What is Automated Revenue Management?

Automated Revenue Management (ARM), also known as revenue recognition (“rev rec”), solutions help automate the accurate and timely classification, scheduling, allocation, calculation, and presentation of revenue in the appropriate periods and amounts within the accordance of accounting standards and other regulatory mandates.

Once viewed as an adjunct to the general ledger of an accounting application and considered an automation tool for revenue recognition, ARM solutions have blossomed into a standalone category that meets the essential accounting needs of a growing number of industries. ARM does not exist in a vacuum. It is part of the larger finance automation ecosystem.

How has the ARM market evolved?

Historically, there was a rather simplistic, linear view of the process and data flow for revenue recognition. Sales configures an offer, prices it, quotes it, gets approval, and eventually signs a contract, resulting in an invoice and revenue flowing through general ledger towards recognition.

More recently – particularly since the adoption of new accounting standards ASC 606 and IFRS 15 in 2018 and 2019 – ARM solutions have blossomed into a standalone category that meets the essential accounting needs of a growing number of industries. Contrary to conventional wisdom, the market for ARM solutions actually expanded and accelerated in growth since the adoption of new accounting standards. At the time of the arrival of ASC 606 and IFRS 15, many in the industry believed that corporate investment in revenue automation tools would decline after the reporting deadlines passed. Instead, as MGI Research predicted at the time, the market has in fact accelerated with more spending, more choices in the market, and more demand for ARM solutions in business and finance.

The supplier’s market for ARM solutions is currently experiencing a period of investment and innovation. New startups are delivering innovative solutions and attracting funding, while existing players are dedicating more investment and resources to improving their offerings. The buyer’s perspective is shifting, no longer seeing ARM as a point solution to meet regulatory compliance requirements, but instead expanding into broader adoption as a method to automate and scale revenue accounting.

The market is beginning to realize that ARM products are responsible for much more than simply automating accounting at the end of the month or quarter – they give finance leaders the ability to influence operational decisions in real-time as business is transacted. For the most progressive CFOs and CAOs, ARM solutions are viewed as a proactive intelligence tool that enables decision-making during the financial quarter.  While some investment in ARM solutions is driven by a migration from legacy systems to cloud-based tools, the bulk of the spend is net-new spend for companies of all sizes as the ROI and business benefits justify significant investment in the category.

ARM Landscape and Timeline

Automated Revenue Management has evolved significantly over the years, driven by advancements in technology and data analytics. Here’s a brief landscape and timeline of its development:

1. Manual Approaches (Pre-Computer Era): Revenue management initially relied on manual methods, such as yield management, where pricing decisions were made based on historical data and basic forecasting.

2. Late 20th Century – Early 2000s: With the advent of computers and data analysis software, revenue management started to become more sophisticated. Airlines and hospitality industries were among the early adopters of automated revenue management systems, using them to optimize pricing and allocation of resources based on demand fluctuations.

3. Mid-2000s – Early 2010s: The integration of more advanced analytics, including predictive modeling and optimization algorithms, allowed businesses to fine-tune their revenue management strategies. This period saw expansion into other industries like car rentals, cruise lines, and retail.

4. Mid-2010s – Present: As big data and machine learning became more prevalent, automated revenue management systems further evolved. Companies began incorporating real-time data feeds, external factors (like weather or events), and dynamic pricing algorithms, leading to more adaptive and responsive strategies. Cloud computing and AI-driven solutions also facilitated easier implementation and scalability.

5. Current and Future Trends: The landscape of automated revenue management continues to evolve with an increasing focus on personalized pricing, omnichannel integration, and real-time decision-making. The rise of the subscription economy, online marketplaces, and sharing economy platforms has further fueled the demand for sophisticated revenue management solutions.

Throughout this timeline, the focus has shifted from simple yield management to a comprehensive approach encompassing demand forecasting, price optimization, inventory management, and distribution channel management. The landscape now includes a wide range of industries, from travel and hospitality to retail, entertainment, and beyond, with the goal of maximizing revenue and profitability through intelligent automation and data-driven insights.