Behavioral Analytics paints a detailed picture of preferences and habits of like-minded individuals, accounts and customer segments. It analyzes how subscribers’ consume content or services, including who, when, where, how much, and on what device. This digital interaction is cross-referenced with customer, device, service, network, location and business data to provide a true snapshot of subscriber and customer groups.
Conversely, the information may also be used to identify candidates for marketing campaigns based on their usage levels, the services/content they consume, the devices they use, and other behaviors.
CONSOLIDATED SUBSCRIBER-LEVEL VIEW OF ALL INTERACTIONS
Our solutions, built on our SONAR analytics platform, offer a consolidated, subscriber-level view of all calls, messages, web sessions, purchases, downloads and other digital interactions across all services and technologies: fixed, wireless, 4G, voice, messaging, and data. Our multi-dimensional analytics platform puts the customer first, but also allows analytics at the aggregate level (e.g., accounts, customer segments), which allows you to perform complementary analytics on device, service and network-level, as well as any combination of the various dimensions.
FROM SUBSCRIBER TO GROUPS: ACHIEVING BETTER SEGMENTATION
The contextual drill-down from customer profile to specific events means that behavioral analysis is extensible with quality of experience and profitability attributes. Ultimately, the analytic environment can be extended for deeper investigation into individual customers, or groups of customers with similar demographic or behavioral attributes. Behavioral Analytics is aimed at marketing professionals, such as product managers responsible for services, devices, rate plans and 3rd party content partnerships, as well as market campaign analysts.
COMBINING METRICS TO BOOST PROFITABILITY
By integrating with other Customer Analytic modules, it is possible to incorporate quality metrics (from the Quality of Experience Analytics module) or financial metrics (from the Profitability Analytics module) to derive a more holistic view of each customer. For example, a highly popular service may, in fact, be incurring significant 3rd party costs, and should be trimmed back rather than expanded. Similarly, a candidate who is likely to buy a new service may be removed from a campaign list if her profitability is low, or has significant usage in locations where coverage may be incomplete.