Short form. Don't memorize the answers word for word — the question might be phrased differently. Understand the logic once, and you can answer any phrasing of it. The full breakdown with every number explained is in the deep dive, the model itself is here.
I built the model together with an AI tool (Claude) — but I decided myself which metrics to calculate and why, checked the numbers against real Ukrainian sources wherever they existed (real prices from the SugarMe/LaserVille chains, CPL for the beauty niche from akitalab.com.ua), and where there was no Ukrainian data, I kept international benchmarks (WordStream, SBA) honestly labeled as such instead of passing them off as local, and I chose which scenarios to show. The AI helped me quickly assemble the interactive calculator and format it. But the logic — why we calculate LTV on margin instead of the check, why response speed in Direct moves the whole economics so much — I worked through and verified myself. I specifically built the "slow vs. fast response" comparison because it's the most practical takeaway for a real business. Using AI as a tool is normal, the same way people used to use Excel; what matters is that I understand what's behind the numbers, not who drew the slider.
CPA (Cost Per Acquisition) — how much it costs to acquire one paying client. We divide the ad budget by the number of clients who made it through the whole funnel to payment: click → Direct → booking → visit.
The check is the client's money, not the company's. Out of the check, the technician takes a commission (~40%), and more goes to supplies (~8%). The real profit is the contribution margin (~52% of the check). Calculating LTV on the check is a classic beginner's mistake.
The ratio of "how much the client brought in" to "how much it cost to acquire them." The "minimum 3x" rule comes from the venture/SaaS industry, but the logic is universal: you need a buffer for rent, salaries, unexpected costs. For a local service business, this is a reasonable reference point, not a strict law.
No. Waxing prices are a real anchor from the SugarMe (11 studios, Kyiv) and LaserVille (9.6/10 rating on barb.ua) chains. CPL in the beauty niche comes from akitalab.com.ua. CPC/CTR come from WordStream reports (an international benchmark — no Ukrainian data for these figures was found, verified directly). Retention (35% / 50% / 45-70%) comes from the Join Blvd study, also honestly labeled as international, but cross-checked against Altegio (a Ukrainian-language CRM blog, ~65%, with a warning about the spread between technicians). The response-speed effect (15%/42%) comes from industry data by LeadResponse.co and Naiva.ai on beauty salons; the direction of the effect is confirmed by the independent B2B study by Oldroyd (MIT/InsideSales 2007) and Harvard Business Review (2011) as a universal psychological pattern. "Direct" isn't locked to Instagram — in Ukraine, Telegram is the main channel for messaging businesses (Kantar Ukraine, USAID/Internews).
No, that's normal for a business with repeat visits. In the first month you pay for acquisition, while the profit from the client is spread across future visits. Judge it by LTV, not by the first month.
LTV:CAC answers "is this profitable in principle," payback answers "how fast will the money come back." A campaign can be profitable by ratio but have an 18-month payback — and then there's nothing to fund it with if cash is limited.
Per LeadResponse.co and Naiva.ai: beauty salons' baseline "direct → booking" conversion is usually below 15%, rising to 35-50% with a fast response — most salons lose 30-40% of bookings to slow responses. The effect shifts both CPA and retention at once — one operational process moves the economics more than a bigger budget does.
In the model — almost nothing, since all conversions are set as percentages. In reality CPA usually rises (the cheapest audience gets "burned through" first) — the model doesn't account for this, and that's honestly a limitation of it.
Four things that close part of my own acknowledged limitations: a season toggle, blended CAC by channel, a month-by-month cohort retention curve instead of one figure, and a sensitivity analysis — which of the 11 variables moves LTV:CAC the most. Each addition is explicitly labeled as an estimate where exact Ukrainian data doesn't exist.