Most telecom operators are still struggling with inaccurate or incomplete data spread across disparate silos and systems, said Datanomic. Despite operators investing in business intelligence, master data management and data warehouse technologies, many are still not addressing the existing underlying data problems around single customer view.
Customers, however, expect service excellence, and believe every provider representative should have a full and instant knowledge of their complete history across all products and services, noted Datanomic. Without reliable data, problems such as inaccurate billing, wrong addressing, and accidental miss-selling of inappropriate or undeliverable services become difficult to prevent and time consuming to resolve, destroying accrued brand loyalty. With many operators offering bundled packages and services, poor customer interaction now places multiple revenue streams at risk, as well as any opportunity to sell future products and services. The stakes of managing customers’ relationships have never been higher, Datanomic added.
“Today’s consumers have more choice, and products have become increasingly complex,” said Jeremy Lovett, vice president for data management applications for Datanomic. “However, many telecom operators continue to struggle with satisfying consumers across multiple channels and a myriad of different services because they cannot gain a single, meaningful view of the underlying data about their customers.
“Achieving a single view of customer data not only increases customer service levels, but also de-risks data migration projects. Datanomic’s dn:Director sits on top of solutions such as master data management and business intelligence, allowing telecom operators to orchestrate how business rules and data governance should be implemented through a single central resource, saving considerable manual time and effort through its automation.”
Datanomic is offering a free QuickStats Workshop to selected telecoms operators, providing an in-depth analysis of customer data to identify potential weaknesses in quality, accuracy, and consistency to improve operational performance and prevent the issues that can trigger customer churn.