Thursday, June 2, 2011

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.


  1. I am doing research on this field. Is there any solid facts(research papers) about telecommunication companies used data mining and got the result?
    I need information, like, what telecommunication companies used what kind of data mining and what the result was. So far, as my perception, main data mining are used for segmentation, churn analysis, fraud detection. Is there any other application of data mining that was proven to be beneficial?

  2. Awesome blog. Thanks for this info!

  3. Nice blog great information.

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