Get a free demo
Other

The Usage Data Revolution Report, Part 1 of 4: 2024’s Software Industry Game Changers

This first installment of a 4-part blog series explores the use of usage data by enterprise companies and leaders. From Q4 2022 to 2023, 1,364 managers, senior managers and CXOs from software and media & entertainment sectors participated. Insights are provided for utilizing usage data and managing data complexities for business growth.

DigitalRoute
DigitalRoute

Updated on November 13, 2024

The Usage Data Revolution Report, Part 1 of 4: 2024’s Software Industry Game Changers

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, we will present the report and our main impressions on the trends in usage data management tool adoption, and the top challenges that SaaS companies are focusing their efforts onto.

Exploring Solutions: How SaaS Companies Manage Usage Data  

Usage data is at the core of all subscription-based services, and the tools used to manage this data play a consequential role in their success, growth, and, considering the current state of SaaS, their survival. 

Below are the key impressions from our research:

  • ERP and modern cloud-based billing systems are popular choices for medium-sized companies. 
  • Usage data management platforms are favored by large enterprises.  
  • Legacy billing platforms are not suitable for managing complex or high-volume data. 
  • ERP system adoption is higher in the EU compared to the US. 
  • The adoption of homegrown platforms for managing usage data is higher in the US compared to the EU. 
  • iPaaS tools have lower popularity in the US.
1 in 5 big-player SaaS companies roll with usage data management

Modern Cloud-Based Billing Platforms 

Popular choice with a 16.55% adoption rate and an 18% combined adoption rate among enterprises in both the EU and the US with revenues over $1B.  

Valuable for robust billing data processing and pricing model innovation. However, they may bring challenges in integrating with legacy systems, struggling with real-time precision and managing complex data at scale. 

Usage Data Management Platforms 

Preferred choice for enterprises in both the EU and the US with revenues over $1B, with an 18% combined adoption rate.  

Valuable for robust usage data processing for billing, especially when traditional methods and in-house integrations lack data complexity handling and pricing model innovation support. 

ERP Systems 

US lags behind the EU in ERP system adoption (14.63% vs 20.39%). Assess if preprocessing usage data is needed before integrating into ERP.  

It’s vital to consider data quality, consistency, robust processing, system integration, time constraints, and scalability when choosing an ERP system. 

Legacy Billing Platforms 

Adopting legacy billing platforms for usage data management is relatively low. However, continued use is substantial at 15.3% among US companies with revenues up to $500K.  

Billing platforms’ expertise is in billing. They aren’t built for enriching data for usage-based revenue, so they struggle with the complexity and volume of data generated by usage-based models. 

Homegrown Solutions 

EU companies have lower utilization of homegrown solutions for managing usage data (7.9% vs 15.4% in the US).  

While the benefits of custom-fit homegrown solutions may surface in the beginning, they usually start waning as data volumes and complexity grows. Building a usage data management solution that is outside of a company’s domain expertise often evolves into a strenuous and costly maintenance endeavour in the long run. 

IPaaS Tools 

IPaaS tools have lower popularity in the US compared to EU-based companies with revenues from $500M to $1B (8.96% vs 15%).  

Companies relying on iPaaS tools for data management may be deterred by challenges with data aggregation and auditability over time, which are crucial for long-term scalability. 

ETL Tools 

ETL tools are the least popular for usage data management. US companies with revenues over $1B and EU companies in the $500M to $1B range use them the least.  

These legacy tools limit visibility, hinder agility, and restrict pricing innovation. 

1 in 2 professionals prioritize customer-centric needs employing diverse usage data management tools to support them in their mission.

The need for customer-centric initiatives is increasing in usage data management tools, except for iPaaS where it comes in second. This reflects a shift towards personalized engagement and retention strategies in an industry with stagnant traditional subscription models. 

Data management is the second priority across various usage data management tools, except for iPaaS. This is because of challenges in handling complex operational systems and diverse data sources, such as different protocol versions and external systems like Salesforce and HubSpot. 

The lower emphasis on operational efficiency needs among companies utilizing ETL tools, homegrown solutions, legacy billing platforms, and modern cloud-based systems could be indicative of these tools’ limitations in providing streamlined and efficient processes. 

ETL tools, being old and heavy-weight technologies known for their batch-oriented processing, lack the real-time and data correction capabilities required for immediate operational efficiency gains. 

Homegrown solutions (often built with ETL) and legacy billing platforms, might face challenges in adapting to the dynamic demands of today’s subscription models, leading to operational bottlenecks and incremental costs. 

Modern cloud-based billing platforms, while offering more agility, may still grapple with rudimentary mediation and data complexities, hindering their ability to deliver optimal operational efficiency. 

The correlation suggests that businesses relying on these tools may need to invest in more modern and agile solutions to enhance operational processes and ensure financial efficiency in their subscription-based models.

