Consulting and outsourcing firm Accenture has announced a suite of five analytics applications aimed at the telecommunications market. The applications, built on the Accenture Analytics Applications Platform, are designed to allow telecom companies to make data-driven decisions about pricing, staffing, maintenance and planning.

The analytics platform allows development of industry- and function-specific applications that are reusable and customizable to individual companies and users.

The suite of telecom applications includes five offerings.

Network Predictive Fault Management is designed to help predict what network systems are likely to fail before it happens. Detailed reports on vulnerabilities guide users in their efforts for preventive maintenance and maximum uptime.

Revenue Forecasting supports business decision-making by providing insights into the customer base, activations, cancellations, traffic and annual revenue per customer (ARPU).

Bundle Pricing supports strategic decision-making regarding packaging products and services together — for example, a handset and a connection. By analyzing customer preferences, users can make pricing decisions that support customer demand, optimize inventory control and maximize revenue opportunities

Call Volume Forecaster aims to predict call centre traffic to allow companies to schedule appropriate staff to manage call volume — one key to improving customer service, satisfaction and loyalty, while making efficient use of call centre resources.

IT Governance helps telecoms optimize the efficiency of their IT infrastructure by delivering insights on asset mapping and future application demands.

The suite of telecom products brings the number of ready-made analytics applications built on the platform to 25, including applications in the retail, financial services and utilities industries. These applications can be run on a standalone basis, or on Accenture’s recently announced cloud-based analytics-as-a-service platform, Accenture Insights.

Applications can be customized according to user — for example, more granular for data scientists and more general for business users — or according to business needs. Development takes a week to 60 days, depending on the level of customization, the company says.

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