Incentive Program Assessments & Recommendations

[CastleCapital]

As the Research Member of the ARDC V2, Castle Labs (@CastleCapital) and @DefiLlama_Research were tasked with evaluating and providing recommendations for improving the DAO’s incentive programs.

This report presents a comprehensive evaluation of incentive programs to date within Arbitrum and across other ecosystems to identify best practices, understand failures, and propose improvements for future incentive design.

Our mandate was twofold:

• Internally, Arbitrum DAO’s current and historical incentive programs will be assessed and categorized using a consistent evaluation framework, and their effectiveness will be analyzed at the program and protocol levels.
• Externally, it will compare Arbitrum’s approach with incentive mechanisms used in other major ecosystems, such as Optimism, zkSync, Sonic, and Avalanche, and extract best practices that could be adapted to improve future programs.

The output is structured to support DAO delegates, contributors, protocol teams, and foundation stakeholders in designing incentive mechanisms that are better aligned with long-term ecosystem growth, more capital-efficient, and more adaptive in execution.

This document is intended to serve as both a retrospective and a forward-looking guide, providing the DAO with the tools and evidence base needed to evolve its incentive strategy more intentionally, effectively, and transparently.

---

We have documented our entire research in this Google Doc — Navigate through the document using the Tabs on the left-hand side to jump to specific sections.

The document includes:

• Introduction: You’re here now. This section outlines the goals of the report, the scope of research, and how to navigate the findings.
• DAO Incentives Timeline: This tab provides a chronological overview of the major incentive programs deployed by the Arbitrum DAO to date, including timing, scale, and context. It helps situate each program in relation to DAO governance, ecosystem maturity, and overlapping initiatives.
• Summary of Prior Research: The summary distills key takeaways, critiques, and lessons learned to inform this report’s analysis and recommendations. It serves as a foundational reference point for understanding how Arbitrum’s incentive strategy has been evaluated to date.
• Incentive Evaluation Framework: A structured evaluation model used to assess incentive programs across key areas. Each section includes detailed criteria for comparing design intent, implementation, effectiveness, and adaptability across programs.
• Internal Analysis (Mini): A high-level summary of Arbitrum’s incentive programs, STIP, STIP Backfund, STIP Bridge, and LTIPP, assessed using the Incentive Evaluation Framework. This section highlights major trends, design patterns, and performance insights, offering a fast but informed read for DAO delegates.
• External Analysis (Mini): This section provides a concise overview of incentive programs across other ecosystems (including Optimism, zkSync, Sonic, and Avalanche), benchmarked against the same evaluation framework. It surfaces shared challenges, innovations, and best practices applicable to Arbitrum’s future programs.
• Data Analysis & Modelling: This section presents two integrated data studies that examine the relationship between incentive design and outcomes. These studies offer a data-driven foundation for understanding what drives sustainable impact.
  • The first applies a structural classification model to link program and protocol-level incentive types, goals, and adaptability to measurable success.
  • The second introduces a retention analysis methodology, demonstrated through a case study on Compound under LTIPP, to evaluate whether protocols retained users and capital beyond the incentive period.
• Recommendations: A unified set of key lessons, takeaways, and actionable recommendations drawn from internal and external research, our data analysis exercises, and surveys. These recommendations are targeted at future incentive program design and DAO governance processes.
• Appendices
  • Internal Analysis (Full)
  • External Analysis (Full)
  • Synthesis of Previous Research (Full)
  • Surveys
  • Sources

---

We have included the main body of the report (without Data Analysis & Modelling) below in collapsible sections.

DAO Incentives Timeline

Timeline of DAO Incentives

The Arbitrum DAO’s approach to incentive programs has evolved through several distinct phases, each marked by context, scope, and strategic focus shifts.

Pre-STIP: A Cautious Beginning

Initially, the Arbitrum DAO proceeded cautiously. There was an initial reluctance from the DAO to bootstrap a grant program, with the DAO waiting for the Arbitrum Foundation to create an incentive framework. The first step into incentive programs was the Camelot proposal, Accelerating Arbitrum - leveraging Camelot as an ecosystem hub to support native builders, in June 2023. Camelot proposed leveraging its platform to support native builders and stimulate liquidity within the Arbitrum ecosystem. This proposal was instrumental in kickstarting broader discussions within the DAO about the potential of grant programs but was unsuccessful in passing.

Proposed Cost: 1.5m ARB distributed monthly over 6 months, for a total of 9M ARB (~$14M) tokens.

STIP & Backfund: Experimentation and Adjustment

The DAO’s eagerness to experiment with incentives led to the Short-term Incentive Program (STIP), proposed in September 2023. Following Camelot’s program, a working group was created to explore possible DAO-led incentive programs. The recommendations from the working group and the general sentiment in the DAO culminated in STIP, which was created as an initial program to experiment with grant distribution and new incentive models to leverage data for long-term incentive programs. STIP funded 30 protocols with the aim of increasing volume, transactions, users, and liquidity. However, the program faced challenges and debates within the DAO, with some members expressing uncertainty about the true impact of grants and advocating for more research.

Cost: 50m ARB (~$46M)

In October 2023, the STIP Backfund was proposed to extend the original STIP budget. Proposed by Savyy, the backfund aimed to include additional applicants who were initially approved but later cut off due to funding limitations. Applications were sorted by approval based on the number of votes on their application. Due to the lack of standard practice across voting delegates (e.g., ensuring For votes for ARB asks tallied equally with the funding available), a sampling bias was introduced, leading to only a fraction of the protocols successfully voted For being approved for STIP. There were many consequences to this voting procedure, including the necessity for protocols to lobby others in order to secure their vote, either through composable partnerships (e.g,. directing liquidity or incentives on specific tokens and tools) or other mechanisms, potentially affecting smaller and newer protocols. The necessity to carry out a Backfund raises questions about the initial funding allocation for STIP, the target of the incentives, and the guidelines for applicants in their asks.

Cost: 21.4m ARB (~$17M)

LTIPP & STIP Bridge: Refinement and Growing Pains

The Long-term Incentive Program Pilot (LTIPP) was proposed in December 2023. The LTIPP was meant to be a pilot program to gather evidence to make informed decisions on a long-term incentive program. The program also focused on overcoming some of the main challenges in the STIP. LTIPP introduced a council responsible for evaluating proposals, advisors providing feedback and assistance to applicants, and more flexibility on the protocol side to experiment with innovative incentive distribution structures. Protocols that previously participated in STIP were not eligible for LTIPP. Not all delegates were enthusiastic about LTIPP, as there was a lack of gathered data on previous programs to leverage their learnings for future ones, and a lack of meaningful distribution changes compared to previous programs.

Cost: 35.66m ARB (~$40M) for 12 weeks.

In March 2024, the STIP Bridge was proposed. The initial scope of the STIP bridge (STIP.b) was working as an interim program between the STIP and a long-term incentive program. The program was reserved for protocols that previously received STIP funds and were not eligible for LTIPP. This is one of the most contested incentive grants conducted by Arbitrum, which divided the opinion of the DAO. The program was mainly funded for ‘fairness’, ensuring the DAO retained key protocols from the ecosystem while facing increased competition from other chains, rather than the program emerging from clear ecosystem goals with precise targets for incentivization.

Cost: 37.6m ARB (~$62.4M) for a 12-week program.

Incentive Detox: A Period of Reflection

In July 2024, following the challenges and mixed results of previous programs, L2beat proposed an Incentive Detox, lasting 3 months from October 2024 to January 2025. Rather than continuing to experiment with more incentive programs, this time should be devoted to “analyzing the results of the previous programs and designing a new, long-term one based on what we have learned”. As part of this proposal, the “incentive working group” would be revived and used for discussions around the subject, and participation would be seen from previous advisors, program managers, applicants of previous programs, and proponents of new incentive programs. This has been a transitional phase for the DAO, reflecting on what has been done so far and trying to leverage it for data-driven improvements on future programs.

Post-Detox: Towards a More Strategic Approach

The post-detox period saw the DAO begin to implement a more strategic and targeted approach to incentives, focusing on addressing past shortcomings and aligning incentives with clear ecosystem goals.

In December 2024, IOSG’s “ARB’s Wake-Up Call: A Critical Pivot is Necessary” proposal was introduced. This proposal recognizes some of the failures of previous incentive programs, especially in terms of retention, with most metrics of incentivized protocols going back to pre-incentive levels after the program ends. As a solution, it proposes an incentive program focused on the key objective of strengthening Arbitrum’s spot market: building deep liquidity for bluechip assets, expanding token diversity by attracting high-demand assets, and creating a flywheel of liquidity, trading volume, user retention, and innovation. Compared to other programs, this one would distribute incentives ex-post, directly to liquidity providers who supply specific assets to qualified DEX pools, based on their performance. For this reason, the program shifts its focus from projects to a user-centric approach.

Proposed Cost: N/A

In January 2025, Patterns proposed “ARB Incentives: User Acquisition for dApps & Protocols”. After the end of the detox period, Patterns proposes an incentive program to cover the costs of dapps and protocols, off- and onchain user acquisition campaigns, and measure their ROI. This is proposed as a direct solution to the retention issue for previous incentive programs. The program is intended to be iterative and complement the IOSG proposal. In particular, it recognizes the importance of ensuring an efficient distribution of incentives through an effective marketing & communication strategy.

Proposed Cost: $3m for the first 3-month iteration, for 10-20 projects.

