Home/ The Cases/ Hair Try-On
B2C App · AI + Beauty · App Install + Subscription Campaigns · New Market Launch

Month 1: market validation, 1,000+ installs, MRR live. Month 3: approaching break-even.

Hair Try-On is an AI-powered app that lets users virtually try on haircuts, colors, and finishes before stepping into a salon. Built entirely in-house by Yasmin and Felipe. When they hired Loocro, the app had just launched with zero audience data, zero install history, and zero proven demand. A brilliant product entering a category the market didn't know it needed yet.

Ad spend metrics reported in USD.

Click play. Or read on.

1,000+
installs (month 1)
2,065
active users (month 3)
$0.84
blended CPI (Android $0.42 · iOS $1.85)

Section 1 · The Problem

Yasmin and Felipe found Loocro through a TikTok video because they were looking for an expert to help them launch their app successfully in the market. They met Loocro not through an ad. It was a simple video where Paulo was talking about growth strategies for apps. By Sunday afternoon they had reached out. Within days, they had signed.

The brief was simple to state and difficult to execute, though: find out if the market would respond. No historical data to lean on. No comparable app to benchmark against. The competitors available didn't have a product as strong as theirs, but they gave us a slight idea of where to move first. But it still was a cold launch into a category the audience didn't yet know they wanted.

"Before we worked with Loocro, we didn't exist. The app was launched and we had to start from zero, understanding the audience, understanding how it would work in the market."

A funded pre-revenue app burning runway on paid media that can't yet measure itself is the riskiest configuration in growth marketing. The challenge wasn't only to acquire users. It was to acquire them while preserving enough runway to find out if anyone would pay.

Section 2 · The Diagnosis

Three things shaped the approach.

01Cold launches need validation, not scale.

With zero install history, zero audience data, and zero comparable benchmarks, optimizing for scale in month one means optimizing on noise. The first job wasn't to maximize installs. It was to learn fast and cheaply which audiences responded, what creative formats converted, and whether the underlying market demand was real.

02Install and subscription are two different jobs that can't run together.

Trying to acquire installs and convert subscribers simultaneously, on a cold app, splits budget and creative attention between two campaigns that need very different audiences, very different messages, and very different feedback loops. Phase 1 had to validate the install economy. Phase 2 could then layer monetization on top of the audience Phase 1 built.

03Android and iOS are not the same channel.

CPI on iOS is structurally 3 to 5x higher than Android due to platform dynamics (Apple's privacy rules, narrower targeting, more competitive auction). Running them under the same campaign, with the same creative and the same bid logic, would either overpay for Android or underdeliver on iOS. They needed parallel structures.

The diagnosis: phase the launch, separate the platforms, optimize for validation before optimizing for scale.

Section 3 · What Changed

We built two phases. First: validate. Then: monetize.

Phase 1 (Month 1): Build the install base.

App install campaigns on Meta for Android and iOS simultaneously, but as fully separate structures. Distinct campaigns, distinct audiences, distinct creative per platform. Each platform got the bid logic, ad format, and message that matched how users on that platform actually behave.

The goal was to find the audience fast and cheaply, then learn before scaling. What we found:

Android CPI$0.42
iOS CPI$1.85
Blended CPI$0.84

For a brand new app entering an untested category, those numbers are exceptional. The market was there. It just needed to be found.

Phase 2 (Week 3 onward): Convert to subscribers.

Subscription campaigns launched alongside the install campaigns, targeting the existing user base for trial-to-paid conversion. The app runs a 3-day free trial at approximately $6/month.

The $6 price is strategic, set internally by Hair Try-On. An aggressive entry point reduces adoption friction at launch, when the priority is proving demand and seeding the subscriber base. The plan is to scale price progressively toward $30/month once the MRR base reaches break-even and the cohort retention data confirms the LTV holds. Cheap in to validate. Optimize price up once the floor is real.

The reporting layer separated install economics from subscription economics from day one. Cost per install told one story. Cost per trial start and trial-to-paid conversion told another. Both had to work for the unit economics to compound.

Section 4 · The Metrics

The numbers that matter at month three are the ones that tell us whether the hypothesis is real.

