Here’s another sales forecast example, part of my standard business plan financials series, following my restaurant sales forecast example posted here yesterday, and how to forecast sales the day before. The point is to call out realistic assumptions that make a sales forecast useful. This is to illustrate my underlying point that anybody who can run a business can forecast sales; and that the goal isn’t to accurately predict the future – which is, of course, impossible – but rather to lay out trackable assumptions that you can follow up and manage. Your real results will be different. If your sales forecast is done right, the difference between what you projected and what actually happened will be the key to ongoing management.
What I’d like to show you here is how when you want to forecasting something new you start with assumptions you can lay out, and then go on from there. That’s what Magda does in my previous post, going from restaurant layout with chairs and tables, to times of day, and days per week. This next example projects unit sales from email marketing. Here again, the key is to track the assumptions. So here’s a sample sales forecast for the projected unit sales of the first few months of a product to be marketed via email. [Warning: This is really simplified. May the email marketing experts forgive me for making it look this simple. It isn’t; but the basic numbers follow these basic principles.]
- It starts of course with how many emails get sent. The assumption here is that the marketing department sends out 20,000 emails the first month, 25,000 the next month, and so forth. And let’s remember that while it’s easy to type numbers into a spreadsheet, execution requires an effective email message, design and formatting, and a good list of email addresses of real prospects. Targeting is essential.
- We put assumptions for how many people open the emails into the second row. And the assumption shown for January, by the way, is amazingly high, and quite unrealistic. A business would have to be sending emails to a list of opted-in email addresses for customers or prospects who like this sender a lot. Available information on average emails opened, from MailChimp and other vendors of email services, runs more like 15% to 25%. The numbers here are high.
- We use the third row for our assumption for how many people click the link on the email. There too, this example is very optimistic. Normal rates rarely get above 2%.
- Next is website views. With emails sent, emails opened as a percentage, and clicks as a percentage, we can project how many people click an email link and arrive at a website. In January, for example, we take 20000*.35*.08 = 560. Here again, the math is simple. The business behind it — a good email list, a good email, subject line, text, and links, and offering — is not simple.
- Then we project a conversion rate, which is how many people who see the offer on the web choose to buy. The 0.5% (one half of one percent) assumption here is not unusually low. Actual conversion rates depend on how well targeted the people are who arrive at the website, how attractive the offer is, and many other marketing and sales variables.
- Finally, in the last row, we arrive at projected sales. The indication here is that sending 20,000 emails produces the small unit sales shown here in the bottom row.
From here we would take the unit sales resulting from these assumptions to the main sales forecast, with the structure we use for the sample sales forecast above: units, prices, sales, direct costs per unit, and direct costs. The spreadsheets would look a lot like the ones for Magda, in my previous post; and Garrett the bicycle retailer in How to Forecast Sales.
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