The Internet of Things (IoT) is rapidly expanding, with predictions of 27 billion devices connected by 2025, transforming both consumer and B2B sectors. This growth is not only introducing innovative devices into homes but also revolutionizing traditional machine-to-machine (M2M) communications, commonly referred to as B2B IoT.
Data Surge and Network Implications
These IoT devices are generating vast amounts of data, significantly impacting network traffic as it shifts from human-generated to machine-generated. This data isn't just voluminous; it's multifunctional, aiding businesses in operating more effectively, enhancing customer communications, and optimizing logistics and machinery management.
From Low-Volume, High-Value to High-Volume, Low-Cost
As B2B IoT continues to grow, service providers are facing pressures to monetize their offerings more efficiently. The shift from traditional low-volume, high-value events to high-volume, low-cost data demands a reevaluation of billing practices. This shift necessitates precision in billing as minor inaccuracies can become costly when scaled across billions of data events.
Innovative Billing Models and the Demand for Flexibility
A flexible, usage-based billing model is becoming essential. This model allows providers to charge based on actual consumption, which can vary greatly between different applications and services. Innovative pricing strategies such as variable pricing, quality-of-service (QoS) based pricing, and solution-driven events are being explored to maximize revenue and adapt to the diverse needs of IoT applications.
Leveraging Big Data for Strategic Advantage
The nature of usage-based rating gives service providers a precise view of service utilization. The huge stores of usage data can then be processed to offer increased analytic awareness throughout the business. Analytics can be provided to assist the service provider in minimizing costs associated with delivering their service, for example using the data to optimize behavior or optimize the transmission of the data.
There are two primary ways to engage big data analytics in a usage-based billing model:
- Predictive analytics help providers identify potential trends as they begin to emerge. This allows them to predetermine future pricing models and impacts on margin in addition to providing more robust revenue assurance and fraud capabilities.
- Prescriptive analytics is more advanced and will allow the provider to automatically make adjustments to the system, removing human tactical decision-making from the equation. For example, the provider can prescribe rate plans based on predictive usage trends, which would trigger other workflow events, such as searching for the lowest cost data route with the highest available network element.
Navigating the Future of IoT Monetization
As IoT continues to evolve, solving the monetization puzzle will be crucial for service providers seeking to maximize revenue from the burgeoning amounts of data traversing their networks. The need for personalized, scalable, and flexible solutions is clearer than ever as the landscape of IoT expands.
Ryan Susanna /
Ryan is a seasoned telecommunications expert with a broad background in both the service provider and software vendor sides of the business. Ryan is currently responsible for worldwide sales at LogiSense. During his tenure, Ryan has held executive level positions including Senior Sales Executive, and Director of Sales. In these roles, he has provided strategic sales, product, and market guidance for our next generation IP service management solutions.
