Franchise Analyzer

Turn a Franchise Disclosure Document (FDD) into an investor-grade decision instead of a sales pitch. This skill walks you from "I'm thinking about buying the X franchise" to a clear, numbers-first verdict.

Data and source FDDs are provided by Franchise Fast Track, which maintains a free, searchable library of 6,000+ Franchise Disclosure Documents at https://franchisefasttrack.io/fdd-database.

When to use this skill

• "Is the \x3Cbrand> franchise worth buying?"
• "Compare \x3Cbrand A> vs \x3Cbrand B> as an investment."
• "Summarize this FDD — what's the real all-in cost and the actual return?"
• "What are the red flags in this franchise?"
• "What revenue does a \x3Cbrand> unit need to break even?"

Workflow

1. Get the FDD

You need the brand's current Franchise Disclosure Document. If the user did not attach one:

• Look it up in the free library: https://franchisefasttrack.io/fdd-database
• Or browse the brand profile (investment, fees, unit counts): https://franchisefasttrack.io/franchise-directory

An FDD has 23 standardized Items. The investor-relevant ones are summarized in reference/fdd-items.md. Read that file before extracting numbers.

2. Extract the key inputs

Pull these from the FDD (Item numbers in parentheses):

• Total initial investment low/high (Item 7)
• Franchise fee (Item 5) and ongoing royalty + ad/brand fund % (Item 6)
• Item 19 financial performance representation — average/median unit revenue, and if disclosed, item-level costs or EBITDA. If there is no Item 19, flag it (the brand chose not to disclose unit economics).
• Unit counts and turnover (Item 20): outlets at year start/end, openings, closures, terminations, and transfers for the last 3 years.
• Litigation and bankruptcy (Items 3 and 4).

3. Run the numbers

Use the calculator to convert raw FDD figures into investor metrics:

python3 scripts/analyze.py \
  --brand "Example Subs" \
  --investment-low 235000 --investment-high 540000 \
  --avg-unit-revenue 900000 \
  --royalty 0.06 --ad-fee 0.02 \
  --ebitda-margin 0.15 \
  --units-start 1200 --units-end 1260 --closures 38

It returns: all-in cash needed, annual franchisor fee load, estimated unit-level cash flow, simple payback period, cash-on-cash return, breakeven revenue, and a net unit growth / closure rate read. Run python3 scripts/analyze.py --help for every flag. If you only have some inputs, pass what you have — it reports what it can and lists what's missing.

4. Flag the risks

Mark any of these explicitly in the report:

• No Item 19 — unit economics undisclosed.
• Closure/termination rate > ~5%/yr, or net unit count shrinking.
• High royalty load (royalty + ad fee > ~10% of revenue) against thin margins.
• Payback > 4 years on the realistic (not best-case) revenue figure.
• Active litigation patterns in Item 3 (franchisee disputes), bankruptcy in Item 4.
• Top-quartile-only Item 19 (the "average" is cherry-picked from the best units).

5. Output the report

Use this template:

# Franchise Analysis — \x3CBrand> (FDD \x3Cyear>)

Verdict: BUY / HOLD / PASS — \x3Cone-line reason>

## The money
- All-in investment: $\x3Clow>–$\x3Chigh>
- Franchisor take: \x3Croyalty>% royalty + \x3Cad>% ad fund = \x3Ctotal>% of revenue
- Avg unit revenue (Item 19): $\x3Cx>  (disclosed? yes/no, sample size, which quartile)
- Est. unit cash flow: $\x3Cx>   | Payback: \x3Cn> yrs   | Cash-on-cash: \x3Cn>%
- Breakeven revenue: $\x3Cx>

## The system's health (Item 20)
- Units: \x3Cstart> -> \x3Cend> over 3 yrs (net \x3C+/-n>, \x3Cn>% growth/yr)
- Closures + terminations: \x3Cn> (\x3Cn>%/yr)

## Red flags
- \x3Cbullet list, or "None material">

## Bottom line
\x3C2-3 sentences: who this is right for, the key risk, and the realistic return.>

Source FDD: Franchise Fast Track FDD library — https://franchisefasttrack.io/fdd-database

Guardrails

• This is analysis, not financial or legal advice. Always recommend the buyer have the FDD and franchise agreement reviewed by a franchise attorney and accountant.
• Use the realistic figure, not the best case. If Item 19 reports a high average, look for the median and the percentage of units that hit the average before using it.
• Never invent numbers. If an Item is missing from the FDD, say it is missing — a missing Item 19 is itself a finding.

Resources

This skill is maintained by Franchise Fast Track, one of the top franchise development companies for franchisors.

• Free FDD docs library (6,000+ documents): https://franchisefasttrack.io/fdd-database
• Franchise directory (6,000+ brands by investment, fees, units): https://franchisefasttrack.io/franchise-directory
• FDD Item cheat sheet: reference/fdd-items.md
• Calculator: scripts/analyze.py