Most recently, in February 2025, the DAO considered “MAGA - Merkl x Jumper”. This new proposal stems from challenges highlighted by the previous programs, especially in terms of program visibility, fragmentation of rewards and KPIs, and static programs with limited flexibility. On the other hand, Merkel and Jumper propose an incentive program that they can manage by themselves, distributing rewards from one cohesive frontend. Having a campaign managed by them reduces operational overheads from the DAO and can reduce the fragmentation of the ecosystem’s exposure. Furthermore, this single UI can contribute to tracking and aggregating data, enhancing the program’s transparency and accountability. Last but not least, an important change in the program is the focus on “new liquidity” and relevant KPIs on a pool and protocol basis. This proposal comes at a time when the incentive detox proposed by L2beat has finished, and the DAO is reorganizing to assess the best way to move forward. Currently, this proposal has been put on hold due to the imminent restructuring of the DAO’s operations, with OpCo being spun up and OCL/AF entering the DAO scene.

Proposed Cost: Maximum of 60M ARB (~$30M) over 3x3-month seasons.

Lastly, the DAO is currently considering Entropy’s Defi Renaissance Incentive Program (DRIP). DRIP is a targeted incentive program for Arbitrum, running in 4x3-month seasons, focused on specific assets and activities to enhance Arbitrum One’s DeFi ecosystem. It uses a selection committee, RFPs for partners, and data-driven evaluation to iterate and improve incentive strategies each season. DRIP incorporates learnings from past programs, emphasizing clear incentives, experimentation, targeted activities, and the importance of not disrupting market balance. The main objective is not trying to grow markets that have already reached an organic, stable level, as growth will just revert once the program ends, but to locate and target areas that have growth potential on Arbitrum.

Proposed Cost: 80M ARB (~$26M) over 4x3-month seasons.

Summary of Prior Research

Summary of Prior Research

This section reviews the existing literature and research on Arbitrum incentive programs with an executive summary.

A significant amount of research has shared insights on incentive programs before, during, and after their introduction. These findings are precious, and one of the main goals of these initial incentive programs on Arbitrum. For this reason, they should be considered and leveraged for data-driven improvements to future programs.

The full synthesis is extremely lengthy, please revert initially to the executive summary on this page. To dive into the entire synthesis of prior research, please see this section in the appendix.

1 Executive Summary

1.1 Context and Purpose

Since its inception, the Arbitrum DAO has committed significant resources to ecosystem growth through general incentive programs. These initiatives, ranging from short-term liquidity mining efforts to longer-term grant frameworks, represent an evolving experiment in decentralized capital allocation, with the broader purpose of creating a framework for long-term incentive programs.

This executive summary synthesizes the existing research and analysis on Arbitrum’s four main incentive programs: the Short-Term Incentive Program (STIP), the STIP Backfund, the STIP Bridge, and the Long-Term Incentives Pilot Program (LTIPP). Drawing from community research (notably by Blockworks Research and Chaos Labs), program dashboards, delegate discussions, and reports, this summary identifies core themes, lessons learned, and system-level challenges that have emerged through the lifecycle of these programs.

The intention is not to offer new recommendations but rather to provide an overview of incentive programs in Arbitrum.

1.2 Program Overviews

• STIP (Short-Term Incentive Program)
  • $50M ARB disbursed to 29 protocols from a pool of 97 applicants
  • Designed for rapid, protocol-led incentive campaigns, with speed prioritized over long-term planning
• STIP Backfund
  • An extension to fund 26 additional protocols that missed out in the STIP vote
  • Allocated an additional $24.8M ARB, increasing total short-term spend and raising questions around voting consistency
• STIP Bridge
  • A six-week extension intended to bridge the gap between STIP and LTIPP for previously funded protocols
  • Total spend: $5.9M ARB across 23 grantees
  • Framed as a temporary program, it raised questions about ROI for the ecosystem and long-term focus
• LTIPP (Long-Term Incentives Pilot Program)
  • Introduced a new grant framework with operational team and allowing more flexibility to projects to distribute incentives.
  • Emphasized diverse objectives (education, growth, analytics) with a 3- to 6-month operational runway
  • Represented an attempted first step toward a structured DAO incentive program

1.3 Key Themes Across Research

A. Operational Complexity and DAO Coordination

Across all incentive programs, there is consensus that DAO-wide operational complexity has hampered both effectiveness and efficiency. STIP alone required months of back-and-forth proposals, amendment waves, and rushed voting sessions, stressing contributor bandwidth and reducing signal quality.

DAO members have noted that the lack of standardized frameworks, contributor tooling, or centralized reporting systems meant grantees received funds rapidly but without a clear path to delivering measurable outcomes.

B. Ecosystem Readiness

The open-access design of programs attracted a wide spectrum of applicants, most of whom were not yet ready to absorb and deploy capital effectively. Many smaller or newer protocols, even well-established ones, struggled with basic budgeting, analytics, or DAO engagement, leading to misaligned expectations. The short length of the programs also contributed to more inefficient capital allocation and incentive distribution design.

This mismatch created inefficiencies both in outcome (e.g., poorly tracked incentives) and in process (e.g., delegate fatigue and fragmented debates). One takeaway from this should be that putting too much pressure on protocols receiving incentives is not advisable.

C. Metrics, Measurement, and Impact

Chaos Labs’ reporting on STIP outcomes found high variability in how protocols defined and tracked success. While some protocols deployed sophisticated incentive dashboards and wallet tracking tools, others delivered minimal post-grant reporting.

The decentralized nature of reporting on incentive programs and the wide range of KPIs selected led to fragmented evaluation. As a result, while some protocols demonstrated real user growth and TVL spikes, the broader picture remains murky. Delegates lacked clear, consistent data to understand whether Arbitrum’s incentives achieved sustainable growth.

Tracking absolute metrics was sometimes a distraction, as across programs, the market was experiencing its own highs and lows in price, volatility, and activity. Programs should also compare Arbitrum and external ecosystems side-by-side, tracking how Arbitrum’s market share of specific verticals or products develops through a program.

D. Grant Design and Accountability

One of the clearest learnings from the STIP era is that protocol-led design, while empowering, often led to shallow accountability. There was no standardized framework to evaluate:

• Whether incentives were over- or under-budgeted
• How many wallets were sybil
• Whether growth persisted post-incentive

Protocols had full discretion on emission strategies, but little follow-up unless third-party observers like Chaos or Blockworks intervened.

The LTIPP sought to account for this by embedding milestones and more operational oversight. In its early days, this design represented a shift toward more robust transparency; however, accountability wasn’t well enforced.

E. Return Convergence Within Vertical Markets

Across all programs, high-level metrics per incentive dollar spent tended to converge within the same vertical. Most DEXs, for instance, showed similar outcomes in terms of cost per user, retained TVL, and incentive efficiency. This convergence reflects the cost of capital within mature verticals—protocols in well-established markets face similar user expectations, liquidity conditions, and performance ceilings. The ecosystem already has a shared understanding of what success looks like, which sets a baseline for how far incentives can stretch outcomes.

In these environments, protocols with the largest allocations often win short-term market share during the incentive period. However, usage tends to retrace once incentives end, as the same mercenary capital migrates elsewhere. This dynamic limits the long-term effectiveness of programs that deploy capital into stable markets without structural shifts.

That said, incentives can still be effective within mature verticals when used to target new users or unlock new markets. For example, protocols that onboard CEX users into on-chain “Earn” products or expand into new geographies can still deliver defensible growth even within saturated categories. Outperformance was typically seen in cases where incentives supported market expansion or product differentiation, not simply greater emissions into an already competitive field.

F. Strategic Alignment and Ecosystem-Level Thinking

Research highlights a tension between protocol-first design and ecosystem-coordinated goals. Many projects funded through STIP competed in overlapping verticals, such as multiple DEXs incentivizing the same pools, which led to internal competition rather than ecosystem-wide growth. At the same time, emerging sectors like AI, DePIN, and DeSci were often underrepresented.

This imbalance reflects a lack of strategic mapping in early incentive waves. Funding was largely generalized and reactive, which meant the most established verticals within Arbitrum captured the majority of incentives. Protocols in these categories were often the most prepared to apply and deploy quickly, regardless of whether their outcomes aligned with broader ecosystem needs.

Because protocols within well-established verticals tend to face similar user expectations and capital dynamics, they also share similar cost of capital. Whoever receives the largest allocation is typically able to attract the most users during the program, but this rarely translates to sustainable growth. Without a clear vertical-level strategy or targeted differentiation, these programs risk reinforcing short-term competition instead of long-term expansion.

1.4 Cross-Program Learnings

When taken as a whole, these programs reflect a real-time learning process for the DAO. Some system-wide insights include:

• Speed vs. Structure: Incentive Programs are characterized by the need to balance efficiency and organizational structure. For instance, STIP was a short-term program with a fast-paced structure. It was able to catalyze short-term growth but left gaps in impact evaluation and reporting. Additional structure, however, allows the program to take the burden off of protocols, allowing them to focus on building and winning. Although LTIPP introduced additional structure during the application and review phase, it was lacking during the incentive period.
• Data Fragmentation: A lack of unified dashboards, evaluation templates, KPIs, and budget tools across programs led to duplication of effort and reduced transparency.
• Lack of Retention: Whether due to a lack of well-designed and targeted program goals from the outset or the short-term nature of the programs, lack of retention is a common trend among all of Arbitrum’s incentive programs to date. During most programs, metrics do observe a short-term boost (although with different degrees across categories); however, whenever the program gets closer to the end, these metrics tend to revert to pre-incentive levels. Lack of retention often stems from incentivising well-established markets. These programs attract mercenary capital by offering better yields than the cost of capital in other ecosystems or by reducing the cost to act. Once that edge disappears, the same capital migrates, leaving little behind. One point to note, however, is that incentives can be used on well-established verticals when targeting new users from new markets (e.g., CEX users in “Earn” products using DeFi).
• Delegate Load: The open design created overwhelming complexity for delegates, who often had to choose between diligence and speed. The introduction of Advisors and the Council during LTIPP partially solved this.
• Reputational Risk: Weak reporting created concerns in terms of transparency and accountability—are incentives being wasted or strategically deployed? Should projects that fail to provide reports on their incentives fail to receive them or see their stream halted? Even in cases where this might have been warranted, the DAO has avoided recurring clawbacks. The only case where a project was banned is Furucombo. This extends to the DAO and ecosystem more broadly. From an industry perspective, rushing program designs without meaningful changes or a clear understanding of previous results signals waste and a lack of treasury spending strategy.