Installs held steady at the $0.84 blended CPI established in month one, with Android coming in at $0.42 and iOS at $1.85. By month three, the active user base had grown to 2,065. The first subscriber cohort was live, generating MRR under the strategic entry pricing while the campaigns kept feeding new installs into the funnel.

Three months in. New category, new product, cold start. The unit economics are pointing the right direction. The compounding hasn't started yet. The conditions for it to start have.

Section 5 · The Business Impact

Launching a new app into a new category is the hardest growth challenge there is. No audience to retarget. No data to optimize against. No benchmark to beat. Just a product, a hypothesis, and a budget that the team can't afford to spend twice.

The hypothesis was right. The market exists. The product converts. The unit economics, while early, are pointing in the right direction.

For Yasmin and Felipe, what changed at month three wasn't a revenue number. It was the answer to the question they couldn't answer at month one: does anyone want this? The data now says yes. Everything from here is execution on a validated hypothesis instead of speculation on an unproven one.

A pre-revenue app that knows its market is fundamentally different from a pre-revenue app that hopes it has one. The difference is what a properly phased launch buys.

Our experience has been as positive as possible. Before we worked with Loocro, we didn't exist. The app was launched and we needed to understand the audience, understand how it would work in the market. Today we already have more than a thousand installations in the first month. We are super happy with this result.

We recommend Loocro not only for the results, but mainly for getting together on the idea. When you are launching a new app, you need people who are by your side, who encourage you. Who say 'this is right, this is cool,' or 'what if we test it this way?'

The care is genuine. It goes beyond the ads, beyond the goals. When we have any question, Loocro is the first place we go. That's the partnership we have. We recommend Loocro for all segments, all kinds of companies, because we see you as partners, not just deliverers of results.

Yasmin & Felipe Yasmin & Felipe Co-Founders @ Hair Try-On
Questions app founders ask about this case

FAQ

A blended CPI of $0.84 sounds low for a brand new app. What's the catch?
There's no catch, but there is context. The blended figure averages Android ($0.42) and iOS ($1.85), and Android dominates the volume early. The iOS CPI is closer to category average. The Android CPI is below average and won't necessarily hold as the audience saturates and the algorithm has to reach further. The $0.84 blended is a real number for the first ninety days. It's not necessarily the steady-state number for month twelve.
Why launch install campaigns before subscription campaigns?
A subscription campaign on a cold app has nobody to subscribe. You can technically run them in parallel from day one, but you'll waste budget chasing trial starts from users who haven't even decided whether the app belongs on their phone. The phased approach (install first, subscription second) lets the install campaigns build the audience that the subscription campaigns then convert. Cleaner economics, faster learning.
Why is iOS CPI four times higher than Android?
Three reasons. First, Apple's privacy rules (ATT framework) make targeting less precise on iOS, so impressions cost more to reach a qualified user. Second, the iOS audience is structurally more valuable (higher LTV historically), so other advertisers bid more aggressively in the auction. Third, iOS audience pools are smaller in many categories, which raises competitive pressure. All three together produce the gap.
A pre-revenue app spending on paid media is supposedly burning runway. How do you justify it?
Pre-revenue isn't the same as zero-validation. Yasmin and Felipe had funding, a product ready to install, and a hypothesis that needed real-market data to validate. Paid media is the fastest way to get that data. The risk isn't spending pre-revenue. The risk is spending pre-revenue without a phased validation plan, which is when runway burns without learning anything. Hair Try-On's phase 1 budget bought the data that justifies phase 2.
When does an app like this hit profitability?
Depends on the LTV / CAC ratio that the subscription data eventually reveals. Hair Try-On is at month three. Approaching break-even on month-three economics doesn't mean profitable. It means the unit economics are pointing in the right direction. Profitability is typically months 9 to 18 after launch for a well-executed cold-start subscription app. The compounding only starts when the cohort retention data confirms the LTV assumption.
Launching a new app or entering a cold category?

Book a 30-minute diagnosis.

Thirty minutes. Bring whatever numbers you have, even if "whatever you have" is a launch date and a hypothesis. We'll look at your unit economics, your platform mix, your phased launch plan, and what could derail it. You decide what to do with the information.