IoT Roaming Monetization
Billions of smart devices are in connected homes, cars, factories, cities and just about anywhere. Innovative digital services provide a huge opportunity for new revenue streams.
How can the potential of IoT be realized? What capabilities do operators need to be able to take advantage of the opportunities?
The digital transformation is continuously challenging businesses and how they can deliver value to their users, especially during COVID-19. The social and economic potential engendered by the proliferation of IoT is immense and, yet, is barely tapped. Many operators find it hard to generate revenues from IoT traffic as they lack the ability to detect, measure, classify and charge for it adequately and automatically. Pressure on network systems and analytics tools places a heavy demand on finite and increasingly stretched resources.
Operators usually rely on APN lists and IMSI ranges to segment the IoT traffic. However, this has been proven to be inaccurate. As opposed to consumers, devices are often in a state of permanent roaming. Since some of them are connected to the network but are silent, it’s key to monitor the signaling to accurately determine the roaming duration and any movement. With signaling information, operators can detect twice as many devices every month. The next challenge is to align these multiple segments with other applications automatically.
IoT traffic differs from consumer roaming behavior and usage patterns. Applying traditional roaming models based on usage is not profitable and results in revenue losses estimated at $7,5M annually for a tier 1 operator. Operators will start to offer differentiated services on the back of 5G and M-IoT to allow enterprises across many vertical markets to charge for specific IoT B2B2B services delivered anywhere in the world. The traditional settlement process via TAP is not equipped to support creative and innovative charging models.
The hidden rise to dominance of IoT roaming
Convert invisible IoT devices roaming in your network to visible profits.
Automating the IoT roaming business
Since the outbreak of COVID-19, IoT roaming has generated more wholesale revenues than consumers. The outlook for IoT roaming is promising and improving.
The step towards monetization starts with the discovery. The TOMIA solution uncovers hidden devices seamlessly, including the silent ones, to automate the entire wholesale supply chain from detection to segmentation, network management and settlement.
- Definition of static parameters such as IMSI ranges and APN lists
- Detection through device-based and Machine learning (ML) models
- Monitoring of roaming duration of silent devices through signaling
- Behavioral insights based on patterns
Network management is critical to ensure the right type of connectivity anytime anywhere, while controlling wholesale costs. TOMIA’s solution automatically distinguishes the radio access technology, allowing operators to redirect devices to the desired network.
- Alignment of IoT segments with steering and charging applications
- Automatic detection of NB-IoT and 5G devices
- Support of multiple commercial strategies and flexible device-based models
- Hybrid settlement options via TAP or BCE
Most of the IoT devices consume low data volumes, and therefore, charging based on usage, as is done in consumer billing, is not profitable. With 5G, differentiated network resources will be available, including the option for local breakout to handle low latency requirements or data processing regulations. To support new commercial models, the industry is leaning towards the Billing Charging Evolution (BCE) settlement process. Moreover, many enterprise deals will require B2B2C implementations at a wholesale level, for which margin calculation is required.
Narrow-Band IoT (NB-IoT), together with LTE-M, is intended to deliver on the promise of scalable, cost-effective massive IoT applications. Some of the main advantages of NB-IoT are lower power consumption, improved and extended indoor coverage and scalability. Considering that IoT agreements are long-term, operators who offer NB-IoT coverage first may benefit . Operators require not only to identify NB-IoT traffic and charge it effectively, but also automatically identify NB-IoT capable devices and steer them differently than consumers.
ML based detection
There are different components involved in detecting IoT traffic. The most common one is based on the hardware profile, or IMEI information, of the device. Devices tend to behave differently than consumers such as low traffic volumes, peaks of signalling traffic or sometimes steady and recurring usage on daily, weekly or monthly levels. Machine Learning (ML) algorithms are used especially when IMEI information is not available. It can interpret signalling and usage information to provide insight into consumers and IoT traffic patterns including those of silent IoT devices.