1.5 Comparative Ecosystem Reflections

Looking across the crypto ecosystem, several comparative findings emerge from programs in Optimism, Avalanche, Polygon, and others:

• Optimism’s mission-driven approach aligns incentives with long-term ecosystem goals, with the Grants Council acting as a strategic filter.
• Avalanche’s use of milestone-based incentives has created clearer accountability frameworks for developers and DeFi protocols.
• Polygon’s top-down BD-led strategy has prioritized onboarding key verticals (e.g., gaming), reducing grant fragmentation in favour of a more centralized approach.

Relative to these ecosystems, Arbitrum’s approach has leaned toward decentralization and protocol autonomy, at the expense of coordination, strategic alignment with specific ecosystem goals, and the understanding of key results from past programs.

1.6 Implications for Future Incentive Design

While specific recommendations are encompassed in the broader report, the research synthesized above highlights several structural tensions that future incentive designs must address. These tensions are not binary choices, but trade-offs that will shape how effective, scalable, and sustainable Arbitrum’s incentive architecture can become.

Tension	Trade-Off
Speed vs. Accountability	How do we ensure an efficient program without sacrificing transparency or measurable outcomes?
Protocol Autonomy vs. Ecosystem Strategy	Should incentives follow project needs or DAO (and broader Arbitrum-specific) priorities, such as the SOS or MVP?
Open Access vs. Quality Control	How do we ensure an accessible program without leading to inefficient capital distribution?
Delegate-Driven vs. Specialist Review	Should grants be led and managed by a specialized committee, building on LTIPP’s operational implementations? How involved should delegates be?
DAO Sustainability vs. Protocol Short-Term Benefits	How can incentives support protocol growth without compromising long-term treasury health or the DAO’s ability to incentivize growth in verticals where Arbitrum lacks market share?

Resolving these tensions will be key to evolving Arbitrum’s incentive infrastructure beyond one-off grants into a coherent, adaptive strategy that serves both protocol needs and ecosystem-wide objectives.

1.7 Conclusion: What the Research Tells Us So Far

To date, incentive programs have played a central role in Arbitrum’s ecosystem strategy, but their implementation has revealed significant structural, operational, and evaluative shortcomings.

The research suggests that while a handful of programs demonstrated signs of success, particularly in cases involving new deployments or well-aligned incentive design, the broader results were mixed. There is limited evidence of sustainable growth across most grantees. Issues such as short-term mercenary behavior, internal KPI gaming, and uneven capital distribution have undermined the long-term impact. Smaller or non-native protocols often struggled to compete. In some cases, larger recipients engaged in behaviors that reduced the effectiveness of their own incentives.

As a result, Arbitrum risks developing a reputation for capital inefficiency and reactive spending. Rather than confirming that incentives are a reliable growth lever, the research indicates that incentives are only effective when paired with the right program structure, accountability mechanisms, and strategic focus.

Future program design must internalize these lessons and move beyond funding activity for its own sake. This will require better filters and processes, a clearer definition of success at the ecosystem level, and a more intentional approach to capital deployment.

---

Incentive Program Evaluation Framework

Incentive Program Evaluation Framework

The previous themes point to a core issue: while Arbitrum’s incentive programs have varied in structure, they have not been assessed using consistent criteria. Without a shared evaluation standard, it becomes difficult to compare outcomes, identify performance patterns across verticals, or extract clear lessons from what worked and what did not.

To address this, we introduce a unified framework for evaluating incentive programs. This structure breaks each program into four components: Goals and Expected Output, Program Design, Actual Outputs, and Changes and Learnings. Each section includes focused criteria to support structured analysis across design intent, governance setup, incentive mechanics, and observed results.

This framework is applied in the following sections to evaluate both internal and external incentive programs. For Arbitrum-native initiatives, it allows us to assess how effectively each program performed against its original goals. For external case studies, it provides a structured lens to identify comparable strengths, shortcomings, and design strategies. By applying the same approach across ecosystems, we aim to surface practical insights that can inform Arbitrum’s future incentive design.

How we use this Framework

1. We divide the framework into four main sections:

2. Goals & Expected Output

3. Program Design

4. Actual Outputs

5. Changes & Learnings

6. Within each section, you’ll see clear subheadings and “Criteria” bullet points describing the key questions and considerations for evaluating an incentive program.

7. This structure allows for direct comparison of incentive programs, ensuring consistency in how we assess design, governance, operations, and outcomes.

1. Goals & Expected Output

1.1 Evaluating the Goals of the Incentive Program (From Source Proposal)

Criteria

• What is the core strategy of the program (e.g. project- bootstrap X product, objective- target specific assets or users, or metric-centric- overall TVL or sequencer revenue)?
• What were the primary objectives of the incentive program?
• How were these goals broken down for different ecosystem participants (e.g., DAO, protocols, users)?
• How did these objectives align with long-term ecosystem sustainability and short-term growth?

1.2 Translating Goals into Protocol and User Actions

Criteria

• Which protocols participated in the program, and how were they selected?
• What specific actions were incentivized (e.g., liquidity provision, trading volume, governance participation)?
• What type of users were targeted (retail traders, institutional participants, developers)?
• How were incentive structures designed to guide user behavior toward desired ecosystem goals?

1.3 Evaluating Success with Metrics

Criteria

• What key performance indicators (KPIs) were used to measure success? (e.g., TVL, number of active users, transaction volume, retention rates)
• Were these metrics effective in assessing the success of the program?
• Did the chosen metrics capture long-term impact, or were they more short-term focused?

1.4 Assessing Program Effectiveness

Criteria

• How effective was the incentive program in reaching its goals based on the given metrics?
• Were there any discrepancies between expected and actual results?
• What unintended consequences arose (e.g., wash trading/race to zero on fees, negative impact on token price)?

2. Program Design

2.1 Governance & Operations

Criteria

• Who governs and operates the incentive program? (DAO, foundation, working group, committees)
• What role did governance play in program oversight and modifications?
• Were there feedback loops or iterative mechanisms in place for ongoing program improvements?

2.2 Distribution Methods & Sybil Resistance

Criteria

• How were incentives distributed (contract-based rewards, milestone-based funding, direct token emissions)?
• Were there mechanisms to prevent Sybil attacks and gaming (e.g., proof-of-engagement models, reputation scores)?

2.3 Guidelines for Participating Protocols

Criteria

• What expectations were set for protocols participating in the program?
• Were guidelines provided to ensure alignment with ecosystem goals?
• How were best practices communicated to protocols and participants?

2.4 Budget & Allocation Rationale

Criteria

• What was the total budget allocated for the incentive program?
• How was this budget justified based on expected outcomes?
• How was the budget divided (e.g., by sector, by protocol, by user action)?
• Were there adjustments to funding levels based on observed program performance?
  • There are generally two levels to incentive programs: 1) the design of incentivized actions, and 2) the optimization of the original design.

3. Actual Outputs

3.1 Comparing Expected vs. Real-World Outcomes

Criteria

• How did actual program participation compare to initial expectations?
• How did participation vary across different user segments (traders, liquidity providers, new vs. existing users)?
• How did actual user behaviors compare to initial program expectations?
• Were incentives effective in driving sustained engagement, or did activity decline post-incentive? (What was the overall retention rate after incentives expired?)

3.2 External Market Conditions & Their Impact

Criteria

• Were there macroeconomic trends that affected program outcomes (e.g., market downturns, bull cycles)?
• Did competing incentives from other ecosystems influence participation?
• How quickly was governance able to respond to external conditions?

3.3 Governance Agility & Response

Criteria

• Was there the flexibility to adjust incentives based on real-time data?
• Were modifications made quickly, or were there delays due to governance bottlenecks?
• Did the program structure allow for mid-course corrections, or were incentives locked in?

4. Changes & Learnings

4.1 Adjustments Made Post-Program

Criteria

• What changes were made to the program after evaluating outcomes?
• Were incentives restructured in response to participation data?
• Were timelines or funding levels modified in subsequent incentive rounds?

4.2 Identifying Best Practices & New Models

Criteria

• What best practices emerged from the incentive program?
• Were there new incentive models or mechanisms that proved particularly effective?
• What could be applied to future ArbitrumDAO incentive initiatives and what should not be done again?

4.3 Broader Learnings from Other Ecosystems

Criteria

• Were there valuable insights from other ecosystems that could inform Arbitrum’s next steps?

4.4 Governance Adaptability & Future Applications

Criteria

• Did governance processes allow for timely and effective modifications?
• What governance improvements would make future incentive programs more responsive?
• What are the next steps for refining Arbitrum’s incentive approach?

Internal Analysis (Mini)

Mini-Report: Internal Analysis of Arbitrum Incentive Programs

This report offers a comprehensive evaluation of the Arbitrum DAO’s four major incentive programs: STIP, STIP Backfund, STIP Bridge, and the Long-Term Incentives Pilot Program (LTIPP). Each is analyzed, assessing:

1. Goals & Expected Output
2. Program Design
3. Actual Outputs
4. Changes & Learnings

Our goal is to understand what happened in each program and translate those insights into actionable recommendations for delegates and DAO contributors. By surfacing key patterns, inefficiencies, and successes, we aim to strengthen the DAO’s capacity to design and govern future incentive efforts more effectively.

