The economy is saved! As hoped for, Black Friday descended on credit cards across the US and tallied up record spending to the tune of $52 billion, a 16% increase from the measly $45 billion in 2010. Hurray! All those riots over waffle irons were worth it.
Or wait, was it? Clearly, the final numbers aren’t quite in yet, and the sales forecasting by the National Retail Federation is being brought into scrutiny and for good reason. It’s really important to forecast accurately because a lot of things (including an eager US stock market) depend on them.
What is Sales Forecasting?
So what exactly is sales forecasting? Simply put, it’s a projection of future demand in a product or service. If you’re in the market to sell Beanie Babies, you’d be best served knowing when the next bubble is likely to occur in order to sell the most and at the highest price (we’re still in a hold mentality).
Furthermore, sales forecasting is a prediction of how much of that demand a company will be able to fulfill. Try as you might, you probably won’t have the time and reach to sell to every person who might want a Beanie Baby come the bubble (though you probably could today), and it would be nice if you could predict how much of your stockpile you could actually sell. That way you have an approximate revenue forecast, which can be used to plan out how much of the next fad you’ll buy into. Like Groupon (just kidding).
What is Sales Forecasting Good For?
Why should you engage in sales forecasting? This is sort of like asking: do you want your business to succeed? Without a good guess into what’s in store for your market, you’re not going anywhere. And the better your data (and your math), the better your predictions will be. It’s worth it too, as studies have shown that “best in class” companies have “nearly 24% better sales quota achievement figures and over 16% better sales cycle reduction figures” than those without great forecasting. Here’s a scary model of what over/under forecasting can do to your numbers.
How About a Sales Forecasting Example?
Sure thing. Say you’re a staffing and recruiting agency who maintains your data well. You’ve logged all the past deals you’ve closed, when they’ve closed, how much each was worth, how long they took to close, etc. You’ve also entered in your deals that are in the pipeline, from luke-warm to red-hot. Once you start extrapolating from these data points, you might get something resembling this:
The green bars show what deals are anticipated to close and in what month. In this example, you see that your revenue stream is projected to lag in the upcoming months and now you’ve got your work cut out for you. Also, with the right modeling, you should be able to pinpoint where the lower revenue is coming from, giving you actionable items to increase your close rate in the coming months.