A simple trick for companies to accurately predict their annual revenue

July is an important month for online subscription businesses, and in particular for cloud businesses (July should really be called “International Cloud Computing Month”). For any business, being able to accurately forecast revenues is key for setting the hiring plan, sales and marketing spend, and a host of other concerns – and, crucially, for setting expectations with investors.
Yet while all businesses do their best to forecast GAAP revenue, and adjust their plans accordingly, there are dozens of factors that typically can make it hard for technology businesses to make accurate predictions – for starters, growth rates, seasonality, bookings terms and revenue recognition policies.
Part of the beauty of subscription businesses, though, is that you have uncanny visibility into revenues. How? It turns out that with just one number – your monthly subscription revenue in July – you can tell what your GAAP subscription revenue is going to be for the year with a very high degree of accuracy.
Just take your July revenue and multiply it by 12.  Or if you want to get even trickier, take your daily revenue on July 15 and multiply it by 365.
They’re both embarrassingly simple, but surprisingly accurate. For a subscription business with a consistent trajectory, it’ll get you extremely close to the ultimate answer – usually within a couple percentage points.
Take, for example, Company A, which starts the year on January 1 with Monthly Recurring Revenues (MRR) of $1 million, and is growing at a respectable 50 percent year over year. Play that forward, and the company exits the year with $1.5 million in MRR, or $18 million in Annual Recurring Revenues (ARR = MRR x 12) and GAAP revenues of $15.1 million.
Now take Company B, which is growing at 200 percent year over year (a very different growth profile). Suppose Company B starts the year behind Company A, with only $660,000 MRR on January 1st. Because of its faster growth rate, Company B will end the year at a much higher MRR and ARR than Company A – setting up a significantly larger 2014.
Despite very different starting and end points, however, when Company B passes Company A in July, not only do they have (roughly) the same ARR, but their ARR also roughly equals their 2013 subscription GAAP revenue of $15.1 million.  (To help show why, we plotted the same data in the following chart).
We picked even numbers to make the example as intuitive as possible, but feel free to run the “July test” with any starting point numbers and growth rates you want, and you’ll get similar results. As a firm with over 65 cloud computing investments over the past dozen years, we’ve run it many times ourselves and always find it surprisingly, amazingly useful.
What if your recurring revenue business looks like legacy enterprise software and you experience significant Q4 seasonality?  As long as your implementation periods are short, it still works pretty darn well. And what if your company is a cloud business, but charges on a transactional model instead of a subscription model? Well, it still works, as long as your business isn’t highly volatile or seasonal, in which case you’ll just need to overlay some rough tweaks (i.e. forecast the volatile accounts separately from the more stable base) to the gross estimate.
In short, one of the best things about subscriptions businesses and cloud businesses especially is that they are predictable. If your model includes a heavy mix of hardware, license sales, or professional services, you’re still going to need to forecast those elements the hard way.
Of course, even if you’re purely subscription-based, true planning and forecasting requires a lot more than a rough revenue estimate (which is why companies like Adaptive Planning exist; note: my firm, BVP, is an investor) but “July x 12” is the best financial trick we’ve seen to approximate the top line number.
Byron Deeter is a partner, and Peter Lee is senior associate, at Bessemer Venture Partners, a global venture capital firm with offices in Silicon Valley, Cambridge, Mass., New York, Mumbai, Bangalore and Herzliya, Israel.
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