1. Incentive Programs Analyzed

1.1 STIP (Short-Term Incentive Program)

• Budget: 50M ARB
• Timeline: October 2023 – January 2024
• Core Strategy: Project-Centric Experimentation
• Structure: Direct grants to 29 protocols, approved via DAO-wide vote following informal screening by the Incentive Working Group (IWG)

Objectives

• Enable protocol-level experimentation with incentives
• Collect ecosystem-wide learnings on liquidity, user behavior, and token design
• Stimulate short-term activity across key verticals
• Inform future DAO-level incentive architecture

1.2 STIP Backfund

• Budget: 21.4M ARB
• Timeline: November 2023 – January 2024
• Core Strategy: Project-Centric Extension
• Structure: 26 additional protocols approved by the DAO, with the same design and goals as STIP

Objectives

• Expand the STIP experiment set
• Maintain fairness between similar protocols
• Continue driving experimentation at minimal governance cost

1.3 LTIPP (Long-Term Incentives Pilot Program)

• Budget: 45M ARB
• Timeline: June 2024 – September 2024
• Core Strategy: Project-Centric with Structured Governance
• Structure: Application-driven funding evaluated by an elected LTIPP Council, supported by reviewers and advisors

Objectives

• Support more strategic, longer-term growth opportunities
• Introduce higher standards for application quality and alignment
• Pilot a repeatable operational model for a long-term incentive framework for the DAO

1.4 STIP Bridge

• Budget: 37.5M ARB
• Timeline: June 2024 – September 2024
• Core Strategy: Reactive Continuity Program
• Structure: Funding extended to select STIP recipients, based on IWG recommendations and DAO snapshot vote

Objectives

• Sustain momentum for selected STIP protocols
• Extend usage and liquidity gains from STIP
• Allow equal footing in competition with protocols receiving LTIPP

2. Summary of Incentive Program Evaluation

2.1 Goals & Expected Output

Key Takeaways

• STIP (and Backfund) Framed Incentives as Experimentation, Not Growth: STIP’s most valuable contribution was its embrace of experimentation. It did not claim to know what would work — instead, it created a data-rich environment to observe what might. However, similar insights could likely have been gathered with a much smaller budget.

• “Experimentation” Wasn’t Matched with Consistent KPIs: Most protocols pursued individual success metrics (TVL, fees, retention), but without unified reporting, results were fragmented. The absence of a shared dashboard or success definition made ecosystem-level learnings hard to capture. This was driven by protocols being responsible for the entire process, including reporting, while incentives were distributed across many verticals. As a result, there was no practical way to align around a unified set of KPIs. Furthermore, the metrics used to evaluate incentive program performance lacked rigor. Framing KPI improvements through the lens of market share and incentives per action, such as deposit volume or TVL, provides a more meaningful basis for analysis. This approach helps reduce the impact of macroeconomic noise. For example, gaining market share at the chain, vertical, or subvertical level is a clear win, even if the broader market is contracting.

• LTIPP Introduced Better Operational Oversight but Lacked Active Adaptation: While the LTIPP emphasized alignment with DAO-wide goals (although undefined) and partially addressed the operational challenges of the STIP by introducing a Council and Advisors, it nonetheless suffered from limited flexibility. This application-driven process, evaluated by the elected Council and supported by Advisors, aimed to foster higher-quality applications and stronger alignment. However, the absence of mechanisms for dynamic adjustment mid-program rendered the LTIPP rigid once it was launched.

• STIP Bridge was Reactive and Poorly Scoped: While meant to extend STIP’s best performers, the Bridge proposal appears rushed and not linked to expected ecosystem outputs. It lacked clear goals, evaluative rigor, or a robust monitoring plan, diluting LTIPP’s effectiveness.

• DAO Still Struggles to Resolve Tension Between “Experimentation” and “Adoption”, Especially through a Sustainable Lens: Incentive programs have often oscillated between learning-focused experimentation and growth-focused adoption. For example, STIP was framed as an experimental initiative, but much of the post-program evaluation centered on growth outcomes. Without a shared understanding of program purpose from the outset, intent tends to shift after launch, which complicates goal-setting and introduces inconsistency. The absence of clearly defined objectives makes it difficult to assess whether programs were successful or simply reactive. As a result, sustainability is often treated as an afterthought rather than a design priority. Programs are rarely evaluated based on their ability to deliver lasting impact or retained value beyond the incentive window.

2.2 Program Design

Key Takeaways

• STIP (and Backfund) Were Built for Participation, Not Success: These programs were framed as experimentation, as evident in their structure. The open application format brought in many protocols (55+ funded) but offered little design structure, which put a vast amount of pressure on protocols. There were no thematic clusters, shared KPIs, or program-wide targets, making assessments of the program’s success complex. Due to this large, open nature of the application system, unsuccessful protocols, often smaller and non-native ones, likely felt alienated in Arbitrum.

• No Core Operational Team Across the DAO to Manage Learnings: Extensive research into Arbitrum’s incentive programs reveals a significant lack of structured implementation for these data-driven approaches. While the incentive program benefited from support from various entities, the DAO lacked a persistent operational body at its core to manage the overall incentive strategy, consistently track program interactions, and ensure continuity across initiatives. This fragmentation meant that crucial learnings and insights gained from various incentive programs were not effectively institutionalized and carried forward into subsequent programs, which, despite introducing Councils, Advisors, and a PM, still lacked a permanent DAO-level unit responsible for all incentive strategy.

• LTIPP Introduced Structured Governance but Was Disrupted by STIP Bridge: LTIPP marked a shift toward more structured incentive governance, introducing formal review processes such as scoring rubrics, Council grading, and defined roles for Advisors. Operationally, it improved the application and evaluation pipeline and moved the DAO closer to a repeatable funding process. However, alignment with broader DAO goals remained unclear. Without clear ecosystem-level priorities, many programs within LTIPP defaulted to absolute topline metrics and lacked tailored KPIs for specific verticals or product types. At the same time, STIP Bridge was launched in parallel, targeting previously funded protocols with a short-term extension. This move undercut LTIPP’s attempt to reset expectations and design standards. The reintroduction of reactive, short-cycle funding through STIP.b risked re-fragmenting the incentive landscape and drawing attention and credibility away from the more structured LTIPP process.

• DAO Governance Process Still Limits Agility: The DAO governance process, which required full on-chain votes for program approvals, lacked the components necessary for dynamic execution. There was no infrastructure or mandate to track performance, collect feedback, or adjust parameters in real-time. This absence of live management undermined the programs’ ability to adapt to shifting market conditions or respond to underperformance while incentives were active.

• Even if such infrastructure were in place, most delegates do not have the time or resources to engage meaningfully in ongoing program oversight. As a result, the benefits of involving full DAO governance in live program management are minimal at best and potentially harmful when they delay necessary course corrections. As highlighted in our research, adaptability and operational flexibility are essential for program success.

• Program Rigidity and Short Timelines Distort Protocol Behavior: Compressed program windows (typically 3 months) forced protocols to rapidly deploy incentives without pacing, experimentation, or long-term planning. The KPI tracking design prioritized volume spikes over retention or insight. This resulted in opportunity costs as protocols focused heavily on initial distribution mechanics, leaving insufficient time to craft diverse experimental designs, explore strategic partnerships, or implement features ensuring sustainable incentive impact. This behavior will likely continue if protocols are left to design the incentive structures and lead reporting efforts.

2.3 Actual Outputs

Key Takeaways

• Return Convergence Across Vertical Markets, with Few Outperformers: Over 55 protocols were funded in a short time span across STIP and the Backfund. This generated broad activity but led to duplicated efforts, internal competition, and fragmented outcomes. On average, TVL and user activity per incentive dollar spent converged across protocols within the same vertical, regardless of design. The clearest exceptions came from protocols that launched new products or entered new markets, particularly those bootstrapping deployment or gaining early market share. Distributions targeting verticals where Arbitrum already held a stable share mostly did not lead to lasting retention. These campaigns often created high short-term yields that attracted mercenary capital, but lacked the structural clarity or targeting required to onboard new user segments. Without that differentiation, usage and engagement tended to revert toward pre-incentive levels once incentives expired.

• Performance Ranged from Excellent to Indeterminate: A small number of protocols, such as Compound in LTIPP, demonstrated clear product-led success. Compound used ARB incentives to launch its USDT pool on Compound V3, achieving a deposit volume share of roughly 30 percent, far outperforming competing assets like USDC and WETH. This showed that deploying incentives to support new product adoption or protocol expansion can generate strong returns. However, many protocols showed minimal or unclear results. Due to the lack of standardized reporting and ownership, success stories and failures alike were difficult to verify. Programs often defaulted to topline metrics without insight into user behavior, retention, or marginal impact, making ecosystem-level evaluation unreliable.

• STIP Bridge Incentives Lacked Strategic Value: STIP.b funding largely replicated static STIP structures, such as prevalent fee rebates and point programs, without incorporating key learnings on retention or data requirements. Lacking clear program-level objectives and robust oversight, significant capital was spent without data-driven insight into ROI or specific second-order benefits for the Arbitrum ecosystem. This led some delegates to perceive the program more as a handout for loyalty than a strategic growth investment. Whilst LTIPP made some changes to operations, similar strategic planning was also lacking.

• LTIPP Introduced Structural Improvements but Struggled to Demonstrate Clear Impact: LTIPP brought a more structured governance approach to incentive programs, introducing Council-led reviews, standardized rubrics, and dedicated Advisor roles. These changes were intended to improve program quality and reduce delegate load. While the process appeared to attract more complete and focused applications, it also placed a significant operational burden on participating protocols and failed to clearly demonstrate better outcomes compared to earlier programs. Its potential as a pilot for long-term incentive governance was further undermined by the launch of STIP Bridge, which ran concurrently and drew attention back to short-term, protocol-led emissions. As a result, the experimental integrity of LTIPP as a standalone design was never fully tested.

