A More Agile Approach To Forecasting and Realizing Predictable Growth In A Modern Commercial Model

CEOs are under pressure from their boards and shareholders to ensure predictable growth, a critical component to increasing the value of their enterprises. This has led most business-to-business organizations to pursue longer term contracts and repackage their offerings in the form of different pricing and revenue models to closer align with their customers and generate more stable revenue streams.

The increased complexity of these revenue models – and the uncertainty of planning several years in the future – has made the ability to forecast effectively an even greater challenge. CEOs, CFOs and operations leaders now have to manage a mix of new business revenue streams. These can include a blend of subscriptions, consumption, time-based projects and one-time sales. At the same time they have to forecast their existing books of business – which include run-rate, on-account and seasonally adjusted orders.  This makes it difficult to provide management and investors the visibility into future demand, revenues, capacity and resource requirements they need to effectively manage profitable growth.

The traditional way most organizations generate forecasts is proving to be too slow, too labor intensive and unresponsive to changing customer dynamics. They are also blind to most of the post commitment variables that can impact future revenue realization, production needs and resource allocation decisions. This lack of timely and accurate information in these forecasts make it nearly impossible for production, sales and success teams to adjust operationally to gaps in expectations.

These dynamics are “breaking the back” of outdated forecasting processes that are based in silos, built on bookings data, managed on spreadsheets, and driven by the fiscal calendar. This has real financial consequences.

  • In the short term, the yield on committed revenue is reduced as CFOs are experiencing a revenue realization gap of up to 50% of expectation due to lack of operational alignment;
  • From a downstream perspective, CFOs are unable to provide reliable revenue forecasts to investors which hurts the share price;
  • From an upstream perspective, the uncertainty about future revenue leads to over or under utilization of production and selling capacity and lowers the return on assets.

Managers need to get faster,, smarter, and more data-driven in the way they generate forecasts. That is why Dynamic Forecasting has emerged as a critical need for CEOs, CFOs and growth leaders.

What is Dynamic Forecasting and why is it so important?

The concept of Dynamic Forecasting is to align the organizations’ processes and data across the core business groups to make forecasting more agile, transparent and precise over time.  Dynamic Forecasting aligns, operationalizes, and automates forecasting. It effectively moves the forecasting process from a calendar-based approach to an “always on” skill set that becomes a day-to-day management tool.

Dynamic Forecasting differs from bookings-based sales forecasting efforts two ways. It looks at the key activities and outcomes upstream and downstream of the sales transaction. And it links the supply chain to the demand chain to the future revenue estimates that form the basis of firm valuation. 

Dynamic Forecasting utilizes existing data, systems and knowledge from the field and operations teams to both expand the inputs and improve the outcomes of existing sales forecasts. It goes beyond commitment size and bookings – to factor in the pre-and post-transaction variables that impact the ramp and finance-based recognition of total expected revenue over time. This provides finance with better visibility into changes to the committed business in-flight as well as the expansion or contraction of existing business on account. It factors in multiple data sources including pre-transaction signals of customer intent and close rates along with post-transaction data from customer success, services, product usage and account management. This helps Revenue Operations and Finance teams create more reliable estimates of how revenues will ramp, when they will be realized over time, and what “run-rate” revenue will be as customers reach steady state. 

Dynamic Forecasting also significantly improves the outcomes of forecasting by creating an active feedback loop. This creates better “connective tissue” that links the financial planning and analysis teams that report revenues – to the upstream product, sales, and production teams that generate revenues. The feedback loop starts by automating more granular revenue forecasts and updating them with customer activity and success data to create more timely and ever more precise forecasts. The loop is closed by providing questions, alerts, and plan adjustments to the upstream product, sales and production teams that expose operational gaps. This helps them take actions to eliminate revenue leakage, variances and shrinkage with better planning, contracting and execution. It also gives leadership teams greater visibility into the operational variables in both production and the demand chain. Better response to changes in expectations unlocks more predictable growth from the revenue chain by eliminating the leakage and “slack” built into it to buffer against uncertainties, variances, and inefficiencies inherent to running the business.

Dynamic Forecasting makes financial sense because it not only improves the yield on current revenue, but also addresses the upstream and downstream financial impacts of unreliable revenue – which are even more severe. For example.

  • From a demand chain perspective, CFOs are experiencing a revenue realization gap of up to 50% on committed new business because they cannot see or anticipate the variables that impact revenue ramp.  This is because as new business ramps up, factors like training, delivery, onboarding, client scheduling, service issues and user adoption can all dramatically change when revenues are realized and recognized. Most forecasting systems only look at commitments at time of sale and fail to capture or manage these critical post-transaction operational processes.
  • From a downstream perspective, the firm’s value is diminished because CFOs are unable to provide timely and accurate revenue forecasts to the business or investors. This inability to create reliable forecasts makes it hard for CFOs to reliably communicate future revenue to investors. When earnings “miss” consistently and investors lose confidence in future revenue, it can cause a material impact on firm value and share prices. Commonly used Sales-driven metrics such as Annual Recurring Revenues and Total Contract Value (TCV) are based largely on “bookings”. Which means they don’t factor in changes in demand, usage, consumption, rollout schedules, and delivery failures that can impact accuracy.  Without better information on client actions deeper in the revenue cycle – from customer success, customer service, and account management teams at the front lines – CFOs and CROs are largely flying blind.
  • From an upstream perspective, all executive leadership must be mindful of the impact of these bookings estimates on company resources. With most sales forecasts are focused on new business in a given quarter and do not take into account the time delay of first transaction or the ramp up that often occurs to produce revenue. This makes it more difficult to use this data reliably when it comes to allocation of production, other capacity, or even the supply chain, to deliver the revenue generating value that is in the new contracts. A greater challenge is in predicting ongoing revenue from existing relationships which is often not formally forecasted as they too impact demand and other capacity planning.

