Perhaps the most critical and difficult challenge that goes into creating a good business plan is making accurate sales projections. It’s easy enough to figure out what your overhead expenses (rent, utilities, fuel, etc.) will be, and it’s not too much harder to figure out what your Cost of Goods Sold (COGS) on a per-item basis or your variable expenses (travel, advertising, etc.) will be, but your bottom line net profit still depends on your top line: total revenue, a.k.a. sales.
In the best circumstances, you have timely, apples-to-apples data upon which you can make your sales projections. If you’re buying an established business that has consistently done $100,000 in sales each of the past three years, and you’re not changing anything, the odds are pretty good that you’ll be doing about $100,000 in sales the first year you run the business.
Similarly, if the Burger King directly across the street from where you’re opening a McDonalds has been selling $1,000,000 annually, the odds are pretty good that your shop will sell between $500,000 and $1,000,000 annually (depending on how much new business you attract to the area vs. how much you “split” with Burger King).
But the best circumstances are exceedingly rare. Usually we have to look at regional or national vs. local data, or data from 3-5 years ago vs. current data, or we have to look at similar-but-not-the-same businesses (budget chain haircut shop like Supercuts vs. a high end, full-service beauty salon), or we have to look at data only tangentially related to our business, e.g. statistics on the # of cars that travel daily on the road on which our coffee shop is located.
Here are some basic guidelines for making sales projections:
- Data that is more recent and related to more similar businesses to your own is better than older data and/or data from less similar businesses. (Duh.)
- At every turn, err on the side of conservative sales estimates. There are no consequences for outselling your projections, but there can be dire consequences if your business model is counting on hitting a certain sales figure to make ends meet and you come up short of that level.
- When you have to rely on more tangential data (Supercuts vs. the Beauty Salon), make note of, and factor in, the differences (Supercuts does higher volumes at lower prices, but has longer hours and spends more on advertising).
- Whenever possible, get data from two or more sources. If the sources all indicate similar sales volumes, great; if not, try to find out why they don’t agree with one another.
Let me share an example (names/details changed to protect the innocent) of how you can “triangulate” your sales projections by looking at multiple data sources that are only tangentially related to your business.
I had a customer who opened a take-out restaurant. He calculated that he’d need to sell about 6 meals/hour on average in order to cover his expenses and make a reasonable profit for himself. That would add up to sales of about $50,000 annually. Based on his prior experience working in restaurants, that seemed reasonable (Data Source #1).
We found regional data on all restaurants that offered take out as part of their models that suggested annual sales of about $500,000 annually. Upon closer inspection, that included restaurants like McDonalds that had longer hours, bigger, cheaper menus, more advertising, etc. We estimated that my customer’s restaurant might do 10% of that average annual sales level, or…$50,000. (Data Source #2).
We found sales data from the previous occupant of the restaurant, who had operated a take-out hot dog stand several years earlier. This was a lower cost product and it wasn’t marketed as aggressively as my customer would be promoting his own business, so we figured that we could conservatively do at least 50% better than he previous business had done, which would put us at about $40,000 (Data Source #3).
Finally, we found Michigan Dept. of Transportation data on the street where the restaurant was located. Based on the number of cars that travelled that road daily, if 1% of them stopped to buy something, that would work out to about $60,000 in sales annually.
- Owner Prior Experience: $50,000
- Regional/Industry Data: $50,000
- Older/Similar Business: $40,000
- MDOT Data: $60,000
All of the numbers more or less agreed with one another, suggesting that the $50,000 sales projection was probably a reasonable one.
In the end, was our sales projection a guess? Of course. But it was a guess that was consistent with the the multiple (if limited) data sources we could locate. It was also a conservative guess. We could have said 5% of cars stopped and we were able to make 20% of the $500,000 regional sales average, which would have suggested a higher sales volume for us, but recognizing the limited quality of our data, we played it safe.
So where can you find data upon which to make sales projections?
- www.wikipedia.org
- Industry Data
- City, County and/or State demographic data
- www.google.com
- Google Search
- Industry Data
- Industry/Professional organizations
- Google Books
- Industry Data
- Google Maps
- Competitors (who often have some sales information on their web sites or on financial sites of they are publicly traded)
- www.freedemographicsdata.com
- Demographic Data re: income, age, gender, ethnicity, family status, household value, etc. by ZIP, City, County, State
- Your State web site (e.g. www.michigan.gov)
- Industry Data
- Demographic Data
- Secretary of State / Dept. of Transportation / other agency data
- Similar businesses in other regions (with whom you’re not competing)
- Other businesses in your same region (with whom you’re not competing, e.g.auto mechanics vs. auto body shops)
- Your prospective customers (via interviews, surveys)
- How much do they intend to spend monthly/annually on your types of products or services? How much might they be willing to spend with you?
- Your local
- Library (in Kent County, MI: www.grpl.org)
- Chamber of Commerce
What other resources or best practices do you use when making sales projections?
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