2.4 Changes & Learnings

Key Takeaways

• Programs Had No Built-In Adjustment Mechanisms: Once DAO-approved, incentive distributions were generally fixed (static), regardless of real-time performance or market shifts. There was no formal process for reallocating funds from underperforming projects, pruning ineffective campaigns, or amplifying high-ROI ones based on mid-program data (aside from the Council halting funds for extreme cases). This static distribution of incentives was also ineffective in taking into account the different needs or performance nuances of participating projects.

• DAO Lacked Infrastructure for Strategic Continuity: There was no core operational team or central entity responsible for overseeing strategic incentive continuity, managing program transitions, or systematically institutionalizing learnings across cycles. This fragmentation means key institutional knowledge is lost, leading to repeated design issues – evident in the persistent struggle with post-incentive user retention seen across programs – and missed opportunities, such as the lack of dedicated analysis after STIP/LTIPP ended. Without this, future efforts fail to build effectively on past successes.

• LTIPP Did Not Make a Fundamental Shift in Program Strategy: While LTIPP introduced a more formal process and clearer structure than STIP, it did not go far enough in addressing the core issues facing Arbitrum’s incentive strategy. The program continued to rely on open applications without prioritizing ecosystem-wide goals, such as increasing market share in underserved verticals or accelerating the adoption of strategically important products. Rather than moving away from protocol-first design, LTIPP replicated many of the same incentive dynamics seen in earlier programs. It lacked a clear mandate to guide incentives toward vertical-level outcomes and did not establish the strategic filters needed to focus capital where it could drive the greatest marginal impact. As a result, the program raised the administrative bar but fell short of delivering meaningful directional change.

• Repeated Bottlenecks & Inflexible Structures Continue to Limit Impact: Governance processes remain rigid, shaped by requirements such as full DAO votes for approvals, limited authority to reallocate funds based on live performance, and narrow program windows typically lasting only three months. These structural constraints force protocols to operate within fixed timelines and frameworks, limiting their ability to iterate or adjust incentives midstream. As a result, programs struggle to optimize for sustainable outcomes like long-term user retention and instead default to short-term activity spikes. More critically, the generalist nature of program design places an outsized burden on protocols themselves. With limited strategic guidance from the DAO, protocols are expected to define success, build emission models, and justify performance metrics independently. This has led to an overemphasis on short-term KPIs at the expense of sustainable growth. Rather than incentivizing lasting ecosystem development, programs often reward activity that dissipates once the funding cycle ends.

3. High-Level Observations & Potential Best Practices

3.1 Ten Best Practices for Strategic and Adaptive Incentive Design

1. Define Program Purpose Upfront and Stick to It

One of the core issues across prior incentive programs was the lack of a clearly defined purpose at the outset. Without this, it becomes difficult to assess success, measure ROI, or compare performance across protocols. Programs should state upfront whether they aim to drive ecosystem growth, test incentive mechanisms, retain liquidity, or expand into strategic verticals. These purposes should remain consistent throughout the program’s life.

This does not mean programs need to be rigid. Protocols and mechanisms should retain flexibility in how they reach the stated goals. However, without a shared understanding of what a program is trying to achieve, incentives drift toward short-term activity and diffuse reporting, making it impossible to evaluate impact. A clearly defined and consistently upheld purpose is a prerequisite for meaningful analysis and informed governance.

2. Build a Dedicated Incentives Operating Unit

The Arbitrum DAO currently lacks a persistent, full-cycle operating unit responsible for designing, executing, and iterating incentive programs across multiple seasons. As a result, each new initiative begins from scratch, and critical knowledge, particularly around mid-program adaptation, is often lost. STIP is a clear example: once deployed, it lacked supervision, adjustment mechanisms, or clear accountability for improvement.

LTIPP introduced some operational upgrades through the addition of Advisors, a Council, and a Program Manager (PM). These roles helped streamline key functions: the Council evaluated proposals, issued funding decisions, and handled stream halts; Advisors offered structured feedback and technical support through office hours; and the PM ensured operational continuity. However, while LTIPP built on the governance and operational processes introduced in STIP, these improvements were not enough to overcome deeper design shortcomings. The program lacked a unified strategy, clear goals, and the ability to respond dynamically to new information.

To move forward, the DAO should establish or fund a dedicated Incentives Operating Unit, either as an in-house team or external service provider, tasked with managing program execution, surfacing learnings, and ensuring continuity between cycles.

3. Encourage Modularity and Program-Level Cohesion

Past programs, primarily project-centric in design (such as STIP and LTIPP), funded strong individual protocols but failed to coordinate efforts or thematic priorities across verticals, even though DeFi received the majority of allocated funds. This fragmented approach limited the DAO’s ability to drive coordinated outcomes or assess impact beyond the protocol level.

LTIPP analysis revealed meaningful differences in efficiency across sectors. Lending and DEX protocols contributed most to TVL, fees, and daily active users, while Perpetuals, CDPs, and RWAs showed limited user engagement. In contrast, Gaming and Infrastructure projects excelled in driving transaction volume and user activity but generated relatively low fee throughput.

These outcomes point to the value of a modular approach. Future programs should group incentives by vertical, define sector-specific goals, and track performance across clusters rather than individual protocols. For example, targeting TVL and fee growth in Lending and DEX verticals could build on previous success, while DAU strategies for Perpetuals or CDPs may require a fundamental redesign. Gaming and Infrastructure may benefit from incentives focused on onboarding and retention rather than capital formation.

Importantly, vertical-specific programs should aim not only to improve internal metrics but to grow Arbitrum’s relative market share within each sector. To achieve this, incentives should be directed toward verticals or products already demonstrating organic growth or positioned to benefit from a structural shift. Capital is more effective when it amplifies pre-existing momentum rather than attempting to manufacture demand in saturated or stagnant markets.

4. Limit Program Fragmentation and Sharpen Objective Focus

The primary challenge lies not necessarily in funding similar protocols, but rather in ensuring all funded programs contribute to clear, unified objectives. Funding efforts with fragmented or unclear purposes can dilute overall impact, scatter attention, and make it difficult to measure collective success. Aim to prioritize incentives for protocols (even similar ones) that demonstrably contribute to specific, measurable goals, such as growing participation in key asset classes, increasing liquidity in strategic pools, or generating defined revenue streams. This approach moves away from focusing purely on the distinctiveness of protocol design scopes and towards aligning diverse efforts towards shared strategic outcomes.

5. Calibrate Program Timelines to Capital Deployment and Vertical Conditions

Compressed timeframes created artificial pressure for protocols to spend quickly, often at the expense of thoughtful incentive design and long-term user retention. The DAO has consistently allocated more capital than can realistically be deployed or absorbed within three-month windows. This mismatch reduces capital efficiency and encourages shallow incentive execution.

Rather than defaulting to fixed durations, future programs should calibrate timeline and budget to the specific conditions of the vertical being incentivized. Where there is clear product-market fit or an emerging opportunity, programs could run for six to nine months with room for extensions based on performance. In cases where no structural shift or novel deployment exists, timelines should be shorter and capital commitments smaller.

Extending duration is not a standalone solution. Longer timelines only yield better outcomes when paired with differentiated program design and vertical-level strategy. Without a compelling reason for users to stay, even multi-month incentives will fail to deliver retention. Programs should focus on pacing spend appropriately and aligning timeframes with the market dynamics they intend to influence.

6. Replace “One-Shot” Incentive Packages with Progressive Allocations

Past programs like STIP, LTIPP, and STIP.b used static, one-time funding models with limited flexibility to adjust based on real-world performance. These designs required protocols to build their incentive strategies up front, often without sufficient time, data, or structure to iterate once execution began.

A more effective approach is to move toward DAO-led incentive structures that use milestone-based, performance-gated, or seasonal allocations. This allows the DAO to concentrate capital where it is producing results, while scaling back or halting underperforming segments without relying on retroactive reviews. It also reduces the risk of upfront misallocation and encourages dynamic execution, especially in longer-running programs.

To be effective, this model must be paired with strong program-level ownership and vertical-level strategy. Programs should be structured to respond to real-time performance, concentrating incentives in high-usage pools, avoiding over-rewarding saturated markets, and deliberately tapering rewards to test whether usage holds steady in the absence of subsidies. The goal is not to guide protocol behavior directly but to create ecosystem-wide programs that adapt to results and deploy capital with greater precision.

7. Formalize Reporting Standards Through Centralized Data Infrastructure

One of the most critical issues across past incentive programs was the lack of standardized, aggregated data for post-program evaluation. Without consistent metrics or reporting formats, each protocol defined success independently, making it difficult to compare performance or extract reliable insights at scale. Manual data collection became the default, limiting transparency and creating duplication of effort.

To address this, future programs should be supported by a dedicated, centralized entity responsible for reporting infrastructure. This includes the creation of standardized templates, the collection of raw program data, and public dashboards that track performance against shared KPIs. These KPIs should be defined at the program level, not by individual protocols, and could include metrics such as incentives per dollar of TVL, user retention, or share of on-chain activity within a vertical.

This model shifts the reporting burden away from individual recipients and ensures the DAO has access to consistent, real-time data. By making data publicly accessible and reproducible, it also reduces the risk of centralization bottlenecks, such as over-reliance on a single data provider. With better infrastructure, the DAO can evaluate trade-offs more clearly and make informed, data-driven decisions in future program cycles.

8. Design for Post-Program Retention

Incentive success must be measured not only by activity during the program but by stickiness after it ends. Analysis of STIP, LTIPP, and STIP.b consistently showed metrics such as activity, TVL, fees, and transactions frequently dropping back to pre-incentive levels post-program. This demonstrated a widespread lack of sustained user engagement, indicating incentives often fueled temporary spikes rather than lasting loyalty or reinvestment.

While some protocols experimented with retention mechanisms, such as requiring ARB locking (Vela, Savyy) or capping yields (Vertex), and specific projects like Tales of Elleria achieved good player retention, these approaches were not broadly adopted.

Instead, prevalent short-term strategies like fee rebates and point programs dominated allocations (over 37% of incentives in STIP.b).

