Last updated on Friday, January 13, 2023
If there was any doubt about traditional annual financial forecasting practices being outdated, it’s been put to rest in the economic chaos of the last few years. Companies that rely on massive, complex, static spreadsheets fed by stale sales data simply can’t be agile enough to adjust quickly to the rapid changes we are continuing to see.
As the word “recession” creeps into our everyday conversations, it’s time to change how your revenue forecasting gets done. Demand and supply are both volatile, and that’s going to continue for the foreseeable future. Agility and accuracy are what will set the winners apart.
Ajit Kambil from Deloitte says it well:
“The COVID-19 pandemic exposed the shortcomings of traditional FP&A approaches, highlighting the rigidity of such rituals as quarterly planning and annual forecasting. It’s now clear that the function requires the technology and skills that will enhance flexibility and enable FP&A teams to execute dynamic plans and forecasts.”
The technology and skills Deloitte refers to exist today, and are accessible to any C-level executive. In this article, we’re going to explore why revenue forecasting needs to be rethought, the three pillars of correcting the problem, and exactly what you can do today to bring dynamic revenue forecasting into your organization.
In their conversations around dynamic forecasting, the experts at Bain, Deloitte, Accenture and the Revenue Enablement Institute reveal how complex forecasting has become, and how the uncertainty of the last few years has contributed. But while dramatic change is happening on a daily basis — drops and spikes in demand leading to extreme supply chain constraints — this is not the only thing making traditional forecasts miss the mark. Much of the problem also stems from gaps in the data.
Revenue comes in from many sources, including new bookings, run rate, channels and account expansions. But accurately forecasting with all those sources remains a challenge. Why? Because this data is siloed. It isn’t flowing to those who need it. It’s disconnected throughout the organization, and getting it to the right teams in time for the next revenue forecast isn’t feasible. FP&A teams must work with what they’ve got.
Traditional revenue forecasting also doesn’t take into consideration the dynamic nature of on-the-ground, customer-level changes in the contract or delivery schedule. If you’re relying on month-old data from your sales team and word-of-mouth updates on customer activity, you might not know that the customer decided to delay their project by two months. Or you might not know that the engineering team can’t deliver this month, but needs three more months than expected. All this data is critical to the accuracy of your revenue forecast, and you won’t find it in your spreadsheets.
These gaps in data and cumbersome spreadsheet processes wreak havoc at all levels of the organization.
For finance, sales, and sales operations, the biggest issue is usually time. It takes massive amounts of time to update, collect, and analyze data in spreadsheets.
50%
One of our customers shared with us that their sales team was spending 50% of their time updating schedules. That’s 50% of their time not making sales, just updating data in spreadsheets.
What would you do if you knew your sales team was spending this much time filling out spreadsheets when they should be growing your company? In fact, do you know how much time your sales team is spending providing input for revenue forecasting?
Now think about the time your finance and sales operations teams are spending requesting spreadsheets, pulling in the data from each of those spreadsheets into a master spreadsheet with formulas and calculations, and only then creating forecasts. By the time those forecasts are shared with the executive team, they’re stale at best — or unreliable at worst.
For the executives who own those forecasts, the traditional revenue forecasting process creates two areas of risk.
The first risk is around accuracy and reliability. With traditional revenue forecasting processes, it’s a struggle to get the big-picture view that you need of the business. You can’t see where you might be over-investing or losing revenue. Even if you have a strong working knowledge of those massive, complex spreadsheets, that insight isn’t easy to pass on to others.
The second area of risk is around visibility. With traditional forecasting, it’s impossible to break down the silos created by these spreadsheets, spot dangerous gaps, and get teams aligned. Your forecast spreadsheet is a “black box,” as one software CFO recently called it in conversation with us. Timeliness in spotting issues is key — and you can easily get blindsided when the data in the report is three weeks old.
The result is that the executive team is working from an inaccurate forecast, and that forecast is 1) difficult and risky to share with investors and stakeholders, and 2) impossible to use as a tool to identify where you’re off and adjust.
