This is the second part of a two-part series. The first part was about what makes an ongoing business forecast realistic. In previous posts, I’ve discussed why it’s important to forecast basic business numbers and ways to improve those forecasts. I’ve emphasized the value of realistic forecasting in those posts. So, in this series, I aim to explore what makes a forecast realistic. How can we identify it? What should we look for?
I split this topic into two parts because the considerations for established businesses differ significantly from those for new businesses. For established businesses, realism is bolstered by past performance data. New businesses, however, lack this historical data. Instead, realism comes from transparent assumptions and comparisons with similar scenarios.
A common complaint from new business owners is, “How can I forecast a new product if I don’t have past data?” Yet, this is not an excuse to skip forecasting. In fact, new businesses need forecasts even more than established ones due to higher levels of uncertainty and risk. While it may be challenging, it’s certainly doable as millions have done it before.
**Focus on Sales Drivers:**
Most businesses have a few key factors that drive sales, such as traffic, channels, stores, leads, or pipeline. A realistic sales forecast breaks these down into specific drivers. Here are two examples:
1. A sales forecast for a subscription-based web business should include estimated web traffic and conversion rates, making it more realistic than simply showing subscription numbers. For added realism, break down the traffic into various sources like organic search, pay-per-click ads, and email marketing.
2. A sales forecast for a product business selling through traditional retail channels might include estimates of the number of stores carrying the product and monthly unit sales per store. It becomes more realistic if the monthly sales assumptions are broken down into different categories of stores.
**Realistic Assumptions Make Realistic Forecasts:**
Even new products and businesses follow established patterns. Modern technology provides massive resources to inform our estimates, even for new ventures, by leveraging norms from existing ones.
For instance, the web business mentioned earlier can research what typical traffic expectations might be for new sites based on factors like the type of site, business, and marketing budget. Similarly, a product business can find data on sales per store for comparable products.
A new business forecast can ground its assumptions in data from existing businesses. When a key assumption for something new is based on actual results for something similar, it adds credibility.
New businesses can also find plenty of data on expenses. Average salaries by job type for your market, average marketing spending as a percentage of sales for your business type, and average office space per person can all be researched. Average rents for office or retail space vary by location and square footage and are also attainable.
**Review and Revise Often:**
Remember, forecasts aren’t meant to be static. They should be regularly reviewed and revised. The goal isn’t to predict the future accurately but to gather information that can help manage the business effectively.
