The Usage Data Revolution Report, Part 3 of 4: Is Growth Your Growth Inhibitor?

The Research in Brief

The aftermath of the 2022 SaaS crash and the subsequent shift towards profitability has fueled the integration of AI technologies into product strategies. This convergence presents a lucrative opportunity for SaaS companies to monetize artificial intelligence, although only 15% of businesses made progress in this area in 2023, according to OpenView’s SaaS Benchmarks Report. The industry-wide move towards profitability, driven by declining growth rates reflected in the ProfitWell B2B SaaS Index, has prompted SaaS businesses to consider the effectiveness of usage-based pricing models, inspired by successful examples like ChatGPT’s strategy. This aligns with the broader trend of integrating consumption-based approaches to maximize revenue and enhance customer satisfaction. 

In parallel to these trends, our research, presented as a 4-part blog series, explores innovative strategies and trends in the utilization of usage data by enterprise companies and their leaders. Spanning from Q4 2022 to 2023, the study involved 1,364 survey participants from software and media & entertainment sectors, including managers, senior managers, and CXO-level executives, providing valuable insights into leveraging usage data and navigating data complexity for business growth. 

In this blog, you will discover how doubt surrounding data solutions can impede growth in the subscription economy.

Introduction

Have you missed growth opportunities due to doubt in your data management solution’s capability? The path to growth can be clouded by complexity, leading to hesitation in seizing opportunities. As the subscription model matures, data complexity increases. Innovative pricing models are crucial for customer-focused revenue growth, so it’s vital to identify which tools will support or hinder growth in the mid-term. 

Data Volume and Data Complexity Matrix

Before diving into specific usage data management solutions, let’s provide an overview of the two key components guiding our assessment:

Data Volume

Transfer from source to quote-to-cash system. Some vendors/solutions provide little or no purposeful contextualization of the data or records. Likely to have data validation and correction capabilities but with limited or no correlation, aggregation or data enrichment features.

Data Complexity

The use of accurate usage data records to perform billing and other quote-to-cash functions. Some more mature systems/platforms have rudimentary data mediation capabilities but generally have difficulty with high data volumes, varieties of source data and product complexity.

The Rigid: Legacy Billing System

How do they handle large data volumes?

Traditional billing systems are generally cumbersome, slow, and not designed for intricate data types or high data volumes. They often struggle with unstructured data, and processing large data volumes can slow down the billing processes. To manage data complexities, extensive customizations might be necessary, which can be costly, complex and generally inefficient. Their primary function is to handle the financial aspects of customer transactions, not to process or analyze data. 

How do they handle data complexity?

Traditional billing systems are generally rigid and designed for single transactions or regular fixed subscription billing. When it comes to pricing models, adapting them for usage-based or hybrid pricing can be challenging and often requires substantial customization. They are unlikely to offer the flexibility needed for innovative pricing structures.

The Struggler: ERP System

How do they handle large data volumes?

Enterprise Resource Planning (ERP) systems are primarily built for receiving invoicing data and managing business processes but not for data management. Their struggle with data complexity arises from managing diverse data types, large volumes, dynamic protocol changes, frequent product launches, and the integration of various external systems like Salesforce, HubSpot, Product Catalogs, CPQs, potentially compromising data quality. Customizing ERP systems to accommodate complex usage data can be costly, time-consuming, and may lead to reduced system efficiency. Inefficient data processing can impact the overall performance of these systems. 

How do they handle data complexity?

Their adaptable processes and invoicing enable ERP to handle new business models, though they are not typically designed to support complex pricing models, especially usage-based or hybrid pricing. Customizing ERP systems for flexible pricing can be challenging and may not yield the desired level of flexibility. These systems are more focused on standard business processes.

The Unyielding: ETL Tools

How do they handle large data volumes?

ETL (Extract, Transform, Load) tools are proficient at extracting data from various sources and transforming it into a structured format. While they can handle various data types, including structured and semi-structured data, and perform transformations efficiently, they are older, heavyweight technologies. Real-time processing, built-in data aggregation, and data correction are among its limitations. To adapt ETL tools for usage billing, additional infrastructure and configuration are required. They may not be the best choice for continuous data streaming or processing unstructured data. 

How do they handle data complexity?

ETL tools are primarily designed for data integration and transformation, not for managing complex pricing models. They often lack built-in features for flexible pricing structures. While you can implement some pricing flexibility with custom coding, it’s not their core strength and they are likely to reduce business agility and limit innovation through pricing models.

The Rudimentary: iPaaS or Similar Integration Tools

How do they handle large data volumes?

iPaaS (Integration Platform as a Service) tools are primarily focused on data integration and exchange between applications. While they are versatile for connecting different systems, they are designed for any type of enterprise data, so customizing them for usage data and billing may require time and resources. iPaaS solutions tend to use a “best effort” approach when dealing with files, web services, and APIs, leading to possible duplicates and missing usage data records. This approach can result in overcharging and lost revenue in usage billing scenarios. 

