Thursday, June 30, 2011

Data Mining applications accross the industries


I am often asked the question about what are the most common applications of analytics in a specific industry. Even though each industry has some application of analytics and data mining that are specific to them, they also have cross-industry applications that are common to many industries. Example of industry-specific analytical application is “policy-lapse prediction” in the insurance industry. Examples of cross-industry applications could be customer segmentation or customer retention, since in any industry where there are customers there is also need to segment them and retain them. Following is a mix of analytical applications and can be done in a specific industry:    

Banking (retail): Analytics can help banks understand and drive decisions related to customer profitability, as well as to enable banking institutions to segment customers according to a multitude of variables: demographics, account history, etc. – in order to create more meaningful and targeted marketing programs. Furthermore, analytics can help banks improve retention rates by determining its causes and predicting future customer attrition. In addition, banks can apply analytics to historical data to find out which customers are good candidates for cross-selling and up-selling and as a result achieve increase in revenue and wallet share. For most banks analytics are used as the most powerful weapon in the fight against fraud.

Banking (investment): In investment banking analytics can be of tremendous value in supporting cross-asset trading and various other trading strategies. Also, analytical technologies are invaluable for enterprise-wide, market and credit risk management. Other applications of an analytics are segmenting and predicting the behavior of homogeneous groups of customers, uncovering hidden correlations between different indicators, create models to price futures, options, and stocks, and optimize portfolio performance.

Insurance (short term): Analytical applications in short term insurance are in rate-making by identifying risk factors that predict profits, claims and losses as well as in identifying potentially fraudulent claims. Common applications of analytics are in segmenting and profiling customers and then doing a rate and claim analysis of a single segment for different product, as well as performing market basket analysis and sequencing that answers the question of what insurance products are purchased together or in succession. Other common applications are in reinsurance, and in estimating outstanding claims provision (severity of the claim, exposure, frequency, time before settlement, etc.), as well as in using analytics to separate claims between digital and mobile assessors.

Insurance (life):  A common application of analytics in life insurance is around policy lapse predictions, modeling brokers’ performance, reactivating of dormant customers to estimating the buying potential, and realizing the untapped potential through using analytics for more effective cross-selling. In addition analytics are commonly used to model response in direct marketing of specific insurance products.

Telco's: Analytics in telecoms are used for churn management, network fault prediction, up-selling and cross-selling, capacity planning personalized advertising and subscriber profiling.

Retail: Analytics in retail are being used for supply chain and demand planning, customer segmentation and profiling, for improving response in direct marketing, for better cross-selling and up-selling, for product management, and for better understanding which products are purchased together or in sequence.

Industrials: Analytics among Industrials are being used for warranty analysis, quality control, process optimization, waste management, supplier segmentation, product and customer profitability, causal analysis, service parts optimization, and for supply chain optimization and demand planning.

Resources: The use of analytics in exploitation of natural resources is to better understand the operational risks associated with situations like equipment failures, human error and security breaches. Analytics can also be used to analyze usage patterns, weather, econometric data, changing demographics, etc. in order to accurately and confidently predict energy purchase/supply requirements.

Oil and Gas (upstream): Analytics in Oil and Gas are used for exploration and production optimization, facility integrity and reliability (predicting shut-downs, outages and downtime in production), reservoir modeling and oil-field production forecasting, estimating the shape of an oil field, fluid flood optimization and permeability prediction. It is also used for optimization of the reliability of equipment. Other applications of analytics include managing oil field assets by identifying trends in asset performance and potential, estimate the potential for infill drilling locations, screening and prioritizing workover candidates, and discover the characteristics of high potential producing assets and identify opportunities for acquisitions.

Oil and Gas (downstream): Common analytical applications are in demand forecasting, prediction of outages (planned, unplanned), grid overloads as well as predictive asset maintenance and fault prediction. Other applications are workforce optimization and consumer analytics.

Healthcare: Analytics in healthcare are being used for medical claims analysis (segmentation of claims (normal claims, claims for case managers, claims for investigative units), outcome analysis, both clinical and financial (mortality, length of stay, etc.), for disease management, for medical errors, as well as for the patient, supplier relationship management (increased patient satisfaction levels, segment suppliers and providers of cost, efficiency and quality of service).

Goods: Analytics among goods manufacturers are being used for quality control, process optimization, waste management, for inventory optimization and demand planning.

Public: Analytics in the public sector are used for improving of improving service delivery and performance of government agencies, improving safety, minimizing of tax evasion, detecting fraud, waste and abuse, analyzing scientific and research information, managing human resources, optimizing resources, and analyzing intelligence information.

Goran Dragosavac

2 comments:

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