Seven Ways To Ensure Your Revenue Forecast Matches Your Revenue Commitments In A Recurring Revenue Model

An organization’s ability to grow revenues has become more tied to firm value than at any time in our business lives.  That’s why most (53%) of boards are pushing their CEOs to repackage their products and services as subscription pricing models, usage-based models, or cloud-based offerings, according to a report by CFO Magazine.   Almost every business that sells “on premises” technology, equipment or software is moving to a cloud model according to Gartner.  Any business that can pull it off – including industrial firms like Honeywell, automotive firms like Audi, hardware firms like Avaya, and infrastructure like Flexential – is trying to move to recurring revenues.

This makes good business sense. Adding a robust recurring revenue stream can make a business more valuable, profitable, predictable, and scalable. For example,  the average mature SaaS business grows at 19% which supports double-digit earnings multiples and stable cash flow while covering large, fixed costs.  It also makes them more valuable. For instance,  – a SaaS business with double-digit growth rates and recurring revenue streams – has a price to earnings valuation that is triple the S&P 500 average.

The problem is moving from a revenue model based on a traditional “pay per product” selling approach to a subscription model is not as simple as it sounds. Executives who are in the process of transitioning to these recurring revenue streams are running into a range of issues. These include managing cash flow in the short term, reliably forecasting future revenues to investors, and producing operational forecasts to guide the sales resource allocation, production capacity and fulfillment staffing required for future growth.

This is because moving into the subscription economy forces organizations to rethink and redefine the way revenue is recognized, realized, and forecasted. It also changes how functional leaders communicate cross the lead-to-cash cycle. Based on my experience leading and enabling fast growing SaaS / IoT businesses – there are several ways CEOs, CFOs and commercial leaders are going to have to adapt their commercial practices if they expect to participate profitably in the subscription economy.

1. Clearly define revenue to the entire go-to-market team. Moving from transactional to recurring revenue streams forces a business to fundamentally change the way they define, calculate, book, forecast and recognize revenues. This is because moving to a recurring revenue model forces sales leaders to recalculate the value and potential of a customer based on their loyalty, cross sell potential, and the number of users or use cases that can generate consumption. In theory this makes common sense. But in practice, it’s surprisingly difficult for the leaders of finance, marketing, sales and success – operating in silos – to agree on the value of a customer, the size of an opportunity, or the definition of revenue. It’s even harder to calculate and quantify revenues and account potential in a reliable and fact-based way that front line sales managers and their teams will trust to set goals, allocate resources and drive incentives and compensation. As your organization transitions from selling tangible goods to subscriptions, your commercial and financial leaders will be forced to clearly quantify and define customer lifetime value, ARR and Net Recurring Revenues as the “north star” for all go-to-market functions to follow. This is essential to creating incentives that motivate your revenue teams to work together to maximize revenues in a recurring model. Creating this common purpose fosters the cooperation and information sharing across functional silos that is essential to revenue recognition, realization and forecasting in a recurring revenue model. “Aligning our incentives and KPIs with our overall objectives is extremely important to achieving our growth goals, especially as we move from a product company to a SaaS / cloud company,” reports Jim Chirico, the CEO of Avaya, who has successfully transitioned Avaya from a on-premised hardware business to one where software and services represent the majority of total revenue and over two thirds of revenues are now recurring.

2. Move beyond forecasts based on booking alone.  When you book a big deal in a traditional “capex” or “pay per product” model – the revenues show up in a big sales quarter reflecting material revenue lift the CFO can report to investors. When you “book” on an opportunity you have been tracking, it usually results into revenue and cash flow that will be recognized within the current period. So opportunity, revenue and cash flow forecasts based on booking are a fair approximation of what is actually coming in. This is not the case in a subscription model.  The definitions of revenue, opportunity size, and account potential based on bookings will not work. There are too many variables and confounding factors involved in realizing those revenues. The percentage of a multi-year recurring contract or subscription that are recognized in the current quarter is far smaller. Revenue recognition is far more dependent on actions and activities that happen after a revenue commitment is made. CFOs can significantly improve revenue realization and forecast reliability on committed new business by factoring in customer specific and post-booking variables that impact revenue ramp into their revenue recognition rules and yield calculations.  This is because as new business ramps up in consumption-based business models, factors like training, delivery, onboarding, client scheduling, service issues and user adoption can all dramatically change. Most forecasting systems only look at commitments at time of sale and fail to capture or manage these critical post-transaction operational processes.

