BeautyPlus AI Filter: 0-to-1 GTM Launch
Context
BeautyPlus had built its reputation on traditional beauty retouching like skin smoothing, facial reshaping, filters, stickers, templates, and creative tools, and more. In early 2023, generative AI was gaining momentum across the consumer app industry, with apps and creators worldwide beginning to explore AI-styled photo transformation on TikTok and Instagram. Every major competitor was moving in this direction, and whoever shipped the right AI feature first stood to see a spike in downloads and new user acquisition.
The strategic question was not whether to launch AI filters. It was whether we could move fast enough to matter. The standard client-side development cycle at BeautyPlus ran on a quarterly release cadence, and by the time a native feature shipped, the trend window would already have closed.
I was the PMM leading overseas GTM strategy for this project. My scope covered go-to-market planning, in-app channel management, localization for prioritized markets such as East Asia and North America, and commercial growth across the full product lifecycle.
The Challenge
- The core challenge was turning an AI feature into a genuine growth lever. The immediate goal was to capture momentum and drive user acquisition, with revenue as the longer-term objective. That meant shipping something compelling fast enough to matter, then finding which specific effects actually resonated with our users across markets. Finding product-market fit was the ongoing work: continuously mapping market signals to our existing user base, judging which effects would land in which markets, and making that call quickly and repeatedly as new AI styles emerged week over week.
- The second challenge was building an incremental revenue model from AI effects without pulling users away from the subscription model that already drove the business. BeautyPlus's core business ran on subscription revenue from retouching features, and we needed a commercial approach that added on top of that rather than competing with it.
Phase 1
Bypass the release cycle and launch as H5
The decision to build and ship as an H5 MiniApp rather than waiting for native client integration was one I pushed from the marketing side. An H5 web-based flow embedded in the app could be built and deployed in weeks, not months. It also meant full funnel instrumentation from day one, so we could track every step from entry to save and iterate on both the product flow and the promotion strategy in near real-time.
The H5 funnel had seven steps: enter, browse effects, upload photo, confirm generation, wait, save, share. We tracked conversion at each layer. This gave us the data layer we needed to make fast decisions.
Cold start promotion strategy: We allocated the highest-priority in-app placements to this project, concentrating promotional resources to maximize early exposure. The goal was to gather enough data quickly to make confident, data-backed optimization decisions in the next phase.
Phase 2
Expand fast, promote smarter
Once the product flow was validated, Phase 2 shifted to accelerated iteration. The generative AI model landscape was moving quickly, and new aesthetic styles, including pixel art, clay, and retro manga, were gaining traction as fast as older ones plateaued. We partnered closely with the design team to ship new effects as quickly as the underlying models allowed, giving users a reason to come back and spend more time exploring within the MiniApp.
With more data on effect-level performance, including usage volume, save rate, and repeat engagement by market, we could adjust promotional weight by effect rather than treating all content equally. High-save-rate effects got banner and pop-up placement, while lower-performing effects were deprioritised or eventually delisted.
Japan-specific localisation became a deliberate commercial decision. Japanese market data pointed toward portrait-realism and stylised illustration aesthetics over the bold Western-facing styles that performed well in English-speaking regions. We built separate effect ordering configurations for Japan and ran localised promotional creative.
Phase 3
Introduce incremental monetisation without cannibalising subscriptions
The commercial challenge in Phase 3 was structural. BeautyPlus's subscription model was built around core retouching features. A large portion of the AI filter audience were free users engaging with the novelty of the effects, not necessarily looking for a long-term feature they would pay a monthly fee to access.
The approach was to introduce a credits-based bundle system alongside subscriptions, allowing users to make one-time purchases for AI content without requiring a full subscription commitment. Subscriber benefits were structured deliberately: subscription users got access to a base tier of AI effects as part of their existing plan, creating a conversion pathway from free AI users into subscribers over time.
Channel economics mattered more than channel volume. We managed three in-app channels:
| Channel | Click-through Rate |
|---|---|
| Pop-up windows | ~22% |
| Homepage rotating banners | ~1% |
| MiniApp navigation tab | ~0.4% |
The Halloween seasonal campaign became the proof-of-concept for the paid bundle model at scale. We launched 13 Halloween-themed effects as a limited-time bundle, drove the campaign through pop-ups and banners timed to peak engagement windows, and made a deliberate call to keep video effects minimal. At the time, technical constraints in the H5 environment limited video uploads to under 10 seconds and did not support in-app video trimming, which created too much friction for most users.
80%
MoM subscription lift during Halloween campaign
22%
Halloween bundle share of AI MiniApp credit revenue
2% → 22%
Free-to-subscriber conversion in 6 weeks
+10%
Q4 homepage revenue vs prior quarter
What I Learned
Speed is a strategic choice.
The decision to ship as H5 rather than waiting for native integration was the decision that made everything else possible. It accelerated our ability to respond to market trends, compressing what would have taken quarters into weeks. In the AI space where trends move fast and windows close quickly, this speed was what kept us in the game.
Trends are readable; audience fit requires ongoing work.
We could see which aesthetics were gaining traction from competitor moves and social signals. The harder, continuous job was matching those trends to our specific user base across markets, not assuming that what worked in Japan would work in the US. Building the data infrastructure to make those calls quickly was what turned a one-time launch into a repeatable growth engine.
Channel economics are not always visible in the headline metric.
Pop-ups drove 22x more clicks than banners, and looked like the clear winner. But when we looked at purchase conversion, banner users bought at a significantly higher rate. The reason: someone who navigated to a banner was already exploring the app with intent. Someone interrupted by a pop-up mid-session was not. Volume and purchase intent are different things, and confusing them would have sent budget to the wrong place.
A new monetisation model needs to create value, not just extract it.
The credits model worked because it gave users who had no intention of subscribing a low-commitment way to try AI features, meeting them where they were before any push toward conversion. The credits model gave users access to AI features that matched their interest, which drove more active usage before any revenue conversion happened. The result was incremental revenue and improved subscription conversion, rather than the cannibalisation we were trying to avoid.
All metrics and performance data referenced in this case study reflect the 2023–2024 period. Sensitive figures have been anonymised or aggregated, and no proprietary or commercially confidential information is disclosed.