Harnessing Usage Data for Innovation and Revenue Growth

Business Model Innovation

Enterprises are increasingly shifting to mixed business models to accelerate revenue and customer growth. At the center of this transformation is usage-based pricing, which is gaining momentum across industries. By allowing customers to pay only for what they consume, usage-based models lower barriers to adoption and reduce risk—particularly for high-value products and services. This approach is especially well-suited to software offerings from cloud resources and AI offerings, where cost structures scale linearly with usage rather than following the fixed-seat models of traditional SaaS.

Properly managed, usage data provides deep insight into customer behavior: when and how products are used, where gaps exist, and which enhancements will drive adoption.

The evolution of usage-based pricing is not new, but its applications continue to expand with technology. Telecommunications was among the first industries to adopt, charging per minute of network usage. Today, cloud hyperscalers and storage providers price by the amount of data consumed, while AI models are monetized through transactional metrics such as queries or questions answered.

Central to these models is usage data—the foundation for accurate and timely billing. Yet its potential goes far beyond revenue collection. Properly managed, usage data provides deep insight into customer behavior: when and how products are used, where gaps exist, and which enhancements will drive adoption. Because usage fluctuates naturally, historical analysis enables more accurate spend forecasting and revenue predictability, benefiting both providers and customers. When used effectively, this analysis can help enterprises improve their ability to understand and address customer needs, enabling them to be more intelligent.

However, current methods of capturing and processing usage data—moving operational data into billing, ERP or subscription systems—are functional but limited, typically treating usage data as any other data and failing to acknowledge its importance and value as revenue data. These methods are designed primarily to move data from source to system, not to unlock the broader organizational value of usage insights. These functional gaps have given rise to a new category: Intelligent Usage Data Platforms. These platforms enable enterprises to capture, refine, and operationalize usage data at scale, turning it into a strategic asset that fuels customer intelligence, product innovation and revenue growth.

For both business and IT leaders, the imperative is clear: assess whether the organization is equipped to harness usage data as a driver of competitive advantage. The question is no longer if usage data matters, but how effectively it can be put to work.

Usage Data as a Growth Engine

Usage data from products and services introduces unique challenges. It is often generated in massive volumes, at high frequency, and in diverse formats. To be actionable, this raw data must be filtered, standardized and aggregated. For billing and usage analysis, the requirements are even more stringent: data must be clean, normalized and auditable to ensure accuracy, reliability and regulatory compliance. Establishing a usage-to-value process can provide an auditable trail to govern the ingestion, cleansing and aggregation of usage event data so it can be effectively used for analysis.

Unlike flat-fee subscriptions, usage data is not known in advance. This uncertainty makes anomaly detection difficult unless expected or plausible values can be established—something that cannot be resolved after rating alone. For public or regulated industries, such a lack of transparency is unacceptable. Even in unregulated sectors, unremediated errors can lead to revenue leakage and dissatisfied customers. In many commercial relationships, the billing event should not be the only regular point of contact between buyer and seller.

Revenue recognition is another critical challenge in usage-based business models. Unlike one-time transactions, revenue can only be recognized when services are actually consumed. As with other financial processes, the ability to assess revenue recognition implications in real time improves visibility for the Office of Finance and provides early warning of potential deviations from expected results. This places a premium on timeliness and accuracy, both of which directly impact accounting and compliance. To meet recognition standards, enterprises must estimate average expected usage within each reporting period—a task that demands robust data, forecasting, and controls.

When properly instrumented, usage data can also become a catalyst for digital innovation.

When properly instrumented, usage data can also become a catalyst for digital innovation. As products, services, and bundles evolve rapidly, the ability to experiment more frequently and adapt offerings quickly is achieved. To sustain this pace, operational processes must not become barriers. Rich usage insights further inform product targeting, promotional design, and discounting strategies, helping enterprises refine customer engagement and strengthen market responsiveness.

