Forecasting for Small Business Owners: How does it Work? (Part 2)

Forecasting for Small Business Owners: How does it Work? (Part 2)

Previously, we learned the what of forecasting—now, it’s time for the how  

No need to be nervous! Business forecasting might be a little more complex than checking the weather on your phone, but you’re not up against anything as complicated as global meteorology.   

With the right data and a sprinkle of common sense, you’ll be a weather master in no time. Let’s take a walk through a few examples of forecasting for a brand new neighborhood pie shop.   

Forecast methods:   

Qualitative forecast example  

Let’s say your pie shop is just starting out and trying to decide what delicious flavors to showcase at its grand opening. Now, you have a couple of house specialties that’ll definitely make the menu, but what about the rest? Assuming your pie shop wants to open with five pie flavors, you need to choose three more to round out the group. The best way to do that is with a qualitative forecast.   

To do this forecast, you’ll need to look at two important factors: competitive choices and market limitations. Competitive choices are all about the successes and failures of your competition. For instance, if you know you’re in the same market as a pie shop that makes award-winning cherry pie every year, you might want to go with a different flavor niche. On the other hand, if every pie shop in your area sells out of blackberry pie every year, then you NEED to hop on the blackberry train.   

Now, let’s add in market factors. After looking at your competition, you’ve decided that the best three additional pies are blackberry, raspberry, and pecan. HOWEVER, during your market research, you discover that raspberry sales are down in stores due to a lower crop and heavy inflation. That means raspberry pies will be expensive to make and dicey to sell.  

Based on the combination of qualitative data, you forecast the following:  

Pies likely to sell out: Blackberry, raspberry, pecan 

Pies affected by inflation and limited supplies: raspberry 

Business decision: switch from raspberry to boysenberry for a similar flavor at a lower cost.    

Quantitative forecast example  

It’s three years later and your pie shop is doing well! Qualitative forecasting worked and now you have a solid financial database to make forecasts off of. Thanksgiving approaches and you want to know approximately how much you can expect to make because you want to invest in a new set of stand mixers.   

You take a look at your previous financials from November and discover the following:   

November year 1: $2,000 in profits after selling approximately 500 pies at $10 

November year 2: $2,300 in profits after selling approximately 500 pies at $11 

November year 3: $2,500 in profits after selling approximately 500 pies at $12 

These numbers show you that the number of pies didn’t really change that much over the years, so you can reasonably expect to sell approximately 500 pies (unless you drastically change your offerings). You can also forecast that customers are not deterred by small price upticks, so you can likely make the same number of sales at $13 a pie. This leads you to assume that you can see profits of around $2,750. That looks like a sunny forecast to us!   

Types of forecasts:   

Demand forecast example  

Demand forecasts can be qualitative OR quantitative. Our earlier example of looking at local pie trends to determine which pie flavors to sell is a great example of a qualitative demand forecast. If every other shop has success selling blackberry pie, then you can forecast that your shop will also see that same success. After all, if all the neighboring towns have cloudy skies, you’d probably pack an umbrella just to be safe.    

Quantitative demand forecasts, on the other hand, use hard numbers. You can see an example of this in our above quantitative pie breakdown. After three years of selling 500 pies, it’s safe to forecast that without major product changes, you will likely sell another 500 pies. It’s the same logic that keeps you from donning a parka in July.   

Cash flow forecast example  

Cash flow forecasts are almost always quantitative because you’re dealing with money. For example, if your pie shop consistently makes $5,000 a month, but spends $4,000 a month, then you know you can reasonably rely on that $1,000 profit.   

Knowing your previous cash flow also lets you plan fun purchases! If you forecast that you have $5,000 coming in next month and you know you want to expand your marketing campaign by $500, then you can try and make $500 worth of cuts in your spending to make room. Keeping an eye on the numbers means you always come out on top.   

Startup cost forecast example  

Like demand forecasts, startup cost forecasts can be qualitative or quantitative because you’re usually starting from scratch. However, that doesn’t make these forecasts any less valuable!   

On the qualitative side, sometimes just knowing a cost is coming up is enough to prepare you for the road ahead. For instance, if you plan on onboarding a bunch of staff one week, it’s probably best to schedule office maintenance a few weeks before or after to spread out your spending. Baby waves are manageable, but a tidal wave is tough!   

Quantitative startup forecasts, on the other hand, are all about delicate budget management. For instance, bringing in $5,000, and spending $4,000 is perfect once you’re up and running, but if you’re in your first year, that $4,000 quickly expands when you factor in things like advertising campaigns or business software programs. Forecasting an additional $600 for advertising in April lets you scale back on other April spending so that you don’t find yourself underwater by May.    

It’s not easy learning the precarious balance of analyzing data and trusting your business instincts, but you can master it! The best way to learn these strategies, though, is always with a helping hand. Contact Know Your Numbers today for help navigating your business’s upcoming weather reports.  


Leave a comment

Please note, comments must be approved before they are published