SaaS Growth and Improvement Roadmap 

Our recent survey included 200 participants who were asked to rank the following key challenges based on their perceived priority for SaaS and subscription businesses. 

The results provide valuable insights into where businesses should be focusing their efforts.

Supporting innovative pricing models in subscription-based or hybrid services is a top pressing challenge for Anything-as-a-Service

Processing and preparing usage data for billing (Mean: 5.45) – To accurately bill customers and avoid dissatisfaction, businesses need to process usage data stenuously, which can be hindered by legacy systems, manual processes, and disjointed system components. 

Detailed understanding of product/service consumption/usage (Mean: 5.44) – Effective interpretation of granular usage data is consequential in tailored offerings, efficient billing, and preventing revenue leakage. Complex usage patterns require teal-time analysis and unified systems for data-driven pricing strategies. 

Reconciliation of financial and quote-to-cash data (Mean: 5.05) – Accurate reconciliation of financial and quote-to-cash data is essential to prevent compliance and loss of credibility, which can be improved by streamlined processes through well-integrated systems. 

Revenue recognition speed and efficiency (Mean: 5.00) – Delayed revenue recognition from inefficient processes can affect financial health, investor confidence, and compliance. It requires seamless data transfer and real-time analysis, which can be hindered by data complexity and disjointed systems. 

Revenue reconciliation accuracy (Mean 4.97) – Advanced analytics and integrated systems maintain financial integrity, prevents revenue leakage, and reduces the risk of errors and customer dissatisfaction. 

Tracking and auditing data records across the quote-to-cash process (Mean: 4.89) – Complex system integrations and configurations can hinder proper data tracking and auditing for transparency, accuracy, compliance, and revenue leakage prevention. 

Revenue assurance and leakage control (Mean: 4.84) – Businesses must have systems in place to identify and control revenue leakage points in complex usage environments. This requires advanced monitoring, analytics tools, and integrated systems for a holistic view of the entire revenue ecosystem. 

Usage limit tracking and enforcement (Mean: 4.73) – Enforcing user entitlements and ensuring fair usage requires advanced data processing, integrated systems, and real-time enforcement capabilities.

Business Initiative by Implementation Timeline

The substantial commitment to tackle complexity and increasing data volumes aligns with the recognition of the critical role data plays in driving operational efficiency and innovation. This signals a shift from reactive problem-solving to strategic anticipation.  

In this chapter, we take a closer look at the strategic initiatives that companies adopt as they navigate the challenging terrain and competition. 

Companies are focusing on partner settlements and revenue sharing processes for long-term growth, which aligns with the trend of expanding revenue through partnerships. 

Companies would rather address data complexity, customer satisfaction, churn reduction, and revenue accuracy initiatives sooner than later. Their plans align with customer-centric models and strengthens financial foundations amid a saturated traditional subscription market.

Companies focus on ensuring end-to-end data for regulatory compliance, building a robust foundation to reduce revenue leakage across the quote-to-cash process. This focus somewhat reduces after the first 6 months, possibly reallocating resources to other critical areas for growth and innovation. 

Companies are transitioning from foundational compliance to strategic initiatives like launching new business models and pricing strategies for revenue growth and staying in-tune with customer needs. 

The diminishing focus on understanding service consumption signals a potential challenge, suggesting that companies may be prioritizing immediate revenue gains over long-term success. It’s a call for balance: while chasing quick profits, businesses shouldn’t forget that a deep understanding of user behavior is crucial for long-term success. 

Business Initiatives by Revenue

Mastering complexity and increasing data volumes is a top priority across companies, irrespective of revenue size. Companies are acknowledging that their ability to harness and manage data intricacies directly impacts revenue generation and growth. 

What strategic priorities follow after companies master complexity and data volumes, and how do they differ based on company revenue size? 

Large enterprises with >$1B revenue prioritize initiatives for business growth and protection, focusing on new business models and pricing strategies for substantial revenue growth.

Understanding customer needs and testing value metrics is crucial, though implementing usage-based pricing models in complex tech stacks can risk revenue leakage without proper IT infrastructure. 

Companies with revenues up to $1B, and larger enterprises, aim to decrease customer churn and improve revenue share accuracy.

Analyzing usage patterns allows companies to personalize offerings, address customer pain points, and enhance retention. 

Companies with revenue below $500M prioritize an additional initiative: protecting the business by ensuring accurate accounting and billing to prevent revenue leakage. This is crucial for maintaining streamlined financial operations, enhancing efficiency, and protecting revenue streams across the quote-to-cash process.

Next part of the series:
Business Model Maturity

DigitalRoute

DigitalRoute

Founded in 2000, DigitalRoute boasts over two decades of collaboration with telecoms and diverse enterprise sectors to provide a deep understanding of product and service usage.

download report

Get the ultimate guide to
monetizing usage-based services

Download the guide