Pipeline Publishing, Volume 4, Issue 12
This Month's Issue:
Consolidation is Key
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Ensuring Customer Loyalty
During Network Consolidation
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speeding new product launches for customers. EPM also defines and improves the product lifecycle process. As products reach the end of their lifecycle, they can be turned down quickly and replaced with newer offerings geared to the customer’s constantly changing needs and interests.

CSPs benefit from reduced costs, both for new product introductions and for product change management. EPM simplifies and streamlines order configuration, reducing errors and gaps in product configuration, and eliminating error-ridden manual intervention. In addition, by enabling carriers to easily bundle products, EPM reduces churn, providing multiple “hooks” into customers to keep them on-board.

EPM is not only about saving money, but also

The single platform approach to product catalog management improves operational efficiency to deliver lower costs, higher margins, and greater customer satisfaction. Customers are the immediate beneficiaries.

that generate automated offers based on available product offerings and network resources to support them.

Predictive analytics works by continuously polling customer databases, creating a “360 degree view” of customers that enables real-time profiling for marketing and product opportunities. Among the benefits, predictive analytics proactively automates custom-


about making it. An enterprise approach enables a CSP to boost ROI on next generation networks by leveraging network assets as reusable components. Further, EPM works hand in hand with another relationship management solution to optimize the profit value of each customer through cross-sell/up-sell activities, and by ensuring customer retention over the full lifecycle of the account.

Predictive Analytics: Where Product and Customer Data Intersect

What if CSPs could develop products based on known customer preferences, and offer them in real time during customer interactions? They can do just that, using predictive analytics.

The function of predictive analytics is analogous to EPM. Predictive analytics sifts through mountains of data to provide a precise, real-time view of the customer – services used, billing, and known preferences. Predictive analytics integrates with EPM product definitions and establishes policies

tailored offers, and delivers pertinent data to a customer service representative’s desktop during an interaction. Because the value of the customer is known in real-time, the operator can deliver the appropriate level of service, based on the value of the customer.

Predictive analytics is commonly found in the retailing and banking industries, where companies can turn customer service inquiries into profit-making opportunities – first by handling the customer’s request, then by moving immediately into cross-sell/up-sell mode with offers best-suited to a customer based on purchase history. Now it’s gaining traction in telecom. For example, when a mobile customer dials in with a question about a service feature, the predictive analytics solutions can pull up the customer’s profile on the agent’s screen. Seeing that the customer keeps tabs on New York Yankees game highlights via his mobile phone, the system then prompts the agent to tell the customer about a new mobile video offer featuring the next Yankees/Mets game. The mobile CSP’s ability to extend the offer proactively, and in real time, creates a positive experience that

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