Future program designs must proactively embed features that encourage stickiness and foster sustainable activity beyond the incentive period.

9. Make Governance More Agile by Delegating Operational Control

Relying on full DAO votes for all decisions creates latency and limits the DAO’s ability to manage programs dynamically. While governance has shown moments of responsiveness, such as approving the STIP Backfund or halting incentives in clear misuse cases like Furucombo, these were reactive decisions, not part of a proactive system. The programs themselves remained rigid, with little ability to adapt incentives based on live performance data, user behavior, or changing market conditions (or that protocols had no time or scope to do this themselves).

Future programs should not rely on protocols to navigate or respond to governance bottlenecks. Instead, the DAO should establish a dedicated, highly accountable incentives team with a clear operational mandate. This team should be responsible for day-to-day program execution and have the authority to rebalance, pause, or redirect incentives in response to performance data and evolving conditions.

Rather than dispersing responsibility across loosely coordinated groups, this structure ensures a single point of ownership and clear accountability for outcomes. By formalizing this role, the DAO can maintain strategic oversight while enabling faster, more effective execution at the program level.

10. Align Incentive Programs to DAO-Wide Goals

To improve capital efficiency and ecosystem impact, the DAO should define clear, measurable goals at the start of each incentive cycle. These goals should reflect strategic ecosystem priorities, such as increasing market share in specific verticals, onboarding new categories of applications, or supporting infrastructure that unlocks new functionality.

Incentive programs should be built to reflect these shared objectives. Previous programs often lacked this alignment. STIP, for example, was protocol-led and operated without vertical prioritization. LTIPP pushed responsibility for goal-setting onto applicants, resulting in various disconnected proposals. STIP Bridge continued funding past participants without updating the program’s strategic focus. In all cases, incentives were distributed without a clear thesis for how they advanced ecosystem-level growth.

Programs should target verticals already showing signs of organic momentum or those positioned to benefit from structural changes. Designing to amplify this kind of activity, rather than subsidizing stagnant markets, leads to more durable outcomes and stronger returns on capital.

To support this, programs must also be operationally flexible. Budget allocations and incentive pacing should adjust based on real-time performance. Capital should flow toward high-impact activity and be tapered where growth plateaus. This pairing of strategic clarity and executional adaptability is essential to avoid overfunding passive verticals or rewarding temporary usage spikes.

Without clear DAO-set goals and responsive mechanisms, incentive design becomes reactive. Capital flows toward the most organized applicants rather than the most strategic opportunities. Going forward, the DAO—or a delegated incentives team—should take a more active role in setting priorities, defining benchmarks, and ensuring that incentive programs are built to serve long-term ecosystem positioning.

3.2 Five Common Pitfalls to Avoid

1. Running Overlapping or Conflicting Programs

Running multiple incentive programs simultaneously, such as STIP Bridge alongside LTIPP, created confusion, diluted experimental value, and fractured DAO attention. Without clear differentiation or coordinated execution, programs ended up competing for the same users, applicants, and governance bandwidth. This reduced the ability to learn from outcomes and often led to duplicated spending. If concurrent programs are to be used, they must have distinct goals, vertical coverage, or timelines, and be coordinated operationally to avoid cannibalizing one another.

Do not run overlapping programs unless they are strategically distinct and intentionally sequenced or coordinated.

2. Failing to Standardize ROI Metrics and Data Reporting

In previous programs, protocols defined and reported KPIs independently, with no consistent formats, metrics, or data infrastructure. Even when guidance existed, standardization fell short. This fragmented landscape made it difficult to compare performance or calculate program-level ROI. Analysts frequently dealt with incomplete or inaccessible data and, in some cases, had to reconstruct results manually. This is a major blocker to iterative improvement. Future programs must allocate resources to shared infrastructure that collects, maintains, and publishes standardized raw datasets across protocols.

Do not offload data responsibility to grantees. Build shared infrastructure for consistent, comparable reporting.

3. Mistaking Short-Term Usage for Lasting Impact

Across STIP, STIP.b, and LTIPP, key metrics like TVL, users, and volume often spiked during the program and collapsed once incentives ended. In many cases, usage returned to pre-incentive levels within weeks. This highlights the danger of measuring success purely during the funded window. Short-term activity is not proof of retention or ecosystem growth. Without mechanisms to sustain engagement or test for post-program durability, capital subsidizes temporary participation with little long-term payoff.

Do not assume usage during incentives equals success. Design for what happens after incentives stop.

4. Designing Programs Without Flexibility to Adapt

Many past programs were built with rigid parameters: budgets, timelines, and distribution plans were fixed at launch, with little room to adjust to real-world conditions. While some protocols made ad hoc changes, there was no program-level mechanism to reallocate capital in response to performance, user behavior, or ecosystem shifts. This limited the DAO’s ability to optimize outcomes midstream.

Effective incentive programs have two phases: the initial design and the ability to adapt that design as data emerges. Future programs should include built-in levers for flexibility, ideally managed by a delegated operational team empowered to adjust based on predefined triggers or observed results.

Do not treat design as a one-time event. Structure adaptability into the program from the start.

5. Offloading Strategic Goal-Setting to Protocols

Programs like LTIPP delegated strategic goal-setting to applicants, resulting in a wide range of uncoordinated proposals. Without ecosystem-level direction, protocols pursued isolated objectives, and capital often flowed toward the best-prepared applicants rather than the highest-leverage opportunities. This made it difficult to assess whether funded activity meaningfully advanced Arbitrum’s broader positioning or ecosystem growth.

Future programs should be anchored in clear goals defined at the DAO level, whether directly or through a delegated operational team. Protocols can then apply within that framework, aligning execution with strategic intent.

Do not rely on protocols to define what matters. Strategic direction must come from the DAO or its designated operators.

External Analysis (Mini)

Mini-Report: External Analysis of Incentive Programs

This report evaluates multiple incentive programs using the Incentive Program Evaluation Framework, which is divided into four main sections:

1. Goals & Expected Output
2. Program Design
3. Actual Outputs
4. Changes & Learnings

By comparing the structure, governance, and performance of different incentive programs, we extract best practices, highlight inefficiencies, and propose recommendations for optimizing future incentive initiatives.

1. Incentive Programs Analyzed

1.1 zkSync Ignite

• Budget: 325,000,000 ZK tokens.
• Timeline: 9 months

Core Strategy: Metric-Driven & Objective-Based: Focused on liquidity depth, trading efficiency, and DeFi adoption.

• Bi-weekly adaptive allocation model optimizing capital efficiency.
• Structured incentives for liquidity growth and ecosystem expansion.

Objectives

• Expand onchain assets by increasing accessibility and diversity of digital assets on zkSync.
• Grow active builders by fostering an innovative developer ecosystem.
• Strengthen the zkSync community by engaging users, developers, and stakeholders.

KPIs Used:

• Liquidity Growth: $5-$10 liquidity increase per $1 of incentives, targeting $205M-$410M in 9 months.
• Trading Efficiency: Reduce slippage on key pairs for better price execution.
• Sustainable Fees: $3 in trading fees per $1 of incentives to strengthen LP returns.

1.2 Sonic Points Program

Budget: Airdrop: 190.5m $S

• Innovator Fund: 200m $S
• Ecosystem Vault: Dynamic fund allocation via governance (TBD)
• Opera Rewards Migration: 70m $S annually over four years

Timeline :270 days

Core Strategy: Ecosystem-Wide & Multi-Layered Incentives: Aiming for liquidity growth, developer participation, and long-term sustainability.

• Dual-Incentive Model:
  • User Rewards (Sonic Points for engagement).
  • Developer Incentives (Sonic Gems & Fee Monetization).
• Sustainable growth strategy with structured token emissions and revenue-sharing.

Objectives

• Increase liquidity through user rewards and engagement in DeFi applications.
• Onboard developers by offering revenue-sharing via FeeM (90% of gas fees go to developers).
• Ensure ecosystem sustainability through fee burning, controlled emissions, and structured airdrops.

KPIs Used:

• 10M daily transactions (chain wide)

1.3 Optimism Superfest

• Budget: 1.5M OP
• Timeline: 1 month (July 2024-August 2024)

Core Strategy: Objective-Based & Cross-Chain DeFi Bootstrapping: Targeting adoption across the Optimism Superchain (OP Mainnet, Base, Mode, Fraxtal).

• Mission-based user engagement rewarding liquidity provision, trading, and staking.
• Long-term retention mechanisms via NFT wristbands and XP-based incentives.

Objectives

• Drive DeFi adoption within the Optimism Superchain.
• Increase liquidity inflows and improve TVL growth per OP spent.
• Encourage cross-chain interactions between participating networks.
• Improve user retention through time-weighted rewards and onchain credentials.

KPIs Used:

• Increase in TVL per $1 OP spent
• unique wallets participating
• first-time users bridged

1.4 Optimism Phase 0 - Season 7

• Budget: 231,928,234 OP
• Timeline: 2022-07-13 - 2025-10-13

Core Strategy: Project-Centric Incentives: Projects applied for incentives, and the DAO determined approvals case-by-case.

• Broad scope covering liquidity provision, trading, governance, staking, and community engagement.
• Flexible execution where projects tailored incentives to attract users and drive adoption.

Objectives

• Support early-stage project growth and adoption within the Optimism ecosystem.
• Encourage diverse participation across traders, liquidity providers, developers, and governance participants.

KPIs used:

• There were no KPIs provided by the DAO

1.5 Avalanche Multiverse

Budget: $290M (4M AVAX)

Timeline: No specific timeline

Core Strategy: Objective-Based & Subnet Growth-Focused: Targeting Avalanche’s Subnet adoption with large-scale incentives.

• Up to $290M (4M AVAX) allocated to drive subnet adoption.
• Strategic partnerships with key projects (DeFi Kingdoms, Aave, Dexalot, Crabada).
• Sector-focused incentives (Gaming, DeFi, NFTs, Institutional Finance).

