An online booking service gets 200 salon registrations, and 50 salons pay for a subscription. The founder wants to increase advertising: another 200 registrations should bring another 50 payments. The marketer knows that one in six salons from Instagram pays, compared with almost one in three from search. Doubling registrations through one channel changes the audience mix, so the previous ratio may not hold.
This is the service from the product-led growth lesson: 12,000 ₸ per month after a 14-day trial. The example figures are made up. AARRR breaks the growth discussion into five questions: where salons come from, whether they reach the first benefit, whether that benefit continues, whether they bring in other salons, and what they pay for.
Five areas for one product
In Dave McClure's AARRR model, the letters stand for Acquisition, Activation, Retention, Referral and Revenue. Each area needs an event, unit of measurement and window. "Activation is 40%" alone is not enough for someone else to reproduce the calculation.
The salon pays for the service, while its owner, administrator and stylists use it. One salon with several staff members must remain one unit. Link events by salon_id; the number of people logging in cannot replace the number of salons.
| Area | Question | Definition for the booking service |
|---|---|---|
| Acquisition | Which salons arrived, and from where? | New salons with salon_signed_up, by registration source |
| Activation | Is the salon ready to receive value? | Enabled online booking through online_booking_enabled within the first 7 days |
| Retention | Does the benefit repeat? | Received at least one online booking in a specified week after registration |
| Referral | Do salons bring in new salons? | A new salon registered through a confirmed invitation from an existing one |
| Revenue | How many salons pay, and how much money remains? | subscription_paid payments, refunds, acquisition costs and service costs |
Here, activation makes online booking available to clients. The first client booking through the link shows that the salon received value: the administrator did not need to exchange messages and enter the appointment manually. The PLG lesson calls this the aha moment. Another product might define activation at the first benefit itself; what matters is agreeing on the event and keeping the definition consistent across reports.
Why the letters do not dictate purchase order
The coffee store receives money with the first order. A repeat purchase and a referral can follow later. The image shows a similar path in a physical store: someone visits, buys and returns. Payment sits between the visits.
The booking service has its own sequence: trial, initial setup and bookings, then subscription. A referral may happen during the trial. Making Acquisition → Activation → Retention → Referral → Revenue mandatory for everyone would exclude buyers who paid before referring someone.
AARRR works as a set of reporting areas. A funnel with a required order needs its own events, timing and participant overlaps. Totals for each area do not establish that sequence.
One cohort, several denominators
Take the 200 new salons from the PLG example and assume all the required observation windows have closed. Within the first 7 days, 80 salons enabled online booking. During the 14-day trial, 60 received their first client booking. After the trial, 50 paid for a subscription.
| Metric | Calculation | What it describes |
|---|---|---|
| Activation within 7 days | 80 / 200 = 40% | Share of the original cohort that enabled online booking |
| First online booking during the trial | 60 / 200 = 30% | Share of the original cohort that received the first benefit |
| First payment | 50 / 200 = 25% | Share of the original cohort that paid for a subscription |
| Payment among salons with a first booking | 46 / 60 ≈ 77% | Paying share within the group that received the first benefit |
| Payment among the rest | 4 / 140 ≈ 3% | Paying share among salons without a first booking during the trial |
The last two rows come from salon IDs: 46 paying salons belong to the group of 60, and four do not. Dividing all 50 payments by the 60 salons with a first booking gives about 83%, but puts four salons from outside that group into the numerator.
The same issue appears between activation and the first booking. Of the 80 salons that enabled booking within a week, 57 received their first booking before the trial ended. Another three enabled booking later and still received a client. So 60 / 80 does not describe conversion within the original group of 80.
Conversations with salons and their paths through the product help investigate causes. The link between a first booking and payment suggests who might need help, but the salon's own readiness also affects both events. Plan a separate HADI test for a change intended to accelerate the first booking.
Retention counts repeated value
For this service, a return is booking_created with source = online. An appointment entered manually by the administrator represents different work. Logging in also cannot tell you whether clients are still booking themselves.
In the retention example, 41 salons from the same cohort received online bookings in week 5. Week 5 means days 35-41 after registration. Their share of all 200 salons is 20.5%. All 41 belong to the 50 paying salons: after the trial, online booking is available only to paying salons. If the question is about use among those 50, the denominator changes: 41 / 50 = 82%. Both rates are valid, but they answer different questions.
The other nine paying salons received no online bookings that week. Support can find out what happened: a holiday, a moved link, disabled online booking or another reason. The subscription payment has already happened, but the benefit stopped recurring. That is a reason to contact the salon before the next renewal.
Recent registrations have not reached week 5 yet. Until the window closes, mark the cell as immature. Compare it with a closed cohort only at the same participant age; otherwise the new audience looks weaker simply because it had less time.
Link a referral to a new salon
The service's channel table groups 30 registrations from booking-page links and recommendations together. That row cannot tell you the number of personal invitations: an owner may have seen the service's name on another salon's booking page without anyone recommending it. Referral needs a separate way to connect the new salon to the inviter.
Before counting, the team defines a confirmed invitation, how long it remains valid and how to remove duplicates. Clicking "Invite", sending a message and a new salon registering are separate events. If the new salon then invites another, there is a growth loop, as discussed in Reforge's article.
Invitation counts help locate a break in the mechanism. People may send links whose recipients never register. The number of new paying salons from those links combines referral with revenue. Include invitation rewards in the channel's costs.
Connect growth to money
50 first payments at 12,000 ₸ produce 600,000 ₸ in revenue. The original example spent 1,560,000 ₸ on advertising, or 31,200 ₸ for each of the 50 paying salons. That overall average includes salons acquired without ad spend. The cost per payer differs by channel: 48,000 ₸ for Instagram and 37,500 ₸ for search.
31,200 / 12,000 = 2.6 monthly payments. That is not a payback period: service costs still come out of revenue, and some salons will cancel. In the retention example, 43 of the first 50 paying salons paid for a second month. A budget decision needs their subsequent payments and contribution after costs, with refunds accounted for.
The largest drop in the table, from 200 to 80 at activation, therefore starts an investigation. Of the cohort, 120 salons did not enable booking within a week: some may be unsuitable registrations, while others may be stuck during setup. Before an expensive redesign, the team establishes how many salons it could help and what the fix costs. It then chooses one task and records its expected effect alongside the expense.
Exercise: five definitions and a reporting error
Build AARRR for your product. For each area, specify the unit, event, window, denominator and source. Choose one row the team will use for a decision and name the missing data.
For the exercise, add these details to the example: 12 of 200 salons sent invitations, bringing in five new salons. One inviter brought three; two others brought one each. The report says, "Referral conversion = 5 / 12 = 42%." How should it change?
The share that sent invitations is 12 / 200 = 6%. Five new salons per 12 inviters gives about 0.42 new salons per inviter. Three inviters succeeded, so their share among senders is 3 / 12 = 25%. The five new salons belong to their own registration cohort; do not add them to the original 200 in this rate's denominator.
Video: an overview of the pirate metrics
A talk by Ruslan Sharipov. After watching, compare the definitions in your table with the events that actually exist in the product's data.