Options for Managing Usage Data

For many enterprises, managing usage data from their products and services is still unfamiliar territory. In the market today, four primary approaches are available. The first is “in-house” that extends existing data management processes, typically using data warehouse or database technologies combined with ETL tools, delivered either through an iPaaS solution or hosted internally. The second is “billing applications” and this approach relies on the loading and data management capabilities embedded within these applications. A third option is “metering applications” which present themselves as managing usage data but in practice only process what is needed for billing. The fourth and most comprehensive option is a dedicated “usage platform” that operates independently from both the data source and the target system, ensuring greater flexibility and scalability.

Each approach is suited to different business contexts and carries distinct implications for operational and revenue outcomes. In-house development may be effective for enterprises in stable markets with limited product innovation and strong internal development expertise. Cloud billing applications, where data management is tightly integrated with rating and monetization, are often best for those that require straightforward metering and billing. Specialist solutions are more appropriate where pricing models evolve rapidly, product innovation is continuous, and data formats are complex and diverse. These platforms offer the flexibility to adapt without the heavy and ongoing investment associated with in-house maintenance.

Unlocking Usage Intelligence

Business models for monetizing products and services are evolving rapidly, and enterprises must be prepared to adapt. Telecommunications offers a useful precedent: as digital switching and networking advanced and internet usage surged, providers were forced to transform and invest heavily in data infrastructure to stay competitive. To remain agile, enterprises should consider decoupling usage data management from rating and billing systems. This approach reduces technical debt and enables support for emerging use cases that may not align with existing platforms. Usage data has value beyond billing. It provides forward-looking market signals to inform strategic planning and revenue optimization.

The benefits extend across the organization. For product and marketing teams, usage analysis can highlight feature adoption, identify underutilization, and uncover opportunities for innovation. For customer service and support, aggregated usage data can serve as an early indicator of customer health and satisfaction.

Predictability is equally important. Buyers depend on accurate forecasts of usage against budget to avoid overages and unplanned costs, while sellers require reliable revenue projections to meet financial objectives. Without these insights, both sides face increased risk and potential disruption.

Key Characteristics to Consider

Because usage data from products and services spans channels, customers, and systems, enterprises should involve a broad set of stakeholders when designing solutions. A narrow focus on billing will risk producing a system that fails to meet wider organizational needs. While the solution may be technical, it must align closely with business requirements to deliver value. The benefits of an intelligent usage data platform span business areas including product, marketing, customer service and finance while also meeting the needs of IT.

The benefits of an intelligent usage data platform span business areas including product, marketing, customer service and finance while also meeting the needs of IT.

Cross-disciplinary collaboration is essential. IT, finance, product, marketing, customer service, and support teams all bring critical perspectives that together create a comprehensive view of use cases. Enterprises must also plan beyond immediate priorities, anticipating shifts in product mix between physical and digital offerings and considering future opportunities to enhance instrumentation. This forward-looking approach reduces the risk of obsolescence and avoids adding technical debt.

The pace of innovation in technology and methods cannot be overlooked. Enterprises must prepare for the expanding role of AI in future offerings. Unlike traditional products, AI carries a linear cost structure, where greater usage directly increases costs. This makes it critical to align usage insights with cost modeling. Building scalable infrastructure today will enable enterprises to design sustainable pricing and profitability strategies for tomorrow, ensuring they capture the full value of data and insights generated by AI.

Next Steps

• Begin by understanding the role of usage data platforms in supporting customers and products with use cases that balance current needs with future requirements.
• Evaluate which solutions best align with business priorities such as customer engagement, revenue growth and product innovation, while taking into account available resources, skills and strategic goals. Data is important, but the real value lies in enabling insights and applying AI across revenue cycles and processes.
• Compare the total cost of ownership across all options with a clear view of the achievable return on investment. This means weighing a variety of approaches.
• A thorough analysis ensures that the chosen approach is both cost-effective and future-proof, allowing the enterprise to fully exploit the potential of usage data and generate insights that guide the business toward optimal outcomes.