Gaming
Live-ops intelligence and gaming AI automation for studios running multiple titles.
Zeacle builds end-to-end live-ops intelligence platforms for game studios operating live free-to-play titles. real-time player segmentation, personalised offer engines, churn prediction with intervention playbooks, toxicity detection, and economy intelligence. deployed across titles on a shared real-time event pipeline.
Read the full case study →- →Real-time player data layer across behaviour, spend, social engagement, and in-game economy
- →Live player segmentation with continuously updated cohorts across four dimensions
- →Personalised offer engine: per-player discount, timing, and surface constraints owned by the design team
- →Churn prediction model with segment-specific intervention playbooks inside the retention window
- →Economy intelligence dashboard: price elasticity, currency flow, and inflation leading indicators
Game studio live ops platform for retention and monetisation
Live free-to-play titles generate strong launch numbers and then lose players in the same shape. a sharp drop between day 7 and day 30, a long tail of players who installed and never converted, and a high-value cohort that monetises hard but burns out fast because the offer cadence is wrong for them.
The live-ops team is talented but the tooling is unfit for the scale. Player segmentation is manual and lags by days. Targeted offers are technically possible but operationally impractical at the speed game economies need. Economy tuning runs on week-old data. Zeacle builds the game studio live ops platform that fixes all of it.
Real-time player segmentation and personalisation
The platform fuses player behaviour, monetisation, social signals, and in-game economy data into one continuously updated layer running on a real-time event pipeline. The player segmentation engine clusters players across behaviour, spend, social engagement, and skill progression. Cohorts update continuously as players move between segments.
The personalised offer engine targets every offer to the right player with the right discount, at the right moment in their session, against an in-engine surface that respects the design team's constraints. High-value players see scarcity and exclusivity offers that lift ARPPU. At-risk players see targeted retention offers calibrated to the specific reason they are at risk.
The studio's pre-platform offer system showed the same offer to every player in a coarse cohort. That meant margin erosion on high-value players and missed interventions on players who were actually churning. ARPDAU climbed by 28% across both titles inside the first 60 days.
Churn prediction and live-ops automation
The churn prediction model scores every active player on a rolling basis and surfaces at-risk players inside the intervention window where action still works. Intervention playbooks are segment-specific: a skill-plateau player receives a different intervention than a spend-fatigue player or a socially disengaged player.
Live-ops manual operations time per week dropped by 62%, freeing the team to focus on creative interventions and event design instead of segmentation spreadsheets. Win-back conversion on lapsed players tripled against the prior baseline once the platform could target win-back offers with the same precision as live offers.
Gaming AI automation handles the segmentation, scoring, and offer targeting that humans cannot do at live-ops speed. Designers retain full control of every offer in the catalogue. The engine targets within constraints the team owns. It does not invent offers the design team has not approved.
Toxicity detection and economy intelligence
The toxicity and anti-cheat layer detects abuse, harassment, and exploit patterns in near real time, with a tiered response policy from automated warnings up to ban referrals reviewed by the community team. Toxicity-related player reports dropped sharply once the detection layer caught patterns at telemetry level rather than waiting for player complaints.
The economy intelligence module gives the design team a daily view of price elasticity across every offer SKU, currency flow across the in-game economy, and the leading indicators of inflation or sink imbalance. The economy team stopped answering last week's question and started tuning on today's data.
Event management workflows integrate with the segmentation layer so live events target the right cohorts automatically. Event performance feeds back into the player data layer, improving the next event's targeting without manual post-mortem spreadsheets.
Proven results: D30 retention up 41%, ARPDAU up 28%
An independent game studio operating two live free-to-play titles deployed Zeacle's live-ops intelligence platform in 29 days. Day-30 retention climbed by 41% across both titles. ARPDAU grew by 28%. Lapsed-player win-back conversion tripled. Live-ops manual operations time dropped by 62%.
The platform has since been extended to a third title in the studio's pipeline. The foundation supports the studio's planned move into a publishing model where the same live-ops infrastructure can be offered to partner studios as a managed service.
Yes. The platform is designed to scale from one title to many on the same event pipeline and segmentation infrastructure. Single-title deployments scope to the telemetry and economy surfaces relevant to that game while preserving the architecture for future titles.
Designers own the offer catalogue, discount bounds, and surface constraints. The engine selects which approved offer to show which player at which moment. It does not generate offers autonomously. Every offer in production has been explicitly approved by the design team.
We integrate with your existing analytics pipeline, whether that is a custom event bus, Unity or Unreal telemetry, backend game services, or a third-party analytics platform. The real-time player data layer normalises events into a consistent schema regardless of source.
Yes. The detection layer runs separate models for harassment patterns, exploit behaviour, and economy manipulation. Each has a tiered response policy. Community team members review ban referrals. Automated warnings handle low-severity cases without human queue time.
Our reference deployment across two live titles was in production in 29 days. Week one is telemetry normalisation and the player data layer. Weeks two and three bring segmentation, offers, and churn prediction online. Week four adds economy intelligence and the operator dashboard.