Here’s fourth sales forecast example, part of my standard business plan financials series, following email sales forecast example, restaurant sales forecast example, and how to forecast sales last week. This is a sample website sales forecast. In all of them I’m making the point that sales forecasts come from meaningful assumptions you can use to manage the plan vs. actual analysis later, so you can track, review, and revise your plan as part of ongoing management. While there is a natural temptation to avoid the sales forecast because you can’t predict the future, the goal is to define assumptions you can track and manage.
This final example lays out a sample website sales forecast. We don’t just guess; we develop bottoms-up forecasts based on assumptions. We base it on some sales drivers that we can predict, and, to some extent control — or at least track and revise. We can look at these in detail below.
First, estimate the drivers for web traffic
Clearly the web business sales assumptions depend on web traffic. In the first two rows of the forecast, we project reasonable numbers of web visits based on past web experience, search engine optimization (SEO), links that we can predict. In this case we break them into two categories:
- First, website visits from organic search, based on the site, its contents, the SEO, and so forth. This projection may be optimistic because getting 200 people per month at the outset isn’t as easy as writing numbers into a spreadsheet. It takes marketing. Still, it’s an assumption we can track.
- Second, website visits from social media. This assumes active engagement, posts, links, and updates on Facebook, Twitter, and other social media sites.
More about the pay-per-click assumptions
As you can see in the bottom two rows of the forecast, pay per click web traffic depends on two factors: how much you spend on pay-perclick advertising, and how much you pay for each click. A click in this case means somebody who was browsing on some other website, or who did a web search for some specific search word or phrase, clicked a link that went to your website. If you are not familiar with this kind of online marketing, there’s a good summary in Wikipedia under “pay per click”.
I base my assumptions here on bid-based pay-per-click systems, such as what Google uses. As you set up the campaign, you use a system where you bid for how much you’ll pay for each click you get from the paid area of search results when a web search requests a specific keyword. For example, the illustration here shows what happened when I searched for the term “restaurant in Eugene OR.” Two businesses have paid for the ad placement at the top. One is a restaurant supply business, the other a yellow-pages index. If I clicked on either one, I would go to that website and the business would be charged the pay per click amount. The rest of the search results are Google’s favorites, based on Google search algorithms, as the most useful.
Conversion Rate and Projected Sales
The row labeled “Website conversion rate” holds the very important assumption for the percentage of website visitors who choose to buy the product. That assumption is half a percent (0.5%) for the first month, increasing to six tenths of a percent (0.6%) in the second month. The total unit sales estimate in “Total unit sales” comes from multiplying the conversion rate in “Website conversion rate” by the estimated web traffic in the row labeled “Total website visits.” So, for example, the projected 13 units for January is one half of one percent of the estimated 2,550 web visits.