(:title APMonitor Applications:) (:keywords nonlinear, model, library, examples, APMonitor:) (:description Nonlinear Model Library - APMonitor:)
Predictive analytics adds great value to a businesses decision making capabilities by allowing it to formulate smart policies on the basis of predictions of future outcomes. A broad range of tools and techniques are available for this type of analysis and their selection is determined by the analytical maturity of the firm as well as the specific requirements of the problem being solved.
Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years.
Advanced Process Control
APC applications adjust manipulated variables to maintain target levels of controlled variables. Improving the accuracy of infrequent measurements, noisy measurements, or inferred process states allows the APC application to better meet the defined objective. APMonitor provides optimized information to the APC application.
Product marketing is constantly faced with the challenge of coping with the increasing number of competing products, different consumer preferences and the variety of methods (channels) available to interact with each consumer. Efficient marketing is a process of understanding the amount of variability and tailoring the marketing strategy for greater profitability. Predictive analytics can help identify consumers with a higher likelihood of responding to a particular marketing offer. Models can be built using data from consumers’ past purchasing history and past response rates for each channel. Additional information about the consumers demographic, geographic and other characteristics can be used to make more accurate predictions. Targeting only these consumers can lead to substantial increase in response rate which can lead to a significant reduction in cost per acquisition. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of products and marketing channels that should be used to target a given consumer.
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.
With the amount 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.
Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk. For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver. A financial company needs to assess a borrower’s potential and ability to pay before granting a loan. For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future. Predictive analytics can help underwriting of these quantities by predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the process of customer acquisition, by predicting the future risk behavior of a customer using application level data. Proper predictive analytics can lead to proper pricing decisions, which can help mitigate future risk of default.
Every portfolio has a set of delinquent customers who do not make their payments on time. The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs.
Fraud is a big problem for many businesses and can be of various types. Inaccurate credit applications, fraudulent transactions, 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. This is an area where a predictive model is often used to help weed out the “bads” and reduce exposure to fraud.
Portfolio, product or economy level prediction
Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example a retailer might be interested in predicting store level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year.
Differential algebraic equation models of the blood-glucose response have enabled researchers to better tailor medical treatments for insulin dependent patients. Infrequently measured blood glucose levels are combined with information about recent food consumption to gain a real-time estimate of the blood glucose level.
Chemical and Refining Facilities
Modern chemical plants are well instrumented to provide alarms, operators, advanced control applications with real-time information about process conditions. The operators or applications adjust manipulated variables to maintain product quality specifications, reject disturbances, and avoid major process upsets. The measurements provide insight into a process that is constantly changing due to unmeasured disturbances, product grade transitions, or process upsets. In these circumstances, the person or application that is controlling the process needs accurate information about key process variables. APMonitor is an optimization-based application to estimate the current state of the plant. This information is presented to operators or applications to help them gleen the most from available measurement information.