3. Incorporate more customer engagement and success data into the forecasting process.  The information needed to better anticipate and forecast ramp and run rate revenues over time exists in most organizations. The problem is most finance teams don’t use inputs from sales after the commitment is made. The general practice is to use models and estimates to predict revenue yield and realization over time based on product type and generalized customer parameters. By factoring in more post transaction data from customer success, services, product usage and account management – Revenue Operations and Finance teams can create more reliable estimates of how revenues will ramp, when they will be realized over time, and what “run-rate” revenues look like as customers reach steady state. To more accurately forecast future recurring revenues, finance leaders need to access valuable data and knowledge about customer consumption, schedules, onboarding, adoption and deployment that materially impacts when and if revenues will be recognized in the future. They can get this information from customer success teams who have day-to-day contact with clients on product delivery, onboarding, installation, service issues, and adoption and usage of products. Account teams have visibility into implementation timetables and rollout schedules. Salespeople understand when a deal is likely to close, how large budgets will be, the size and composition of deals, the pricing and terms negotiated, and how those variables may slip or change over time. They are starting with the most critical – information from sellers at the edge of the organization in CRM and the post transaction information from Customer Service and Success teams. The most efficient organizations are recoding this core information streams into CRM opportunities to create a reliable fact base finance can draw upon for forecasting.  Over time, there are many additional data sources that can provide much more granular and reliable information about future revenues. These include but are not limited to: IoT data from products that show usage, first party marketing data from customer facing systems that provide signals of intent and interest, customer engagement data from enablement systems, pricing and contract terms from RFI and CPQ systems.

4. Take an expanded view of the entire lead-to-cash cycle.  Converting leads into cash flow in a recurring revenue model requires a much tighter set of information and coordinated set of motions all along the revenue cycle –  including demand generation, opportunity development, value selling as well as customer retention and expansion. The consumption phase of the revenue cycle is particularly important because that is where value and revenue are realized. Client onboarding, set up, education, training, adoption, service resolution and ultimately cross sell, upsell and retention are all critical and discrete steps that determine revenue performance and profitability. While sales may “book” a transaction in a traditional sale, it is increasingly the customer service and success teams that manage the bulk of this post transaction engagement data in a recurring revenue model. This requires more “horizontal information sharing” and coordination across the revenue team. This is forcing marketing, sales, operations, and finance organizations to rethink and reengineer how they manage their lead-to-cash cycle.

5. Stop using spreadsheets and automate more of your revenue recognition processes. You can’t do revenue recognition on a spreadsheet in a subscription model. There are too many more variables, opportunities for errors, and more frequent changes in usage, users, adoption, plans and options to consider. They all happen and hit the P&L faster. The crux of the problem is that all of these variables are unique to each customer contract – so estimating revenue recognition and yield based on baseline assumptions becomes a significant source of error. These problems are compounded by the amount of work involved in making updates and changes to those estimates based on delivery failures, schedule slips, or onboarding issues – given the volume of usage and consumption variables and the frequency of events that impact revenue timing and recognition. In our experience, when a business migrates to a recurring revenue model it often “breaks the back” of manual, time-intensive planning processes that are managed in spreadsheets that are “lobbed” between silos. Progressive firms are finding ways to automate the capture and automation of information from systems they already have – notably CRM – to get better information on pre and post-transaction events from customer facing teams. This reduces the efforts required from the field to create and update data as well as providing a complete view of all data vs. a subsegment of information used in the current approach.

6. Foster more collaboration and information sharing  across the entire revenue cycle. Moving to a recurring revenue model forces organizations to manage the entire revenue cycle – from demand generation through purchase, consumption, renewal and expansion – as one coherent motion. This flies in the face of the fractured way most organizations are set up, where marketing generates demand, sales drives the purchase, and a rapidly maturing customer service and success function manages the lion’s share of customer engagement and value creation. This uncoordinated management of the revenue cycle causes the booked revenue commitments to leak and shrink over time. These “self-inflicted wounds” include the failure to enforce good contracting discipline, respond to customer adoption issues, or communicate changes in delivery, training, onboarding and rollout schedules to finance teams. This is more of an organizational problem than a technical one. Finance teams can dramatically improve their visibility and predictability by working to share information impacting revenue ramp, run rate, risks and changes across sales, fulfillment, operations, and customer success siloes more effectively.

7. Improve your ability to dynamically forecast future revenues. As companies transition a portion of their revenues from traditional to recurring revenues, the CFO must dance a fine line between showing investors ever increasing Annual Recurring Revenues (ARR), while ensuring cash and profits do not take a big hit in the current quarter.  This makes it critical to have much better visibility into future revenues and airtight revenue recognition systems than conventional forecasting processes and systems can deliver. Dynamic Forecasting utilizes existing data and knowledge from the field and operations teams and systems to expand the inputs and improve the outcomes of existing sales forecasts. It expands the inputs to forecasts beyond commitment size and bookings – to factor in the pre-and post-transaction variables that impact the ramp and finance-based recognition forecasts of total expected revenue over time. This provides finance better visibility into changes to committed business in flight as well as the expansion or contraction of existing business on account. Dynamic Forecasting also significantly improves the outcomes of forecasting by creating a feedback loop between the revenue recognition, realization and forecasts generated by the financial planning and analysis teams and the upstream product, sales and production teams generate revenues.

You can learn more about this important management discipline by participating in the Dynamic Forecasting Research Initiative our faculty has undertaken to help CEOs, CFOs, CXOs and operations executives develop a faster, smarter, and more data-driven approach to generating growth plans and revenue forecasts. Growth leaders, operations executives who want to participate in the research and get access to the findings can learn more at this link and arrange a briefing by contacting us directly using this form.


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