What's the best way to compare two franchises?

Compare them on the same FDD data points: total investment (Item 7), franchise fee and royalty (Items 5 and 6), median earnings (Item 19), net unit growth (Item 20), and litigation (Item 3). Using AI connected to an FDD database, you can pull all of these for several brands into one table instead of reading each document separately.

How do you compare franchise profitability?

Use Item 19 financial performance, and prefer the median over the average. The average gets inflated by a few top units. Then sanity-check it against SBA loan default rates, which show how often these businesses actually fail to repay. A high advertised average paired with a high default rate is a warning sign.

Can AI compare multiple franchises at once?

Yes. That's where AI beats manual research. Connected to FranDB, ChatGPT or Claude can pull the same data points for many brands and return a single comparison table in seconds, something that would take hours of reading FDDs by hand to assemble.

What data should a franchise comparison include?

Total investment range, franchise fee, royalty and ad fund percentages, median Item 19 revenue and profit, three-year unit growth from Item 20, litigation and bankruptcy history, and SBA loan default rate. Together these cover cost, earnings, momentum, and risk: the four things that actually separate brands.

How to Compare Franchises with AI (Side by Side, in Minutes)

Comparing franchises by hand is miserable. Each FDD is 200 to 400 pages, the numbers you need sit in different tables, and holding three or four brands in your head at once is hopeless. AI fixes the mechanical part: connect it to real FDD data and it lines up any number of franchises on cost, earnings, growth, and risk in one table.

The four things worth comparing

Most franchise comparisons drown in detail. Strip it back to four dimensions that actually separate good brands from bad:

Cost: total investment (Item 7) and the fee stack (Items 5 and 6).
Earnings: median revenue and profit (Item 19), where disclosed.
Momentum: net unit growth over three years (Item 20).
Risk: litigation (Item 3), bankruptcy (Item 4), and SBA default rate.

A brand can look great on one and fail on another. Cheap to open but shrinking. High average revenue but a stack of franchisee lawsuits. The comparison only means something when you see all four at once.

Why this is hard to do manually

The data is standardized (every FDD has the same items) but it's spread across hundreds of pages per document. To compare five brands the old way, you download five FDDs, find Item 7 in each, then Item 19, then Item 20, transcribe it all into a spreadsheet, and hope you didn't misread a table along the way. It's a half-day of work, minimum, and it's exactly the kind of repetitive reading where attention slips.

This is the work AI should do. The figures are structured; the model just has to fetch them.

Building a comparison with AI

Connect AI to a franchise database (FranDB's MCP integration does this for ChatGPT, Claude, and Cursor) and a comparison is one question:

"Compare these five brands by total investment, franchise fee, royalty rate, median Item 19 revenue, net unit growth last year, and litigation count. Put it in a table."

You get the table back in seconds, pulled from real filings. Then you interrogate it:

"Which of these had more closures than openings?"
"Sort by median profit, not revenue."
"Add the SBA default rate for each."

Every follow-up refines the picture without you opening a single PDF.

Reading the comparison honestly

A table makes brands look equivalent. They're not. A few things to watch:

Average vs. median in Item 19. If a brand reports a $1.4M average but an $800K median, a small number of standout units are carrying the average. The median is closer to what you'd actually earn. Always ask for it.

Missing Item 19. About a third of franchises don't disclose financial performance. A blank isn't a zero, it's a question. The strongest brands usually disclose because the numbers help them sell. When a franchisor stays quiet, ask what it's not showing.

Cheap doesn't mean safe. In the SBA loan data we've analyzed, lower-investment franchises defaulted more often, not less. A small franchise fee is not a discount on risk. Weigh cost against earnings and default rate together, never alone.

Net growth beats raw size. A brand with 500 units that closed 40 last year is in worse shape than a 200-unit brand that opened 30. Item 20's three-year trend tells you direction, which matters more than the headline count.

From comparison to decision

The table narrows the field. It doesn't decide. Once two or three brands rise to the top, the next steps are human: call current and former franchisees (their contacts are in Item 20), and have a franchise attorney read the agreement before you commit.

What AI changes is how many brands you can seriously consider. Reading FDDs by hand, most buyers compare two or three because that's all they have time for. With the data a query away, you can screen twenty and compare the best five properly. That's a better shortlist, and a better shortlist is most of a better decision.

Next: AI franchise due diligence for the full vetting workflow, or how to analyze a single FDD with AI.