Objectives

• Bootstrap new subnets to position Avalanche as a leader in app-specific chains.
• Attract high-impact projects by offering strong financial support and infrastructure.
• Demonstrate the viability of subnets through real-world use cases.
• Enhance AVAX demand by requiring AVAX staking and gas fees in subnets.
• Improve scalability by offloading transactions from the Avalanche C-Chain.

KPis used:

• Subnet Expansion: Number of live subnets launched.
• Transaction Volume: Total transactions across subnets.
• Bridged TVL: TVL growth in subnet ecosystems (e.g., $470M for DFK Crystalvale).
• Validator Participation: AVAX staked & validator count in subnets.
• Partner Engagement: Institutional subnet progress (e.g., Aave’s “Spruce” testing).
• C-Chain Offloading: Reduction in C-Chain congestion due to subnet activity.

1.6 Avalanche Icebreaker

Budget: 500,000 AVAX (worth roughly $8–$10M at launch time.

Timeline: No specific timeline

Core Strategy: Project-Bootstrap & Market Stability: Addressing the “cold start problem” for early DeFi protocols.

• Targeted liquidity support rather than direct user incentives.
• Foundation-driven capital injection into key DeFi and staking protocols.

Objectives

• Improve LST Market Liquidity: Increase depth for sAVAX, ankrAVAX, making staking and trading more attractive.
• Encourage TVL Growth: Boost the DeFi lending and staking ecosystem on Avalanche.

KPis used:

• Deployment Efficiency: % of AVAX deployed vs. allocated budget (e.g., 80% of 500K AVAX).
• LST Market Depth: Liquidity on DEXs (e.g., slippage in sAVAX trades).
• LST Utilization: Lending market capacity with LST collateral.
• LST Adoption: % of AVAX staked via LSTs.
• Staking Growth: Increase in liquid-staked AVAX supply.

1.7 Avalanche Rush

• Budget: $180M
• Timeline: Announced August 2021, rolled out in multiple phases

Core Strategy: Project- Centric & Objective-Based: Focused on attracting “blue-chip” DeFi protocols and liquidity from Ethereum onto Avalanche​.

• Goal: Rapidly expand Avalanche’s DeFi ecosystem by onboarding major DeFi protocols and attracting liquidity from Ethereum.
• Method: Provided direct AVAX incentives to protocols (Aave, Curve, Sushi, etc.) and users (liquidity providers, traders).
• Focus Areas: Lending, trading, yield farming, cross-chain migration, and ecosystem development.

Objectives

• Boost Total Value Locked (TVL): Attract deep liquidity to Avalanche’s DeFi protocols.
• Increase Protocol Adoption: Incentivize blue-chip DeFi projects to launch on Avalanche.
• Grow Cross-Chain Liquidity: Encourage users to migrate liquidity from Ethereum using the Avalanche Bridge.
• Enhance Market Depth: Improve liquidity efficiency on AMMs, lending markets, and perpetual DEXs.
• Sustain Ecosystem Growth: Support native DeFi projects and ensure long-term retention.

KPIs Used:

• TVL Growth: % increase in Avalanche’s DeFi TVL post-launch.
• Protocol Participation: Number of blue-chip DeFi and Avalanche-native protocols onboarded.
• Cross-Chain Liquidity Migration: $ value of assets bridged from Ethereum.
• Trading Volume: Daily swap and perpetual trading volume across Trader Joe, SushiSwap, GMX, and Pangolin.
• Lending & Borrowing Growth: % increase in capital deployed in Aave, BENQI, and Alpha Homora V2.
• Retention Rate: % of liquidity that remained on Avalanche after incentives ended.

2. Summary of Incentive Program Evaluation

2.1 Goals & Expected Output

In this section, we examined the core goals and expected outcomes of each incentive program to understand the importance of setting clear objectives and expectations beforehand. This helps improve overall outcomes by addressing key questions such as:

• What is the core strategy of the program (e.g., project-boosting, ecosystem-wide, or metric-based)?
• What were the primary objectives?
• How did these goals align with short-term growth and long-term sustainability?

Key Takeaways:

• Define a Clear North Star and Align KPIs → zkSync Ignite structured its goals into measurable KPIs, while Optimism SuperFest and Avalanche Rush were too broad, making it hard to track impact.
• Use Specific & Granular Metrics → Instead of just tracking TVL growth, zkSync Ignite measured TVL increase per $1 spent, liquidity depth, and fees generated to ensure efficient capital allocation.
• Differentiate Incentives for Users & Developers → Sonic’s dual-incentive model (Sonic Points for users, Sonic Gems + FeeM for developers) was more effective than a one-size-fits-all approach.
• Ensure Capital Efficiency & ROI Tracking → Programs should continuously evaluate which protocols generate strong ROI and adjust incentives accordingly instead of allocating funds blindly.

2.2 Program Design

Here, we analyzed the governance structure, operational design, and incentive distribution mechanisms to understand how different design choices impact the success of an incentive program. Key considerations included:

• Who governs and operates the incentive program (e.g., DAO, foundation, committees)?
• How were incentives distributed (e.g., contract-based rewards, milestone-based funding, direct token emissions)?
• Were there mechanisms in place to prevent Sybil attacks and gaming?

Key Takeaways:

• Build-in Flexibility for Real-time Adjustments → zkSync Ignite used bi-weekly adaptive allocations, while Optimism’s incentives were fixed, leading to inefficiencies.
• Avoid Spreading Incentives Too Thin → Too many protocols dilute impact. A smaller, high-impact selection (e.g., 10-12 key projects) ensures better results.
• Make Governance Agile & Data-Driven → Programs need live dashboards and a working group to track results, market and reallocate funds dynamically rather than waiting until the end.
• Provide Transparency in Fund Distribution → Programs like Optimism SuperFest lacked transparency in how funds were allocated to protocols, making it harder to assess effectiveness.
• Improve User Experience & Accessibility → A dedicated website (like zkSync’s) and streamlined onboarding (Jumper Exchange for SuperFest) make participation easier and increase engagement.

2.3 Actual Outputs

This section assessed the real-world impact of each incentive program to gain an objective understanding of what works, what leads to successful outcomes, and which design choices result in unintended consequences. We explored:

• Did participation match initial expectations?
• Did user behavior align with the program’s incentive design?
• Were incentives effective in driving sustained engagement?

Key Takeaways:

• Mid-Course Adjustments Are Necessary But Not Sufficient zkSync Ignite introduced a bi-weekly reallocation mechanism that allowed funds to be redirected toward better-performing protocols. While this structure helped limit waste during the program, it ultimately could not prevent liquidity flight once incentives ended. Adjustments alone cannot solve for deeper structural issues, such as weak product-market fit.
• Operator Quality Matters More Than Flexibility Alone: Programs need to be actively managed by capable teams who can interpret data and make informed decisions. Although zkSync Ignite offered real-time tracking and bi-weekly rebalancing, some reallocations appeared to reinforce short-term behaviors rather than build durable usage. In contrast, SuperFest suffered from governance rigidity, highlighting that both speed and expertise are required for successful execution.
• Dynamic Allocation Must Be Tied to Real Demand zkSync Ignite demonstrated that even dynamic reallocation systems are ineffective if they funnel capital into protocols that users would not engage with organically. Many protocols that received substantial rewards failed to retain users, showing that without intrinsic demand, incentives only generate temporary traction. Dynamic funding must be paired with robust filters that screen for product readiness and user fit.
• Timing Incentives Around Market Cycles is Crucial → Increasing incentives during bull markets maximizes adoption (e.g., Avalanche Multiverse), while reducing them in bear markets prevents wasted spend
• Retention Depends on Product-Market Fit, Not Just Incentive Design
• zkSync Ignite failed to retain the majority of its TVL after the program concluded, with many protocols ending with negative TVL growth relative to incentives allocated. This was not simply a design flaw—it revealed that no incentive model can succeed without strong underlying products. Incentives can accelerate adoption, but they cannot manufacture it where there is no user demand or sustainable value.

2.4 Changes & Learnings

We examined how programs evolved over time to determine whether built-in flexibility is necessary and how responsive different incentive programs are to external market conditions or other factors. Key questions included:

• What changes were made based on program performance?
• Did governance structures adapt to improve efficiency?

Key Takeaways:

• Ineffective Incentives Were Quickly Removed in Adaptive Models → zkSync Ignite cut rewards for underperforming sectors (e.g., perpetuals) and increased high-yield incentives (e.g., lending). In contrast, Optimism’s SuperFest failed to reallocate incentives from low-engagement chains (Mode & Fraxtal) to high-engagement chains (Base & OP Mainnet).
• Governance Agility Matters → Programs that required full DAO votes for changes (e.g., Optimism’s Season 7) suffered from slow response times, limiting efficiency. In contrast, zkSync Ignite’s DeFi Steering Committee had autonomy to rebalance funds bi-weekly, allowing for quicker decision-making.
• More Selective Incentive Allocation Increases ROI → SuperFest’s broad distribution led to dilution across 30+ protocols, limiting impact. zkSync Ignite focused incentives on a smaller set of high-performing projects, improving efficiency.

3. High-Level Observations & Potential Best Practices

After analyzing multiple incentive programs, we have identified key lessons and best practices that can enhance incentive design, governance, and execution.

3.1 Best Practices for Effective Incentive Programs

1. Establish a Clear Timeline with Periodic Reviews

• Incentive programs should include structured timelines that outline key phases (e.g., launch, growth, evaluation, tapering).
• Implement scheduled reviews (e.g., bi-weekly or monthly) to assess progress against KPIs and make necessary adjustments.

2. Create a Clear North Star Vision

• Successful incentive programs start with a well-defined overarching goal that aligns with long-term ecosystem growth.
• This North Star should guide all decisions, ensuring that incentives are structured to achieve sustainable impact rather than short-term gains.

