How GlamUp grows

Faceless TikTok slideshow: girl without makeup -> girl with makeup with GlamUp branding -> app screenshot of the personalized routine. Hundreds to thousands of posts used the identical on-screen hook ('never saw myself with the / right makeup routine' + two crying-face emojis on a third line), identical caption ('I will be unstoppable' + exhausted emoji), same hashtags and one of ~10 proven audios. On-screen text kept small and off-center, 'Gen Z Insta-girly' style. Hook wording, line count and text size were tested; other audios did not go viral. Earlier formats that failed: Latina makeup slideshows, color theory, celebrities, makeup tutorials. Posted across ~20 UGC creators' accounts.

After the (blurred) scan result, the paywall offers two buttons: 'View results' (-> paywall) or 'Invite 3 friends'. Inviting leads to a share-your-invite-code screen; results unlock free once 3 friends download, finish onboarding and redeem the code. Friends get no benefit beyond helping. Target users are 13-25 and don't want to pay, so they post codes in TikTok comment sections. Branch was used for attribution/referral links. Repeated on Sprout and the newest app.

Hard paywall with weekly tiers $8.99/$10.99/$12.99. On close, immediately show a discounted paywall at 50% off ($4.49/$5.49/$6.49). Not used on Sprout.

Place social-proof screens between onboarding questions, tailored to the previous answer: if the user picks acne as their skin concern, next screen says '80% of our users have acne as their top concern'. In the stealth app, after asking 'Where are you based?', it shows how many users the app has in that location.

Found photos of attractive women on Pinterest, ran them ~10 times through a separate makeup-removal app until plain, then enhanced with GlamUp's makeup to create maximum before/after contrast. Used as app demos rather than real users' photos (privacy).

The copy button doesn't copy the bare code; it copies a template like 'Download GlamUp and use my referral code ...'. Users paste it into TikTok comments, so the keyword 'GlamUp' ranks high in TikTok search and searches surface the app's videos.

Raised the weekly subscription from $3/week to $9/week (weekly and monthly plans only, no annual).

User picks a scan, gets photo instructions, takes a selfie or video, sees an 'Analyzing' screen, then all results are blurred behind the paywall. The scan is fake; the AI API is called only after payment to avoid wasted API cost.

After onboarding questions, a 'Leave a rating' screen triggers the native rating pop-up; to continue, users must tap an 'I rated' button (whether or not they rated). Same screen used on Sprout. Newest app dropped the forced button (co-founder uncomfortable) and uses a plain 'Leave a rating' + continue, which still works. Similar approach for the notification permission page.

Onboarding is for priming, not analytics: ask about goals/concerns (beauty goals, skin concerns; job goals) so users feel they need the app, then tailor a plan. On Sprout, added 'fun fact' pages after questions: e.g. 'Have you tried other job search apps?' is followed by a Sprout-vs-other-apps comparison; 'What is your goal?' is followed by 'you can reach that goal with Sprout way more than without'. Then rating, notifications, referral code.

First version: scan your face, get text-based makeup tutorials. It did not work on social media. Changed the core feature so users scan their face and see a before/after of how they would look with specific makeup looks, a visual result that suits TikTok content. Had tested several candidate features before landing on it.

First version: scan your face, get text-based makeup tutorials. It did not work on social media. Changed the core feature so users scan their face and see a before/after of how they would look with specific makeup looks, a visual result that suits TikTok content. The team usually tests a few candidate features; once a feature is viral it works ~10x better and you know within the first week.

For consumer apps the priority of onboarding is to prime users to convert, and only second to learn about them. Use 10+ pages. Skip questions that don't drive conversion (email, phone). GlamUp asked: what's your beauty goal, skincare concern, what do you always struggle with, what products do you use. She studied ~100 apps' onboardings, learning only from high-MRR apps.

Inspired by Umax, a looksmaxing app for men built by founders in the same HF0 batch as her co-founder, they built a glow-up app for women focused on makeup, after checking that no app like it existed on the App Store yet.

Work through a list of distribution strategies, 2-3 weeks each: Reddit marketing; faceless TikTok/Instagram content (slideshows, videos, different CTA variations); face UGC (AI UGC or real creators); and, with capital, paid ads and influencers. Within each category exhaust variations (e.g. slideshow vs video, different slideshow styles) and keep iterating until one goes viral, then scale it.

Each co-founder ran ~3 TikTok accounts, warmed them up and tested formats. Only after a format went viral did they hire creators (30-40 at GlamUp's peak), trained through a course covering account creation, warm-up and exact content details (text size, placement, etc.).

Tiers include e.g. 2 or 4 scans per week. Many scan types (color analysis, glow-up guide with six makeup guides, etc.) make users want more scans; running out triggers an upgrade paywall to a higher tier.

Took Umax (face-rating / looksmaxxing app for men, seen early via a co-founder's HF0 cohort) and reframed it for women around makeup and skincare products rather than face ratings; checked the App Store and found few such apps. Built a barely working MVP in 3-4 weeks and worked on distribution in parallel; v2 redesign took another month.

For 3-4 months marketed the text-based first version via influencers, faceless accounts, the founder filming her own UGC, Reddit marketing and Discord. Nothing worked until the feature changed (see pivot). Her takeaway: if several variations and methods fail, the feature is not viral and you learn that quickly.

For 3-4 months marketed the text-based first version via influencers, faceless accounts, the founder filming her own UGC, Reddit marketing and Discord. Nothing worked until the feature changed. Her takeaway: if several variations and methods fail, the feature is not viral and you learn that quickly.

When starting out, her whole day was blocked with interviews of UGC creators. She accepts roughly 50% or fewer, favouring people who take it seriously, so the program's foundation is good.

With no money, Nicole or her co-founder made all the content until something went viral; only then did they scale by hiring creators. Finding viral formats is the founder's job (hours and days of research), not the creator's. She still films the example videos in each app's content bank.

Across her apps she has raised prices by ~20-40% and seen the conversion rate go up. Treats price as something to test until you find the point that works, with tiers A/B tested.

GlamUp started with $10-20K as its entire runway. No paid acquisition: growth came from viral organic content (UGC and faceless accounts). All money is reinvested in the business or new apps.

For Glam Up (2024) they ran faceless UGC on TikTok and Instagram. She designs the app itself to be innately shareable on social (visual results like color analysis and makeup looks on your face), then tests viral formats and doubles down on the winner.

All apps are hard-paywalled; the scan results (Glam Up) and core features require a paid plan. Business model is a weekly or monthly subscription. Paywalls run on Superwall, revenue tracked in RevenueCat.

After Glam Up and Sprout, built two more apps in 3 months and grew them with the same design + UGC distribution system.

Apply consumer-app design principles: welcome pages, onboarding questions that personalize the result (job postings tailored to answers in Sprout, chosen scan in Glam Up), and priming users to convert. Treat the product design as part of distribution by making outputs innately viral for social media.