The CFO has never had more power and more responsibility to drive value than they do today. As the market volatility continues and customer behavior changes quickly, the CFO must bring greater accuracy and visibility to the whole organization for better decision making and continuous improvement.
How? By eliminating silos and driving the flow of data with dynamic revenue forecasting.
Dynamic revenue forecasting uses existing data and software to give you greater accuracy and agility in your revenue forecast. It also creates a flywheel of improved operational effectiveness by creating a virtuous cycle of continuous improvement.
Dynamic revenue forecasting is underpinned by three pillars:
Pillar 1: Collaboration
Hitting revenue goals requires every part of the organization to pull in the same direction. From field sales to operations to supply chain to finance, if there is a stumbling block or a gap in any part, it will have upstream and downstream effects. Collaboration is critical to identifying and solving challenges quickly.
Collaboration requires that critical data flows freely between teams. Massive, complex spreadsheets prevent data flow and obstruct cross-departmental collaboration. Dynamic revenue forecasting enables teams to align their processes so they can collaborate around the data and find solutions quickly to close the accuracy gap.
Pillar 2: Visibility
Business is dynamic by nature. Changes happen every minute to customer orders, delivery, supply, etc., and these changes impact the revenue that you can realize. To get an accurate revenue forecast, you need to identify the data sources that capture those changes, and ensure that critical data flows across the organization in a timely manner. What many executives don’t realize is that the CRM is the best place to look for customer-level changes. Getting real-time visibility to this data alone will make a huge difference in your forecast accuracy.
And when you have all your data flowing with a dynamic revenue forecasting process, it breaks down the silos and brings immediate visibility to all revenue sources and real-time changes, at all levels of the business — from sales to the CEO.
Pillar 3: See gaps and act on them
When a CFO doesn’t see expected money in the bank, they’re going to ask what happened. The traditional silo approach toward forecasting with a massive spreadsheet can’t answer that question. And until you know where the problem is coming from, you can’t fix it.
Dynamic revenue forecasting reveals the gaps — but more importantly, it reveals them as they happen. This puts your organization in a better position to solve problems, wherever they stem from, before they affect revenue.
As you identify and close gaps, it creates a cycle of continuous improvement. So as time goes on, you have fewer gaps, and a more accurate revenue forecast with less and less effort.
So how do you do it? How do you make dynamic revenue forecasting a reality?
The first thing you need to do is get your revenue data flowing. Not just pipeline and bookings data from your sales team, but on-account (run rate) revenue and revenue coming in from channels as well. If that seems daunting, just start with one data source and work your way through the rest. If all you do is break down the barriers between silos and get visibility to all your revenue data, you’ll achieve greater agility than your competitors who are still using traditional forecasting methods.
If your sales team is already using Salesforce as your CRM provider, you’re halfway there to making dynamic revenue forecasting a reality for your organization, because revVana works natively in Salesforce. With this single solution from the Salesforce AppExchange, you can auto-generate revenue forecasts from your sales data across all revenue layers from any object in Salesforce by embedding the logic your finance team uses today to forecast.
As the data changes, revVana automatically updates the numbers and translates the data so it can be managed by sales, finance, operations, and so on for forecasting. revVana provides a bridge to the data and processes between sales, finance and operations. This is going to give you much more insight into what’s happening across the organization, so you can act on it rapidly.
Outdated revenue forecasting methods put companies in peril. You need greater accuracy and real-time visibility to your data to avoid the risks.
Dynamic revenue forecasting is how your organization will gain the accuracy and agility to succeed in this increasingly volatile market. Traditional, static forecasts are by their very nature unreliable and inflexible — not to mention ripe for human error. When you bring visibility to the data and transparency to the processes within each department, your forecast accuracy will grow over time. And that grows cross-functional collaboration, trust in the forecast, and knowledge of what actions to take to head off problems before they happen.
You can use your existing data and software to make this happen for your organization. You just need the bridge between teams to enable collaboration, gain visibility, and take action when you see gaps.
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