How do they handle data complexity?

iPaaS tools are primarily focused on data integration and may not inherently support flexible pricing models. While they can help integrate different systems, they may not have the built-in features for creating and managing complex pricing structures. Flexibility can be limited.

The Not-So-Flexible: Modern Cloud-Based Billing Systems

How do they handle large data volumes?

Newer billing systems may offer improved data processing capabilities, particularly when compared to legacy systems. However, their effectiveness depends on the chosen software. Some modern billing systems incorporate data processing functionalities, making them more agile with rudimentary mediation that can handle low complexity and low scale usage data volumes, yet their Customization can still be necessary to accommodate specific usage data requirements. 

How do they handle data complexity?

Newer billing systems provide increased flexibility compared to legacy systems, incorporating features for configuring and managing usage-based or hybrid pricing models, including usage tracking, rate configurations, and automated tiered pricing. However, the degree of flexibility can vary depending on the chosen software. Some modern systems provide extensive pricing options, while others may have limitations. Still, they can get stuck as companies grow their usage or hybrid offerings.

The Independent: In-House (Homegrown) Solutions

How do they handle large data volumes?

In-house solutions are often built with ETL/iPaaS or native integrations and can be tailored to meet specific data processing requirements, including managing a broad spectrum of data types and handling large data volumes. Real-time processing and adaptability to changing data types are typically strengths of in-house platforms. However, the effectiveness of these systems often depends on the expertise and knowledge of the solution owner. 

How do they handle data complexity?

In-house solutions can be highly flexible, as they can be customized to support innovative pricing models. When designed with pricing flexibility in mind, they can adapt to changing business needs and offer a high degree of customization. The level of flexibility depends on the organization’s development capabilities and generally requires excessive maintenance and a dedicated IT team to keep them running. Time to market however does not compare with off-the-shelf solutions and companies can struggle when in-house system knowledge is held by key individuals (and when they leave the company with that knowledge.)

The Data Maestro: Usage Data Management Platforms

How do they handle large data volumes?

Dedicated usage data management platforms are purpose-built to handle the complexities of usage data. They excel in managing various data types and formats and efficiently processing large volumes. These platforms often include features for real-time data processing, data validation, and analytics. Their architecture is designed to meet the specific requirements of usage-based pricing and subscription models, making them highly efficient for handling usage data complexities. 

How do they handle data complexity?

Dedicated usage data management platforms excel in offering flexibility for usage-based and hybrid pricing models. They are purpose-built to deliver accurate golden records into quote-to-cash processes and systems, with usage data that has been validated, corrected, correlated, aggregated and enriched to present metered usage for billing. They are designed to facilitate pricing model innovation and agility and offer templates to reduce time-to-market for competitive advantage.

Conclusion

Mastering usage data volumes and complexity is crucial for optimizing your subscription and pricing models. From evaluating comprehensive usage data management platforms to recognizing the limitations of tools with restricted functionalities, choosing the right approach can significantly impact your business growth.

The key takeaway is to assess your current systems against the demands of growing data volumes and complexity, as well as the strategic goals of your business. This includes evaluating legacy systems, cloud-based billing solutions, ERP platforms, iPaaS, ETL, and advanced usage data management platforms. Understanding how each handles large data volumes and intricate pricing models is essential for making informed decisions and ensuring your systems can scale effectively.

Our assessment of where the different usage data management solutions stand on the matrix was cultivated by our 20+ years of experience working with customers and partners in the subscription space. 

Participants in the Study

Research Facilitor DigitalRoute 

Target Audience by Role Managers, Senior managers, CXO-level executives   

Target Audience by Industry Software, Media & Entertainment 

Data Source Online survey 

Number of Survey Participants 1,364  

Participant Industry Representation Software & IT, Media & Entertainment 

Research Span Q4 2022 – 2023 

Other Collected Information Participant functions, Company sizes, and annual revenue 

Research aims to explore innovative strategies and trends in usage data adoption by enterprise companies and leaders 

Research examines prevalence and significance of initiatives in utilizing usage data and navigating data complexity for growth 

Survey was utilized to gather data from professionals accessing educational content produced by the company 

Primary data collected with participant consent 

Responses analyzed using statistical and BI tools  

Limitations of This Research

• The sample for the research is limited to participants who accessed educational content from the initiating company. 
• The aforementioned limitation means that the sample may not fully represent the target audience. 
• The survey questions had a limited set of answer options, potentially limiting the capturing of a full range of business initiatives related to data utilization and growth. 
• The survey responses may be subject to response bias based on participants’ industry, function, or company size.