Forecasting Sales for A Pre-Revenue Startup

When starting a business, chances are you will need funding to get you through the first couple of years. Whether you finance your startup through debt or equity, your funders will likely want to see pro forma financial statements. These statements do more than identify how much funding is needed and when; they force the entrepreneur to perform due diligence and understand their business.

A common issue entrepreneurs face when building a financial plan is forecasting revenue. It’s difficult to forecast revenue in a startup that has yet to make any sales. Without experience, there’s only so much you can do to accurately forecast your business’ financial needs for the next three years. Below are common practices used to solve this dilemma.

First, write out your anticipated expenses. When you are at the pre-revenue stage of your business, it’s much easier to determine your projected expenses than sales. Classify these expenses in three categories: startup, fixed, and variable costs. Startup expenses are generally one-time layouts needed to get the company off the ground. Be careful not to mistake capital expenditures with expenses. Your fixed expenses include items like rent, monthly accounting expenses, salaries, and other overhead. Lastly, variable costs relate to those needed to sell your product or service. Cost of materials, supplies, packaging, and sales are often included in the “Cost of Goods Sold” variable expense of a financial statement. Once you’ve accounted for all anticipated expenses, including depreciation, amortization and taxes, you can then determine your expected monthly burn rate. This is especially important when asking for funding because it will communicate how long your business will last with their contribution until you need another injection of funding. This is known as the “cash runway”. Absent the forecasted revenues, the entrepreneur already has a better idea of their business and funding needs. But the current cash runway makes the strong assumption that there are no sales.

The entrepreneur can use one of the following methods to weaken this assumption: top-down sales forecasting and bottom-up sales forecasting.

Top-down analysis takes a macro view of forecasting sales. The first step is quantifying the total addressable market (TAM), considering current and expected market trends. Next,consider what percentage of the total addressable market your company can service. The serviceable market is determined by considering how many customers can be reached using the company’s sales channels. Where incumbent firms have built high entry barriers, a startups’ serviceable market is often small because large investments are needed to reach their competitors’ market. After quantifying the serviceable market, which is often expressed as a percentage of TAM, the entrepreneur can decide on a target market based on demographic, psychographic, and geographic data. This market is best positioned to adopt the new product or service. Using anticipated pricing, sales volume, and purchase frequency, the entrepreneur can determine the expected revenue per year by multiplying the total number of purchases per month by purchase size. Important to consider when forecasting for more than a couple of months is the growth in purchase size, volume, and frequency over time.

The Bottom-up approach takes a micro view of sales generation and starts with product sales or production estimates. Using current production and sales capacity, the entrepreneur predicts how many unique sales can realistically be generated each month. Multiplying anticipated sales numbers by the average volume per sale will give an accurate revenue estimate for each month. The bottom-up forecasting approach is often favored because it provides a more accurate assessment of actual sales. According to studies by Gordon (1998) and Gelly (1999), the bottom-approach on average yielded more precise forecasts 75% of the time (Wanke, 2007). It is especially superior when your startup has a large product mix with different sales cycles. When products have varying sales patterns, estimating sales for them separately and aggregating the estimates is often more accurate than estimating aggregate demand.

On the other hand, if you are selling multiple products with similar anticipated sales variations, a top-down approach would be beneficial. Since aggregating demand will cause less variation and volatility, the sales forecasts will be more accurate relative to each other. In other words, your estimates may not be as precise, but month by month variations will be more accurate than a bottom-up approach (Lapide, 2006). Larry Lapide, Research Director at MIT, demonstrates this idea in the accompanied diagram.

At this point, you may be thinking that it can’t be as simple as picking one or the other. Using a hybrid of the two methods can provide even more accurate sales forecasts. By employing both approaches, you are using each of their strengths to complement the others’ weaknesses. Where the top-down approach is less precise, the bottom-up approach can provide a better sales number. Where the bottom-up approach has too much variation, the top-down approach can normalize the month by month changes. Using this hybrid method will give the entrepreneur and other involved parties a holistic view of anticipated sales.

The chances of your sales estimates being correct are very slim, which is why this is not a one and done process. As an entrepreneur, you should be constantly revising your forecasts based on actual performance. It may seem like a lot of effort, but your investors will thank you, and you’ll have a more successful business because of it.

Works Cited

Lapide, L. (2006). Top-Down & Bottom-Up Forecasting in S&OP. The Journal of Business

Forecasting,Summer.

Maguire, A. (2017, November 15). Top-Down vs. Bottom-Up: Which Financial Forecasting Model Works

for You? Retrieved from

https://quickbooks.intuit.com/r/forecasting/top-down-vs-bottom-up-which-financial-forecastin

g-model-works-for-you/

Wanke, P., & Saliby, E. (2007). Top-Down or Bottom-Up Forecasting. The COPPEAD Graduate School of

Business. doi:1678-5142