July 8, 2008...8:47 pm

What is your revenue formula? Part I

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Sean Macnew is our first guest blogger.  Sean recently joined Sugar Inc. (www.sugarinc.com) as Chief Financial Officer.  Prior to Sugar Inc., Sean spent five years at Symantec and VERITAS Software in M&A, business development, finance and operating roles. He was most recently Senior Director and General Manager of the NetBackup RealTime product group. Sean was also an early employee of Epinions, Inc., a consumer review powered shopping website acquired by Ebay, and worked as an investment banker for Goldman Sachs and Alex. Brown & Sons. He received a B.A. in Computer Science from Dartmouth College and an MBA from Stanford Business School, where he was an Arjay Miller Scholar.

I just drove up to Mendocino for the Fourth. With my rapidly growing family and dog, we wound up taking both our Prius and our Subaru Outback. The Outback is no SUV, but I still used twice the gas compared to the Prius to get there despite driving the same speed. I painfully experienced in the wallet one of the key points I’d like to make in this post: focus on the right metrics and leverage points (MPG in my case) and you will achieve results more efficiently. For a start-up, if you are twice as efficient as your competitor in generating revenue, that means more money to spend on product or marketing and less time spent refilling the gas tank with VC money at way higher than $4.50 a gallon!

The revenue formula is a great tool that entrepreneurs can use to think through their business models and identify the key metrics they should manage to grow revenues efficiently. The concept applies to any industry, but web businesses give you the data and metrics you need to dial in your revenue formula faster. Getting your whole organization to understand your revenue formula will lead to a focus on revenue efficiency, higher operating margins and more visibility into your growth and how to manage it.

The Formula

For those of you that were management consultants, the revenue formula should be a well known framework:

Revenue = Price * Quantity

From that simple equation, a good consultant can generate a six month engagement and a powerpoint deck that breaks your inbox. It is just a framework, however, and the real value comes from how you apply it to your business model to discover your key operating levers and metrics. The goal is to replace Price and Quantity with the relevant variables for your business in enough detail to provide insight.

First, let’s talk about the formula and what it implies. From a math standpoint, revenue is just multiplying two numbers together. That means that any percentage change in either Price or Quantity is going to yield the same percentage change in revenue. Both are of equal importance, but need to optimized together to maximize total revenue. The obvious example is that raising price in most industries will decrease quantity sold and lowering price will increase quantity sold. In theory, the market sets the price for a certain class of products or services, leaving the entrepreneur the decision of what class of product/service to sell and how to optimize quantity. In reality, you have a lot of control of both variables, particularly in new markets.

Ecommerce

Let’s make things more concrete by looking at an ecommerce website. A simple revenue formula would be:

Revenue = Unique Visitors * Purchase Rate % * Number of items bought * Average Purchase Price

This is very straightforward and should make perfect sense to anyone who has sold anything online or in the real world. The more people that come to the store, the more often they buy and the more they spend all translate into higher revenues.

Laying out the revenue formula at this level of detail lets an entrepreneur look at each key metric in isolation as well as assess the impact of changes in one metric on the entire equation. Historical results can easily be analyzed by this formula, but It also is a terrific tool to build accurate forecast models using a controllable number of assumptions.

Plugging real numbers into the formula helps us draw some insights. I put together a simple spreadsheet with some example numbers and the ability to do analysis. It should demonstrate for any remaining skeptics that the same percentage increase in any variable will generate the same impact in terms of revenue.

Click here for the Zoho spreadsheet example of a simple revenue formula for ecommerce.

Let’s go through the components of this revenue formula to see what it can tell us mathematically:

1.  Unique visitors - Any commerce business puts a high priority on getting customers to visit their store and this number is always scrutinized.  Somewhat surprisingly, unique visitors is actually the least leveraged variable in the revenue formula because it is the largest component. The law of large numbers comes into play quickly and unique visitors in particular tend to be very expensive to increase inorganically.

2.  Purchase Rate % - Purchase rate is the smallest number in the formula, but the most important metric for commerce sites. For example, is it cheaper for you to buy advertising to double your traffic, or instead figure out how to double a weak visitor purchase rate by improving user experience, merchandising and pricing? Understanding your purchase rate, how it compares to your peers and how high you can drive it will provide the most leverage in your revenue formula. This variable is so important that in more sophisticated revenue formulas it is worth breaking out into a more detailed set of metrics (e.g. % of customers that create a shopping cart, % of shopping carts that get purchased) to better track of what is going on after a customer arrives.

3.  Number of items bought - The bigger the shopping cart, the larger the revenue. Amazon got this and was one of the first to offer free shipping for orders over a certain amount and aggressively push related items other customers bought. Going from buying 1 item on average to buying 1.1 items on average increases your revenue by 10% with no extra traffic required! The purchase point is still the most effective time to try and get more revenue and margin out of a visitor. Look at your supermarket checkout aisle for real world inspiration. Upsell. Cross-sell. You want fries with that?

