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

Applications of Analytics and Data Mining in Telecommunications

The telecommunications industry was an early adopter of data mining technology and therefore many data mining applications exist.  Telco’s generate a tremendous amount of data, such as call detail data, which describes the calls across the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. Such rich data is a fertile environment for many data mining applications built with the purpose of reducing some of the most pressing business problems in telecommunications.

In general , the telecommunication industry is interested in answering some strategic questions using data mining applications such as :

-       which customer group is highly profitable, which one is not?

-       to which customers should we advertise what kind of special offers?

-       which customers are most likely to churn?

-       how do customer profiles change over time?

-       fraud detection and prediction ( for example stolen mobile phones or phone cards )

-       how does one retain customers and keep them loyal as competitors offer special offers and reduced rates?

 -      how does one predict whether customers will buy additional products and services like cellular services,

 call waiting or basic services?

-       what characteristics differentiate our products from those toour competitors?

-       when is a high-risk investment, such as new fiber optic lines ,acceptable?

-       what kind of call rates would increase profit without losing good customers?

Overview of the most common app's of data mining in telco's in more detail: 

////Sources: web, GDDM library ////

Network Fault Prediction

Network shut-downs for prolonged periods of time and more often can mean two things – loss of revenue and loss of customers. Here, predictive modeling can be used to generate alert just before shut-down so that immediate preventative actions can be taken. Model is built on historical instances of previous shut-downs and state of the network prior to shut-down. Such model is then applied in future time periods being able to recognize times before network failures and generating alerts.

Capacity Planning

Capital expenses contribute significantly to the overall cost of running a network. Operators invest in network capacity to address scalability and future growth. Since this growth can be unpredictable, operators typically over-provision their networks—leading to significant amounts of unutilized capacity that cannot be immediately monetized. Data mining and correlation techniques applied successfully on network data help the operator identify heavily utilized parts of the network at different points in time. This helps the operator to make key decisions related to adding capacity at the right location at the appropriate time. This analytics-assisted capacity planning, combined effectively with dynamic traffic routing, helps operators to optimize network resources—leading to overall cost reductions.

Subscriber Data Analysis and Profiling

Operators have access to large amounts of data about a subscriber, based on their usage of the operators’ services. Analysis of calling patterns, billing data and support requests, when combined with subscriber’s personal information such as demographics, age, gender, home address and income, forms the basis for creating a profile of the subscriber. For mobile and wireless services, current location and changes to the location provide additional context for the subscriber’s profile. The subscriber profile becomes the basis for other innovative services.

Social Network Modeling and Analysis

By leveraging calling patterns and other data points from a subscriber’s profile, operators can build a social networking model for the subscriber that identifies connections and proximities between different subscribers. The social network model deduces these proximities through data analytical techniques and is periodically validated and reinforced through automated and manual actions.

Personalized Advertising

Given the lower ARPU and competitive environment, operators are exploring alternate sources of revenue. Advertisement-based revenue is one such popular source. Randomized advertisements, being intrusive and interruptive, can adversely affect the subscriber’s satisfaction with the operator. On the other hand, personalized advertising that caters to the likes and needs of the individual can enhance loyalty. These advertisements, when combined with context-specific information such as location, can significantly improve the “hit-rate.” Further, advertisers are amenable to paying premium rates for personalized advertising to the targeted audience, resulting in increased revenues for the operator.

Up-Selling and Innovative Tariffs

The 80-20 principle holds true for most operators—wherein 80% of the revenue comes from 20% of the high net-worth subscribers. The analysis of service usage and billing can help the operator identify the top 20% of subscribers and focus their attention on improving loyalty by ensuring high subscriber satisfaction. Specifically, tariffs can be personalized to provide the best value for the subscribers’ money without reducing operators’ ARPU—a win-win situation. Further, this analysis also provides an opportunity to up-sell additional services (preferably personalized) based on subscribers’ profiles.

Churn Management

Competition among operators (especially mobile operators) lends itself to increased subscriber churn because subscribers have multiple options to select from. This is further exacerbated by mobile number portability, reducing the barrier for churn. To retain their subscriber base, it is important for operators to proactively identify subscribers who are likely to churn and incentivize them to stay. Many techniques, including social network modeling, can be used to identify the subscribers who are most likely to switch out. The churn management solution is integrated with the CRM systems to ensure that appropriate actions such as personalization of tariff, discounts etc. are offered to retain the customers.