Ant Colony, Bee Colony: Iterations and Pivot

ANt Colony

Based on market research, I identified that arcade idle games were emerging as a popular genre in mobile gaming. Prior tests with three arcade idle games showed strong in-game metrics. In one such game, Monument Builder, players collected resources and NPC builders used these resources to gradually construct monuments. The promising metrics from this game inspired me to develop a new concept: Ant Colony.

Concept and Initial Development

In Ant Colony, the player controls a main ant that directs worker ants by dropping resources. The game showed promising CPI (Cost Per Install) and impressive in-game metrics from the outset, even with limited content. The visual progression of creating new ants provided a satisfying experience for players.

Seeing the potential, we decided to iterate further and planned two-week sprints in consultation with stakeholders. During this phase, I brainstormed various types of ants, enemies, and expansion ideas. I worked alongside another programmer, assigning features and reviewing pull requests to ensure a smooth development process.

Performance Optimization

As the number of worker ants grew to 100 or more, we faced performance issues. To address this, I avoided using skinned renderers for worker ants, opting instead for DoTween animations. This approach reduced draw calls by 50% in certain cases because the worker ants were dynamically batched. Additionally, I optimized performance by not processing off-screen objects.

Final Outcomes and Learnings

Ant Colony achieved one million downloads but didn’t scale as expected. Marketability was an issue when trying to scale, and monetization challenges resulted in a lower than expected LTV (Lifetime Value).

In summary, Ant Colony started strong with effective visual progression and engaging gameplay but faced challenges in scaling due to marketability and monetization issues.

Bee Colony

Following the high playtime of Ant Colony, we aimed to create a new game by building on its gameplay elements, with the goal of achieving a breakthrough in marketability. For inspiration on gameplay and visuals, I and the team drew references from the Bee Movie.

Development and Technical Approach

In Bee Colony, I implemented behavior tree AI for the bees using Opsive’s Behavior Tree asset. This approach aimed to create a more dynamic and engaging AI system.

Market Challenges and Decision to Pivot

Despite these efforts, we faced challenges as the Cost Per Install (CPI) increased and player playtime decreased. These issues indicated that the game was not resonating as hoped with our target audience. As a result, we decided to pivot and explore other game concepts.

In summary, Bee Hive was a learning experience that helped us understand the importance of balancing innovative gameplay mechanics with marketability. Despite initial enthusiasm, the project highlighted the need for adaptability in response to market feedback.

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