Reward campaigns on platforms like Zealy or Galxe can feel either motivating or unfair depending on how you distribute rewards. A practical approach from the transcript is to “distribute your rewards by bucket,” meaning you group participants by rank ranges and assign different reward amounts to each tier.
To keep things fair and add variety, the speaker also recommends holding back a smaller share for random participants outside the top tier. A specific guideline mentioned is to allocate about 10% to random participants outside the top 100 bucket.
The rank-bucket approach to reward distribution
In a rank-bucket system, participants are grouped into rank ranges (buckets). Instead of treating every rank the same, you assign different reward amounts to different buckets.
This structure supports two goals:
- Merit-based incentives: Participants understand that better performance (higher rank) leads to better reward tiers.
- Controlled allocation: You can plan how much of your total reward budget goes to each segment of the leaderboard.
The transcript summary emphasizes that the bucket method typically involves mapping different rank ranges to different reward amounts—for example, one bucket might represent a lower segment of ranks, while another bucket represents a higher segment.
How rank ranges map to different reward amounts
The key implementation idea is straightforward: once you decide on your rank buckets (the rank intervals), you decide how rewards will differ across them.
From the guidance provided, the bucketed system works like this:
- Define rank buckets based on where participants land on the leaderboard.
- Assign distinct reward amounts to each bucket, so participants in different rank ranges receive different rewards.
While the transcript summary does not prescribe exact bucket boundaries, it does make clear that you should use separate tier ranges rather than a single flat payout.
Why reserve rewards for randomness
If all rewards are locked to the top bucket or top ranks only, the system can feel overly narrow: discovery and continued participation may drop because fewer people see a meaningful chance to earn.
The transcript’s recommended refinement is to reserve a smaller portion of rewards for random participants who are not in the top tier. This creates a balance:
- Participants still have a reason to rank competitively (because most rewards are tiered by rank buckets).
- But there is also a chance for participants outside the highest tier to receive rewards (because a portion is allocated randomly).
This randomness pool is intended to “add variety” and maintain fairness beyond the very top of the leaderboard.
Suggested guideline: 10% random outside the top 100
The transcript summary includes a practical rule of thumb: allocate around 10% of the rewards to random people outside the top 100 bucket.
Interpreting that guideline within a rank-bucket system:
- You still structure the majority of rewards by rank buckets.
- Separately, you set aside about 10% for a randomized selection.
- The random selection should be drawn from participants not included in the top 100 rank bucket.
This approach keeps the reward structure predictable for top performers while ensuring that participants who are not in the top bucket still have a chance to earn.
Putting it together: a simple reward design workflow
If you want to apply the transcript’s strategy in a clear, durable way, use a step-by-step workflow:
- Choose your rank-bucket tiers.
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Decide which rank ranges you will treat as separate buckets.
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Allocate the majority of the budget by bucket tier.
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Assign different reward amounts to different rank ranges.
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Create a randomness pool.
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Reserve a smaller share of the total rewards for random participant allocation.
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Use the suggested randomness rule.
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Set aside about 10% for random participants outside the top 100 bucket.
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Run the quest and distribute accordingly.
- Winners in each bucket tier get their tier-based rewards.
- The reserved portion is then distributed randomly among the eligible participants outside the top 100.
Benefits for engagement and perceived fairness
This hybrid strategy—bucketed payouts plus a randomness reserve—is designed to address a common challenge with quests: participants may want both measurable performance incentives and some level of inclusive opportunity.
By distributing most rewards across rank buckets, you reward effort and performance in a structured way. By reserving a smaller portion for randomized participants outside the top bucket, you avoid making the system feel like only a small slice of people can benefit.
In the context of Zealy or Galxe-style quests, that matters because users typically evaluate whether rewards feel achievable and whether the rules look transparent.
Conclusion
A rank-bucket rewards structure is a practical way to run quest campaigns: group participants by rank ranges, assign different reward amounts to each tier, and keep most rewards merit-based. To improve fairness and add variety, the transcript’s guideline is to reserve about 10% of rewards for random participants outside the top 100 bucket.
If you’re planning quest rewards, define your rank tiers first, then build a dedicated randomness pool so you can balance competitive incentives with broader participation.
