Subscriber Acquisition

Netmind POS

The Netmind® POS module is a real-time Application Program Interface (API) that interfaces with the order or billing system to transmit subscriber information for analysis when orders for service are taken at the client’s point of sale. When the subscriber data is submitted, NPOS processes the customer attributes of the order against internal and/or external data sources and then returns a result based on the information found and business rules applied. NPOS is recommended as part of a new subscriber acquisition process that utilizes a waterfall type process. A waterfall type process is one in which all subscriber orders for service enter at the top of the process and as subscribers meet criteria checked at a given level, they pool while the remainder traverse downward to the next step until all steps have been completed. Examples of checks at a given level are redundant applications, matches to prior debt, subscriber authentication and risk scoring. By identifying risks in real-time at the Point of Sale (PoS), Operators can stop these “potential” customers early in the acquisition cycle , thereby avoiding provisioning costs, stolen equipment, future transactions like toll and service revenue losses. Better utilization of internal data to screen and determine credit worthiness of new applicants improves collectability and net income.

Benefits:

• Better analyze marketing leads and potential sales
• Identify redundant applications for service
• Enforce credit policy
• Improve overall Net Income

Aliasing

Aliasing is a module that analyzes subscriber attributes, like name, address, phone etc, and determines whether a new subscriber for service has had a prior experience of bad debt or fraud. New subscriber attributes are matched against delinquent subscriber details utilizing direct, complex string comparison algorithms and fuzzy matching logic to link the new subscriber to a former unpaid subscriber. Comparisons of the subscriber credentials with delinquent subscribers for possible associations is performed on attributes such as address, name, tax-id, driving license, and bank account to name a few. By identifying these matches to prior debt or fraud accounts, you can identify subscriber risks quickly and prevent further debts from reoccurring.

Benefits:

• Increase collectability
• Identify conditions of ID Theft
• Mitigate Subscription Fraud
• Avoid additional losses to bad debt and fraud
• Improve Net Income

Risk Modeler:

The Risk Modeler is a module that utilizes predictive modeling to authenticate new subscribers via multidimensional attributes as well as scores the probability of the new subscriber becoming a bad debt. Knowing predicted outcomes of potential bad debt conditions allows you to take preventative actions and limit risk during the acquisition process as well as effectively determine the credit worthiness of new applicants (i.e. Limiting services offered, requiring deposits and/or establishing spending limits, etc.)

Benefits:

• Improve the ability to authenticate the subscriber of record
• Comply with FTC Red Flag requirements
• Reduce Bad Debt
• Mitigate Risk and Maximize Yield