Jonathan Ginter is coach, speaker, and a veteran of commercial enterprise software, having helpied to design, build, and guide solutions sold to many of the Global Fortune 1000. He is currently the founder of the The Dream Hall, an organization dedicated to helping people make the leap into entrepreneurship.

One of the most crucial elements of any business, even a non-profit, is to determine whether the business you want to build is able to make enough revenue to pay its bills. If your business is already operational, you can always look at past performance and see if you can spot a trend (preferably, an upward trend). But how can you make such a prediction if you are still in the planning phase? The answer is to build a revenue model.

Revenue models are composed of revenue streams that are driven by various assumptions, each one a primary actor in your ability to make revenue.

  1. Price
  2. Conversion rate
  3. Lifespan of the customer
  4. Seasonal variations
  5. Reseller rates
  6. Annual growth rate

By combining all of these factors with your revenue streams, you can create a high-quality model that lays out how much you will earn month-by-month over the next 3 – 5 years. More importantly, a good model will…

  • …help you find weaknesses in your business
  • …contribute to your cash flow prediction

Cash Flow predictions are the end game, since they help predict when you will finally start to break even, how much investment you will need, and when you will be able to pay off those investment debts. Cash Flow prediction will be the subject of another article.

The end game of any financial projection is to predict the cash flow

Revenue streams

Every different source of revenue is a separate “stream”. Let’s imagine that you sell refrigerators. You will likely offer maintenance for a monthly fee. In addition, assume that anyone who walks in looking for an oven is referred to a partner store, which gives you a fee for each referral. Here are your different revenue streams…

  • Refrigerators
  • Maintenance contracts
  • Referral fees

These are treated differently because their prices are different and the number of customers that will opt into each one is different as well. Not everyone that buys a fridge will take a maintenance contract, for example. In order to build a proper model, you have to control each one separately with different assumptions.

There is no end to the number of revenue streams you might have. You might sell other products, such as microwaves. Or you might offer monthly memberships in exchange for discounts. Every new source of income is a revenue stream. The goal is to identify them and then give each one its own set of assumptions.

Every source of income is a revenue stream with its own assumptions

Assumption 1: Price

This is fairly simple. Each revenue stream is focused on something that has a predetermined price. You just need to determine that amount. Pricing will be the subject of another article.

NOTE: if you also offer discounts, then you are creating more than one price for this item. In that case, each combination of product and price becomes its own revenue stream so that you can make different assumptions about the number of customers buying that product at that price.

Assumption 2: Conversion Rate

Conversion rates predict the percentage of people that will convert from a non-customer to a customer when given the chance to do so. For example, if your marketing is able to get 1000 people into your store and you manage to get 250 of them to buy something, then you have a conversion rate of 25%. Each revenue stream should have its own conversion rate assumption.

So what is a good conversion rate? This may come as a shock, but 5% is actually good. If you get above 10%, you are doing really well. Anything above 20% and you are a god. A new business should assume 2%. As your marketing gets better, you can expect this number to get higher but that will take time.

Any new product or service should assume a 2% conversion rate

Assumption 3: Customer Lifespan

The customer lifespan is an often overlooked element in modelling your revenue. It is designed to answer a simple question…

  • If you are selling monthly subscriptions – how long before the customer cancels the subscription? This is also known as “churn”.
  • If you are making one-time sales – how long before I can sell another product to the same customer?

Let’s take cars as an example. If I sell someone a car, I don’t really expect them to come back for another car for at least 8 years. However, if I lease them a car, I can expect that they will drop the lease after 3 years of monthly payments (standard length of a lease agreement).

The importance of this assumption is that your model must adapt to the loss of revenue. For example, if I sell 50 subscriptions in January and my lifecycle assumption is 10 months, then I have to drop the revenue from those 50 descriptions in November, 10 months after they started.

NOTE: some people like to use a “churn rate“, which is expressed as the percentage of customers that will drop your service each month (typically set at 3% – 7%). I’m not crazy about this particular metric because I feel that the results it produces are fairly arbitrary. The lifecycle approach forces you to get to know your user’s mindset much more clearly.

Lifespan measures how long a customer will continue to pay their subscription

Assumption 4: Seasonal Variations

Seasonal variations are a global assumption that apply to every revenue stream equally. They are an admission that your product or service might not always be in high demand all year round. Most businesses experience months that are much higher or lower than the average. This reflects the changes in customers’ behaviour due to weather, holidays, school semesters, or other external factors.

For example, Martha Stewart’s site gets most of its traffic leading up to a holiday, like Christmas or Easter. However, her blowout month is November because of American Thanksgiving. Similarly, any business that is selling to other businesses or corporations knows that July and August are dead months because of vacations, and that December and January are dead months because of Christmas and annual budget discussions.

As you create your month-by-month predictions, your assumptions will determine the revenue for a “standard” month. Then you apply the seasonal variation for that month, expressed as a percentage increase or decrease – e.g., +5% for March vs -20% for December.

NOTE: seasonal variations do not change over time, since they are based on your customers’ lifestyles and culture.

Seasonal variations reflect lifestyles and cultures and do not change

Assumption 5: Reseller Rates

If you have managed to create partnerships with resellers (and I strongly suggest that you do), then you have a multiplier effect on your revenue. If you have 5 resellers, you could be earning up to 5x your expected revenue.

Even better, your resellers can help you create better assumptions. Resellers will likely already have experience selling your kind of product. They already know their conversion rates, their pricing, their customer lifespans, and their seasonal variations. By interviewing several of them, you will quickly arrive at a set of authoritative assumptions (which will be much more convincing for investors and others).

It’s best to assume that each reseller’s sales figures will reflect your model, modified by a commission you should be expected to give them on every sale, measured as a percentage. In addition, track the amount of commissions being paid out to resellers as a separate item.

Finally, assign a reseller rate to each reseller – a percentage of higher or lower success that you think they will achieve. For example, Bob’s Appliances and Phil’s Home Needs both agree to resell your fridges. However, you believe that Phil will sell half of what Bob will. In that case, set your model to fit Bob and give Phil a reseller rate of -50% (Bob’s reseller rate would be 0% – i.e., no modification).

Resellers are an excellent source of baseline assumptions

Assumption 6: Annual Growth Rate

Your annual growth rate reflects that your business will be more successful over time, due to better marketing, viral growth, or other factors. The growth rate is applied by taking the baseline for that month and increasing by the growth rate for that year. For example, January 2020 might be expected to be 3% better than January 2019. Each year should be assigned its own growth rate, so that the rate can increase over the first few years.

A good annual growth rate is in the single digits – e.g., 5%. If you experience growth over 10%, then you are doing well. Rates of 20% and higher are considered explosive growth. If a product goes viral, you might see high double digits for a while, but that never lasts. Once that thrill is gone, the growth rates will drop down under 10% again.

Like seasonal variations, the annual growth rate should be applied to all revenue streams equally.

A good annual growth rate would be 5%

Conclusion

It’s not easy to build a good revenue model. It takes effort to research reasonable assumptions. However, if you put in a bit of time, you can rapidly pull together a strong model that will validate your business idea.

Break out those spreadsheets and get to work!


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