Capstone: The Recommendation

You've done the analysis. Now comes the part that really matters. A decision needs options, with their costs and benefits, and a clear recommendation. Let's write the page that the founder will take to the bank.

• Analysis is done
• Options, costs, and benefits
• A clear recommendation

Comparing the options

We lay out three options, with their one time costs, and their yearly gains. These are assumptions, and we'd state them clearly. We compare the value over three years. Run it. Interesting. Closing looks tempting, but shrinking the cafe and focusing on coffee, with its higher margin, is worth more. Our analysis opened up a choice that nobody had thought of.

options = {
    "Keep as is": {"cost": 0, "yearly_gain": 0},
    "Close the cafe": {"cost": 60000, "yearly_gain": 25000},
    "Shrink and refocus": {"cost": 30000, "yearly_gain": 38000},
}

for name, o in options.items():
    three_years = o["yearly_gain"] * 3 - o["cost"]
    print(f"{name:<20} three year value ${three_years:>8,}")

Keep as is           three year value $       0
Close the cafe       three year value $  15,000
Shrink and refocus   three year value $  84,000

The recommendation

Here's the page. Recommendation, don't close. Shrink and refocus the two weakest cafes. Why? They lose money after rent, but their sales are still growing. Coffee earns higher margins than food, at sixty eight percent against fifty four. And the three year value beats closing. Risks, the rent assumptions are estimates. Next step, a six month pilot in one cafe, with a clear target. If it misses, we close it. That's a decision that a founder can take to a bank.

What you did

Look at what you just used. Cleaning, joins, group by, bootstrapped ranges, trend comparisons, and option analysis. Then you turned it into one clear page. That's the job. And there's a final lesson here. The most useful thing that you did was to find a third option. Good analysis doesn't just answer the question. It improves the question.

Summary