July 9, 2026
About
ShareChat's Quick TV is a fast-growing microdrama app in India and a flagship subscription product within ShareChat's microdrama ecosystem, which reaches over 60 million monthly active users and 400 million daily episodic views. Designed for binge-watching short dramas, thrillers, and romance mini-series, Quick TV delivers bite-sized, vertically formatted videos ranging from 1 to 5 minutes, making it ideal for quick entertainment during commutes or breaks.
Challenge
Quick TV faced a common but critical challenge in the freemium subscription model: high drop-off rates between trial sign-ups and paid subscriptions. While the platform successfully attracted users to start free trials, a majority of them cancelled before converting to paying subscribers. The core issue was clear that optimising only for trial sign ups wasn’t enough. Quick TV needed to identify and acquire users who would not only start trials but also complete them and convert to paying subscribers. Business growth required focusing on subscription revenue, not just trial volume.
Solution
Quick TV partnered with Moloco to develop a custom optimization approach that could identify users with genuine intent to subscribe, not just intent to try. Rather than treating all trial sign-ups equally, the strategy centered on building a new optimization event that more accurately reflected the user behavior Quick TV actually wanted to drive.
Building a smarter optimization signal
Moloco’s team worked with Quick TV to create a composite optimization event that used the trial window itself as a signal. By analyzing user behavior during the trial period, the AI model learned to identify patterns associated with early cancellation and applied this understanding to filter out low-intent users while targeting. Trial starts were treated as a positive input signal, while users showing cancellation-prone behavior were used as a negative signal. The result was a model trained to find users who would remain subscribed past the early cancellation window.
Leveraging per-advertiser AI to improve precision
A key factor in the approach was Moloco’s per-advertiser AI model, which learned specifically from Quick TV’s own user data along with pooled signals across the platform. This allowed the model to build a precise behavioral profile of a high-intent Quick TV subscriber — accounting for the nuances of the Indian streaming market and Quick TV’s specific content category. The AI model continuously refined its understanding of what a valuable user looked like as more conversion data became available.
Result
By targeting paid subscription intent rather than just trial volume, Quick TV transformed their User Acquisition from a quantity play into a quality-focused growth engine. Within a 45-day period (December 18, 2025 – January 31, 2026), Quick TV achieved:
- Cost per subscriber reduced by ~46% in a span of 45 days (pre-post) of the new custom ML event launched on 17th December 2025
- D7 cancellation rate improved by ~43% within the same observation period i.e 45 days (pre-post) of the new custom ML event launched on the 17th December 2025
- This gain of efficiencies led to an increase in the trial to subscription rate and improved the overall funnel for quickTV
Customer
ShareChat
Headquarters
India
Industry
Réseaux sociaux
Product