What can leaders do to become more agile, transparent, and precise?

Progressive business-to-business firms are taking several concrete steps to move from the existing forecasting approach to one that is more dynamic through:

1. Automating the forecasting process. Manual and spreadsheet-based forecasting processes are a big part of the problem. They are too slow, too labor intensive and inflexible to manage the length and complexity of long term contracts.  This labor-intensive approach hurts your ability to plan, recognize, realize, and forecast future revenues over time.  For example, managing the unique revenue attributes of 1,000 contracts using a spreadsheet based process leaves little time for analysis teams to actually analyze the data. Data entered into the spreadsheets is already outdated because in the real world, it has already changed. Making updates and changes to those estimates based on delivery failures, schedule slips, or onboarding issues is next to impossible given the large number of variables in large contracts – and the frequency of events that impact revenue timing and recognition. Progressive firms are finding ways to automate the capture of information directly from systems they already have – notably the CRM system – to get better information on pre- and post-transaction events. Updates from customer facing teams are captured in real-time. This reduces the effort required from the field to create and update data to support forecasts. It also provides a more a complete and dynamic view of more of the data needed to recognize revenue  vs. the relatively small subsegment of information used in the current approach.

2. Better alignment and collaboration across the revenue team with finance and operations. Reliable planning and forecasting of revenue is much more of a collaboration and organizational issue than it is a technical or systems integration issue. That is why Revenue Operations – the alignment of revenue teams with the operations and processes that support them – is so critical to effectively managing, forecasting, and realizing future revenue in a modern commercial model. This is because forecasting requires a variety of inputs to be accurate. Upstream inputs from product, pricing and contracting teams materially. Information about account activity must flow vertically from front line customer facing employees to finance at the top of the organization. And information needs to move between revenue operations and finance to marry knowledge of historical buying and revenue patterns with actual customer consumption and delivery information. The resulting more accurate view provides longer term horizons for operations to plan for demand, further strengthening the alignment of the organization and optimization of resources. 

3. Becoming more data driven.  Many organizations struggle to anticipate new customer ramp-up or existing relationship run-rate revenue even though they are likely to have the information to do so. For example, Customer Success teams have day-to-day contact with clients on product delivery, onboarding, installation, service issues, adoption and usage of products. Account teams have visibility into the likelihood of expansion, contraction or churn. Salespeople understand when a deal is likely to close as well as the size and composition of the transaction, and how those variables may slip or change over time.

The most critical data needs to come from those closest to the customer at the edge of the organization. Most of that information can be captured and stored in the CRM system. Smart organizations are aggregating those information streams with CRM and using it to better forecast their revenue. This is only the start. Over time, there are many additional data sources that can provide much more granular and reliable information about future revenue. 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. Create feedback loops along the Revenue Chain. A new generation of growth leaders – CXOs – with more direct control over the entire revenue cycle are expanding the aperture of Revenue Intelligence. They are incorporating more upstream revenue and operational inputs as well as downstream diagnosis and remediation into their forecasting efforts. They are tasking their operations teams to better connect the dots across functions and systems to create a “feedback loop” that connects finance, sales and production teams. This initiates the process of continuous process improvement process. This usually starts by asking how much the revenue forecast was off – but also why the numbers changed. By continually unpacking the root causes of unreliable revenue ramp and run rate – teams start to identify the internal and external factors leading to the problem.  These can be fixed immediately.  Over time, these questions will lead to more reliable information feeds on other external factors impacting the forecast, such as better inputs on client budgets, timetables and training schedules.

For example, Konica Minolta Business Solutions is driving new growth in the rapidly expanding market for IT services, business process automation, and analytics that support the digital workforce. To do this they expanded their product and solutions portfolio and introducing recurring revenues. This compelled their Chief Operating Officer – Sam Errigo – to put in place systems and processes to simplify the selling, pricing, and booking of complex solutions bundles and subscription services that are driving margin and monthly recurring revenues. “Recurring service revenue in our direct sales channel increased to 43% for new contracts this year,” says Errigo. As a consequence, his team had to completely automate the workflow process and upgraded their systems to allow them to combine many solutions into bundles and subscription services using any basis for billing a service, from the amount of storage used to usage, per employee fee, flat rate or variable based on customer requirements.  “We’ve made it easier for our sales organization to execute the quote to order – from configuring solutions, quoting prices, delivering proposals, to providing subscription-based billing for our business,” he continues.

To better understand these issues, and identify best practices to address them, the faculty of the Revenue Enablement Institute is leading a research initiative on Dynamic Forecasting.  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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