It’s no secret how vital AI-enhanced assurance is to ensuring quality of service and accelerating effective troubleshooting. From early fault and anomaly detection to significantly reducing mean time to resolution, it’s at the heart of peak-performing networks and a superior customer experience. But the strategic value doesn’t stop there. Assurance is a great aggregator of data and a generator of insights. As such, operators with AI-enhanced assurance with the capabilities for cleaning, segmenting, and analyzing this data at scale will be able to tap into a whole new set of monetization opportunities.
The two paths to monetization
Saving costs with internal monetization
Enhanced automated assurance with 5G AI analytics enables operators to extract insights from network data to improve efficiency and reduce costs. For example, insights about traffic patterns, network load, and other factors can be leveraged to optimize power consumption and reduce energy-related costs.
By tracking and identifying the level of connectivity in and around a city or even in rural areas, operators can see where additional sites and infrastructure are needed for better planning that optimizes costs.
Driving revenues with external monetization
Operators can also turn network data into a value-driving offering for external monetization.
Namely, they can extract previously unattainable insights from data about user behavior, movement and commute patterns, and more and deliver them to entities across multiple industries, empowering them to refine strategies, optimize operations, and grow revenues.
Let’s consider a critical external data monetization consideration and some real-life use cases.
An external monetization must-have
When crafting the network data monetization strategy, operators must make sure that they don’t overlook the need to ensure data privacy.
Before network data is delivered to third parties for business or operational insights, subscriber privacy may be protected by cleaning and anonymizing the data to remove all identifiers.
Doing so means applying advanced anonymization methods, aggregations, and data modeling that are driven by:
- Mass data handling capabilities
- Real-time analysis with automated insights
- Assimilation of data from multiple sources and domains
- Extraction of precise and high-quality data
Three network data monetization use cases
To illustrate how new revenues can be created from network data offerings, let’s look at three examples from operators leveraging RADCOM to drive external monetization.
Monetization for the public sector
Our first example comes from a European operator who created an offering for a local municipality to help enhance road infrastructure planning.
- With origin-destination analytics, the municipality gained important insights into:
- Which routes are most preferred by commuters during specific periods when traveling from origin X to destination Y?
- Where along the popular commute routes do intersections and traffic circles get congested?
- Congestion time ranges and respective travel directions
The insights delivered by the operator’s offering enabled the municipality to understand which routes are most popular among commuters and to make data-driven decisions about whether a new bridge or a highway renovation project would better serve the public.
Empowering transportation services providers
Network data insights enable transportation service providers to make more informed decisions about which lines and routes need to be added or augmented so they can meet demand and grow revenues. For example, they can leverage origin-destination analytics in this use case as well to query network data and deliver answers to questions such as:
- Which are the most popular bus lines and stops during the morning/afternoon rush hour?
- What is the origin/destination zip codes and neighborhoods for commuters entering or leaving the mass (metropolitan) transportation system at each of its busy stations?
- Which zip code contributes to the most bus rides during weekdays/weekends?
One North American operator is providing the region’s primary transit services provider precisely these types of insights with RADCOM. As a result, a complex system of lines and routes has been enhanced to better support commuters who require access to the city’s main business centers.
Mass event management
Roamer analytics, population density analysis, mobility analysis, and tourist behavior pattern profiling empower mass event producers to better plan and orchestrate crowd management, policing, parking, and in-store commerce traffic, among others. For example, an annual parade with tens of thousands of participants mandates careful planning to ensure their safety. At the same time, such an event holds great commercial potential for the city’s businesses and for the municipality itself.
To help a municipality in EMEA meet the need and capture the opportunity, the local operator is leveraging RADCOM to deliver the required insights. These include:
- When is the population density highest, and at which locations?
- How many people typically stay at which hotels and on which dates?
- How many days do tourists stay in the city where they visit, including POIs outside the city?
Such insights help the city to better plan for the annual parade – knowing how many police officers should be deployed and where and what should be the opening hours of various parking lots around the parade’s routes, all while empowering local stores and hotels to prepare for the increase in potential buyers and guests. These data insights can also be helpful for governments and tourism, enabling organizations to make better data-driven decisions about the movement of people for security reasons at a local and national level and help connect businesses and potential consumers.
With access to masses of subscriber data, operators are uniquely positioned to offer strategic insights to various organizations. Turning network data into a new kind of offering enables them to bring unprecedented value to their customers and create multiple streams of new revenues for themselves.