4.  Average Purchase Price - Price is the second biggest number in this formula after unique visitors. It also tends to not be as leveraged or as optimizable as you might initially think. Shopping engines and search have made pricing a commodity and generally you have to price competitively unless you are doing a promotion to get rid of inventory. As Amazon also found out, consumers often rebel at price discrimination where you overtly charge different prices to different people.  With promos and coupon codes there are levers that might help drive visitors or more commonly incent bundled purchases to go after #3 above.

When you use real numbers, such as in my spreadsheet, it becomes obvious that the smallest numbers have the biggest impact and are the leverage points. Math geeks can look at the rate of change of revenue for each variable to prove it using derivatives. The big numbers (unique visitors and purchase price) are important, but you will get less leverage from improving them in isolation. For example, growing unique visitors from 1 million to 1.1 million might have the same impact on revenue as growing the purchase rate from 1% to 1.1%, but it probably will be cheaper and more sustainable to do the latter. Even assuming flat visitor growth, you can grow your revenues and increase margins significantly by focusing on purchase rate and order size. Then when you want to advertise, you can profitably pay more to get the traffic you need because you monetize more efficiently.

Instead of just managing total revenues or expenses as the goal, focus on the efficiency of obtaining revenues and use the revenue formula to measure performance against that as well. Most well run companies already do this, whether explicitly or not. Smart merchants track purchase rate and # of items purchased instead of just total revenue for their category. Good marketers track not only the cost per visitor they pay, but look for targeted visitors that will have a higher purchase rate or buy more expensive items once on the website. Decisions at a company rarely affect just one of these variables, so you need to get everyone thinking about how they can increase revenue by impacting as many leverage points as possible in everything they do.

Burning your revenue formula into the minds of your employees can make this kind of thinking standard and guide operational and strategic decisions. Many companies I know of drown in data and suffer from metric overload. The focusing effect of a clear, well understand revenue formula can help ensure alignment from the board down to senior management. Metrics like revenue per visitor are a great gauge of your revenue efficiency, but without a good understanding of the revenue formula that drives that metric, you will be shooting in the dark when you try to improve it.

In a follow-up post I’ll share some other examples of revenue formulas, including online content (advertising) and software.

Some Takeaways

1. Build a custom revenue formula for your business to identify key metrics and variables that are important to your revenue efficiency. Stick to the basic framework (P*Q) and make sure the variables multiply!

2. Focus on improving the the leverage points: small numbers that have a big impact on revenues!

3. Use your revenue formula both for building forecasts and reviewing performance.

4. Make your revenue formula known internally so the entire company can align activities around revenue efficiency.

5. Revenue formulas make monetization metrics like revenue per visitor actionable.

6. Sit back and enjoy improved margins, more accurate forecasting and a better framework for making resource decisions!

5 Comments

  • This is a very useful way to think about things because it puts the emphasis on flow — revenue flow.

    Here’s a suggestion though — it would be easier to see how things are either growing or decaying by looking not only at the time-series information contained in your formula, but also the frequency information.

    In mathematical terms, that means taking the Fourier transform of your function R = P * Q

    Why should you take the extra time to do this?

    Because if you look only at the size of the numbers in your formula, you might be focusing on the wrong thing!

    For example, maybe purchase rate % is the smallest factor in the revenue as a function of time, and increasing it will lead to more revenue over the next period. But if you look in frequency-space, you’ll see how if you push the rate beyond a certain frequency, then you’ll see the overall frequency of your revenue cycles decrease when you’ve reached a point of diminishing marginal utility.

    In other words, your revenue optimization needs to be optimized in both time and frequency in order to achieve the most sustainable long-term growth.

  • Actually, this gets even better. If you assigned each frequency component of your revenue stream to an axis — say price as a function of frequency on x-axis and quantity as a function of frequency on y-axis — then what you’d see is a surface, a big lump. That’s what a physicist calls “phase space” and it tells you at a glance a whole lot about the stability of your system.

  • I’ve elaborated a bit on my blog. Sorry for the multiple posts. You sort of pushed a button.

    http://brokensymmetry.typepad.com/broken_symmetry/2008/07/dynamic-account.html

  • Very cool Michael. I just read your post. I agree completely on time-series average views of data and how that can distort your view on results. I think particularly for a high transaction rate, market sensitive business this kind of analysis could be extremely insightful.

    Finding the diminishing marginal returns points for your key variables is the toughest and your suggestion of using phase space plots is awesome!

  • @smacnew

    Glad you found it useful.

    One quick reply on the use with “high transaction rate” cashflows — what’s a “high” rate will depend on both the size of the transaction and the length of the period over which you’re doing your optimization. In other words, it might not make sense to be looking at the frequency spectrum within a daily time window, even when multiple transactions occur per day, if what you’re trying to do is optimize the revenue flow over a ten year period.

    The point is that the frequency spectrum for a cash flow is just another way to look at how a firm is performing. It doesn’t tell managers what strategy will work best; it just gives them more information about how decisions made in the past are playing out in terms of revenue flow (in both time and frequency).

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