Governing Customer, Product, Network, and Big Data in Telecommunications
Best practices for managing industry-specific information
Telecommunications operators are growing increasingly aware of the importance of treating customer, product, and network data as an enterprise asset. In addition, these companies are grappling with the governance issues relating to big data such as Call Detail Records (CDRs), billing and data usage event records, social media, and geolocation information. This article discusses best practices for governing the different types of information in the telecommunications industry.
Improve the overall customer experience by establishing a single view of the customer across multiple silos
With the high costs of acquiring new customers, most telecommunications companies have placed a strong focus on retaining their existing customer base and reducing churn. Telecommunications companies recognize that the overall customer experience is strongly correlated with customer loyalty and revenues. This is an even bigger issue as more subscribers move to the prepaid (pay as you go) model, where there is no annual contract, no penalties to terminate service, and it is as simple as switching to the SIM module from another pay-as-you-go provider. Customer experience is based on the range of interactions with the company including making a call, sending an SMS message, checking the weather report, ordering a new service, speaking to the agent in the call center, and topping up a prepaid phone or receiving the monthly bill for a postpaid one. Most telecommunications companies make extensive use of their customer, service, and network data to analyze customer experience. Sound information governance improves the level of trustworthiness in analytics around the customer experience and enables telecommunications companies to manage the customer experience more efficiently and cost-effectively. The information governance program needs to improve the reliability of a number of attributes around the overall customer experience:
- Demographic data such as gender, age, marital status, nationality, socio-economic group, income, and language preference are critical to the marketing department to drive segmentation models, marketing campaigns, and churn models for residential customers. This data tends to be readily available for postpaid contract customers, but can be of questionable validity for prepaid customers. For example, the marketing department at a mobile operator may notice that younger customers use SMS more than voice calls. The product management team will produce a new offering with a large number of inclusive SMS messages but fewer voice minutes for a contract price per month. The marketing department then drives a campaign targeted towards younger customers. However, with poor information governance around the age attribute—for example, an operator had thousands of subscribers who were born on 1/11/1111—it is possible that the mobile operator will send the offer to the wrong customers. These customers in turn will find the offer to be inapplicable, and that may damage the image of the mobile operator.
- Customer preferences are also important. For example, do not contact indicators are important from a privacy perspective. The information governance program needs to ensure that data stewards are managing the accuracy of do not contact indicators, or else the operator may be exposed to fines from the regulators.
- Household structure is important for residential customers. The marketing department can derive better insight into parents who might have different accounts as well as additional users of the services who might be the kids.
- Usage data relates to Call Detail Records (CDRs), Internet Protocol Detail Records (IPDRs), phone top up records, and value-added services such as weather reporting, ringtone, movie, and music downloads. The marketing department at an operator implemented information governance around the business rules within the campaign management system. As a result, the marketing department found inconsistent rules such as an offer for a traffic monitoring service to a customer who did not have a data service.
Support product standardization initiatives across the enterprise
One division reports revenues separately by red and blue mobile phones of a certain make and model. Another division does not break out the color separately. The inconsistency in product naming and hierarchies causes a number of issues for telecommunications companies that have multiple silos or that have grown through mergers and acquisitions. One telecommunications operator implemented governance over its enterprise product catalog. The operator had more than 20,000 Universal Service Order Codes (USOCs) in its legacy product catalogs. However, the operator decided to implement a new enterprise catalog with only 500 products that accounted for 99 percent of the revenues. Because of the new catalog, product management was able to reduce the time to introduce new products by 70 percent, which increased the revenues for the associated products by three to four percent. Finally, the operator was able to shave four weeks from the training time for new customer service representatives on the simplified product catalog. This was important because the average tenure of a customer service representative was only 12 months.
Enhance the effectiveness of performance management, capacity planning, and location-based services
Telecommunications operators are making significant investments in the networks that will enable next-generation offerings such as location-based services. One large telecommunications carrier found major data quality issues around the master data relating to its network inventory. The telecommunications carrier used separate systems for network provisioning and for network inventory management. The network field operations teams might receive a service request within the network provisioning system to upgrade a trunk line. However, this change was not always reflected in the network inventory system that was maintained by a separate team. The network inventory data then flowed into the business intelligence system. Because the business intelligence system still reflected the slower trunk line, the performance management team classified the trunk line as a bottleneck because it was listed as 300 percent utilized. To make matters worse, the capacity planning team included the trunk line on its list of planned upgrades as well. When this scenario was duplicated across thousands of network elements, the operator faced huge challenges. The operator would accept new customer orders because the network inventory system reflected sufficient network capacity. However, when the network technicians visited the switch, they would find that there was insufficient capacity. On the flip side, almost 40 percent of the upgrade requests that were sent to the network field operations did not actually require incremental network build. The operator also faced metadata challenges when the terms “node,” “router,” and “switch” were used inconsistently across its network.
Reduce costs through more efficient management of information
Few industries have felt the impact of the explosion of data more than the telecommunications industry. The associated impact on IT budgets has had a dramatic impact on how telecommunication companies have viewed traditional IT and business operations. In addition, over the last decade, telecommunication companies have been hit with ever-increasing demands from governmental agencies, such as The United States Department of Homeland Security, who require access to vast amounts of historical CDR data. Many telecommunication companies are coming to the realization that, without robust information lifecycle governance processes and policies, they cannot meet the demands of all stakeholders. For example, one Asian telecommunication operator is now using information lifecycle management tools to archive over one terabyte of data every month.
Secure access to sensitive big data such as CDRs, IPDRs, and location information
Telecommunications operators generate highly sensitive data about their customers including who they call, when they call, how often they call, from where they call, what data services they use, how often they use these services, and what they download. If this information were to fall into the wrong hands, it could cause a lot of embarrassment for their customers and potentially expose the operator to fines and reputational risk. One large operator wanted to implement a predictive analytics strategy around churn management. The operator decided that it needed to outsource the analytics to an overseas vendor. Because the CDRs needed to be shipped to the vendor on a daily basis, there was significant concern about safeguarding the privacy of customer data. After the appropriate deliberation, the operator decided to mask sensitive data such as the name because the calling and receiving telephone numbers were the primary fields of value for churn analytics.
Ensure consistency of business definitions
Telecommunications operators also need to drive consistency of critical business definitions. For example, Average Revenue per User (ARPU) is a critical term that is relied upon by executive management to make business decisions. However, marketing, finance, and sales often have inconsistent definitions for ARPU. For example, finance might include employees while marketing might exclude employees from churn calculations. The information governance program needs to drive to a consistent definition for critical business terms. Telecommunications operators have several areas to govern their customer, product, network, and big data. As with any information governance program, it is important to start small with a defined set of key attributes, measure results, drive executive sponsorship, and broaden the scope over time.
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