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Predictive Analytics: Extending BI Infrastructure
Insurance Networking News, December 16, 2008
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:
- Defining your prediction goal
- Evaluating the prediction results and redirecting
- Deploying your prediction model
Across Industries
Increasingly, organizations in virtually every industry around the world are realizing the benefits of using data to align their current actions with their future objectives. By incorporating predictive analytics into their operations, companies have gained control over the decisions they make every day so that they can successfully meet their business goals. Predictive analytics is increasingly used in retail, telcom, insurance, financial services and the pharmaceutical industry. The most successful and rewarding predictive analytics processes:
- Enhance customer retention and loyalty
- Identify cross-sell and up-sell opportunities
- Identify customer lifetime value
- Include fraud detection
- Underwriting
- Portfolio analysis
- Direct marketing
- Telecommunications
With the increasing importance of customer retention, loyalty and churn in the context of customer lifetime value, companies are keenly following customer churn and the scale of efforts required for an appropriate retention campaign. In the diversity of consumer business today, the mass marketing approach cannot succeed; it is necessary to undertake customer value analysis along with customer churn predictions to make marketing programs target more specific groups of customers.
Predictive modeling helps identify subsegments of the customer base that are likely to churn and provides a well-identified segment to target with retention programs. Accurate predictive models can:
- Predict churn propensity into a subscriber base
- Identify churn’s key indicators
- Effectively target subscribers with proactive retention campaigns prior to churn
Another area of predictive analytics in telco is to identify prospective customers for effective cross-sell and up-sell opportunities, thus uncovering a profile of the customers who purchase numerous products or upgrades.
Study of usage patterns, payment profiles and demographic credentials are among other factors to be considered for selling additional products and services. The key benefits of applying predictive analytics for cross-sell and up-sell marketing strategies are:
- An identified set of customers with high probability of buying specific products as a key target for cross-sell and up-sell campaigns
- Increased average revenue per user by offering best-fit products to customer
- Crucial information on ideal product/services bundles for each customer profile
Successful insurers credit predictive modeling for improving identification and segmentation of insurance risks, leading to better underwriting, pricing and marketing decisions and smarter management of the insurance business.
What can predictive modeling do for the insurance industry? It can help insurers improve their rating plans by identifying mispriced risks. By analyzing distributional relationships in insurance databases in a multivariate framework, predictive modeling identifies assumptions that give misleading results. For example, when considering the relationship between insurance losses and age, and between insurance losses and prior accidents, younger drivers and those with prior accidents respectively cost more to insure.
Factors influencing customer attrition is another area where predictive analytics is used to estimate and devise appropriate retention strategies for a high value customer likely to lapse.
Banking
Predictive analytics in the banking industry is a dimension of BI that allows the assessment of risk and opportunities. In retail banking, this process translates into questions such as: Which customers are likely to default on loans? Which are likely to be profitable, long-term customers?
Getting the right answers is important because it has a direct effect on the bottom line. Banks need to anticipate and satisfy the changing needs of the customers with a wide range of products, such as credit cards, mortgages, home equities, lines of credit, savings and checking accounts, insurance and investment products.
In addition, financial institutions require capabilities for risk management and regulatory compliance like Basel II and mandatory capital requirements. Here, banks demand best practices for decision-making in all areas of operations, including:
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