referral, collections, churn management, cross-marketing
RetailCustomer credit management, marketing
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