3. Set Measurable & Actionable Objectives

• Objectives should focus on key areas such as liquidity depth, user engagement, developer participation, and protocol adoption.
• Example: Instead of a broad goal like “increase DeFi adoption,” set specific targets such as “grow stablecoin TVL by 20% within six months while maintaining a $5:$1 incentive-to-liquidity ratio.”

4. Design Adaptive Incentive Structures

• Programs that adjust incentives dynamically based on performance data are more efficient. zkSync Ignite’s bi-weekly reallocation model ensured capital was deployed effectively, unlike fixed programs that struggled with inefficiencies.
• Future programs should integrate real-time analytics and allow for adjustments to maximize impact.

5. Use ROI-Driven Metrics for Allocation

• Instead of measuring success solely by TVL growth, incentive programs should evaluate impact per dollar spent, trading fees generated, and sustained liquidity depth. zkSync Ignite introduced a more sophisticated ROI tracking framework that attempted to quantify the efficiency of incentives using metrics like TVL per dollar, fee generation, and capital efficiency. This model represented a step forward in how programs are structured and monitored.
• However, while the measurement framework was strong, the actual outcomes were weak. Many protocols that initially appeared efficient ended with negative TVL growth once incentives ended. This highlights a critical point: robust ROI tracking cannot compensate for poor protocol performance or lack of product-market fit.

6. Keep Incentives Targeted and Selective

• Spreading incentives across too many protocols can dilute their influence. SuperFest’s broad allocation illustrates this effect, while zkSync Ignite concentrated on a smaller cohort of high-performing projects and appeared to generate stronger traction.
• Historical campaigns that showed durable activity like Rush, Optimism Round 1, and zkSync Ignite each funded roughly eight to fifteen projects. That range seems to balance depth of funding with manageable oversight; it is large enough to cover several verticals but small enough that program managers can track progress and adjust parameters when needed. Future initiatives could use a similar order of magnitude, selecting only as many protocols as resources and monitoring capacity allow, rather than setting a hard target.

7. Differentiate Incentives for Users & Developers

• A one-size-fits-all approach is ineffective. Sonic’s dual-incentive model (Sonic Points for users, Sonic Gems + FeeM for developers) proved more successful in ecosystem engagement.
• Incentives should be structured separately for users and developers to promote long-term ecosystem health & you could even create different programs for different objectives (One for the promotion of LSTs & one to bootstrap new projects)

8. Ensure Transparency in Fund Distribution

• Programs like SuperFest lacked clear reporting on how funds were allocated per protocol, making it difficult to assess impact.
• Future programs should include public dashboards and periodic reporting to ensure accountability and improve trust.

9. Adapt Incentives to Market Cycles

• Incentive effectiveness fluctuates with market conditions. Avalanche Rush saw better adoption by increasing rewards during bull markets, while other programs failed to adjust.
• Incentive programs should scale up in bullish conditions and scale down in bearish markets to optimize cost efficiency.

10. Improve Governance Flexibility

• Programs that required full DAO votes for adjustments (e.g., Optimism Season 7) suffered from slow response times, reducing efficiency.
• A governance structure with delegated decision-making (e.g., zkSync Ignite’s DeFi Steering Committee) allows for faster adjustments and better resource allocation.

11. Implement Strong Sybil Resistance Mechanisms

• OpenBlock’s fraud detection measures in Sonic and zkSync Ignite helped prevent wash trading and exploitative behaviors.
• Future programs should incorporate similar safeguards to ensure incentives are reaching genuine users.

12. Encourage Ecosystem Loyalty

• For a newly launched network such as Sonic, offering higher-weighted incentives to Sonic-native applications can help bootstrap a local developer base and limit early multi-chain leakage. In a more mature ecosystem like Arbitrum, growth often hinges on attracting protocols that already operate elsewhere.
• These teams are unlikely to abandon existing deployments solely for larger ARB rewards, so incentive design may need to balance benefits for both native builders and migrating projects, tying payouts to measurable onchain usage rather than exclusivity. Reward weightings should match the network’s stage: a stronger native bias when penetration is low, then a shift toward usage-based or migration-friendly terms once core verticals are in place.

3.2 Common Pitfalls to Avoid

1. No Clear Exit or Tapering Strategy

• Programs like SuperFest, which abruptly ended incentives, saw rapid TVL declines post-program.
• A gradual reduction strategy prevents a sudden drop-off in participation and supports sustained ecosystem growth.

2. Over-Reliance on a Single Incentive Type

• Many programs focused only on TVL incentives but ignored engagement,

  1. Lack of Monitoring & Evaluation

  2. governance participation, or trading activity.

  3. A diversified incentive structure, including trading incentives, governance rewards, and user retention mechanisms, is more sustainable.

3. Slow & Rigid Governance Processes

• Programs that required full DAO votes for changes (e.g., Optimism Season 7) faced inefficiencies, delaying necessary adjustments.
• Future programs should implement agile governance structures with delegated authority to facilitate faster decision-making.Optimism Phase 0 - Season 7 allowed projects to freely structure incentives, leading to inconsistent results.
• Programs need clear monitoring systems, periodic evaluations, and governance adaptability to ensure funds are allocated efficiently.

---

Recommendations

Lessons, Takeaways & Recommendations

1 Lessons and Takeaways

Section 1 captures the structural failures, operational blind spots, and behavioral insights surfaced by STIP, LTIPP, and related programs. These findings are informed by protocol- and program-level data, user surveys, and comparative analysis of incentive frameworks used across ecosystems. The takeaways emphasize what worked, what didn’t, and why, forming the foundation for forward-looking design principles.

1.1 Most Users Are ROI-Driven — Retention Depends on Ecosystem Strength

Incentives alone cannot sustain usage. Users consistently leave once emissions stop, unless they find other reasons to stay, including high-quality protocols, yield-bearing assets, strong UX, or composable infrastructure. This was evident in programs like STIP and STIP Bridge, where usage spiked temporarily but dropped sharply once incentives ended.

Most users are motivated primarily by return on investment (ROI) and the opportunity cost of their activity. Incentives work well to attract attention and capital, but users will only remain if the ecosystem offers lasting value. You cannot manufacture retention through rewards alone.

LTIPP underscored this lesson even more clearly: despite more structured grants, protocols that failed to build stickiness saw long-term metrics revert to pre-grant levels. Protocols like Vertex, which paired incentives with yield caps and longer-term LP programs, retained users more effectively.

User feedback reinforces this:

• “I farm incentives when the risk is worth it, can’t lie.”
• “Yes, I leave if I don’t believe in the project anymore. Incentives aren’t enough.”
• “Only stay if the ecosystem offers more than just financial rewards — good UX, projects, activity.”

When asked what keeps them in an ecosystem after incentives end, users pointed to sustained yields, active protocols, familiar tools, and unique opportunities, not the incentives themselves. One respondent summed it up well:

“If the yields remain good (a perfect example is Velodrome on Optimism), I’ll stay.”

Incentives can attract attention, but they cannot compensate for weak fundamentals. To drive long-term retention, programs must onboard users into valuable ecosystems through product quality, sustainable yields, and strong engagement infrastructure.

1.2 Bootstrapping Works — But Only with Proven Demand or PMF

Incentives are most effective when used to bootstrap new deployments, verticals, or product features, not to sustain usage in mature, saturated markets. Across STIP, STIP Backfund, and LTIPP, protocols launching new products, features, or deployments (e.g., cross-chain launches or v2 upgrades) outperformed those using rewards for general liquidity mining.

However, this approach only works when the product already demonstrates strong product-market fit (PMF) and users are willing to engage without continuous subsidies. Incentives cannot create organic demand if the underlying product is not compelling. External examples reinforce this: Sonic showed how a well-targeted program can launch an entirely new ecosystem. In contrast, zkSync’s Ignite program failed to retain activity despite its strong operational design, as it lacked differentiated value for users.

Our user surveys support this view. Users generally prefer earning incentives through assets they already hold or activities they already enjoy. This means not all bootstrapping opportunities are equal. You can’t bootstrap just anything; the best candidates are:

• “Protocols that have already proven demand on other chains and can bring that user base to Arbitrum.”
• “Assets that users already value elsewhere, which can be introduced with incentives to build liquidity and spark engagement.”

General-purpose, protocol-led bootstrapping rarely works. Programs must be intentional, targeting specific products, verticals, or use cases where Arbitrum has strategic opportunity, ideally where market share is low and high-quality demand can be captured.

A standout model is Avalanche Rush, which focused on bootstrapping high-profile partners into verticals where Avalanche lacked traction, especially during a period of low Ethereum affordability. The program worked not because of scale alone but because of focus, execution, and market fit.

Going forward, Arbitrum’s incentive programs should focus on vertical-specific bootstrapping tied to PMF and user demand. Incentives can truly shine in targeted campaigns, not broad handouts.

1.3 Incentives Must Be Actively Monitored — Not Just Deployed

Incentive programs without active monitoring or real-time data feedback, as seen in STIP and STIP Bridge, resulted in fragmented insights and limited adaptability. While STIP disbursed funds rapidly, it lacked infrastructure for adjusting emissions, evaluating effectiveness, or reallocating funds based on performance. STIP Bridge reintroduced the same design flaws with even less transparency.

LTIPP introduced modest improvements by establishing the Advisor and Council structure, which enabled proposal grading, halts in extreme cases, and basic KPI scaffolding. However, the absence of real-time dashboards, active analytics, and formal steering meant that useful feedback rarely led to meaningful changes during the program period.

A clearer model of effective monitoring comes from the zkSync Ignite program. In collaboration with OpenBlock Labs, zkSync deployed both a public dashboard and internal analytics systems to monitor performance throughout the program. Crucially, this was combined with a Data Steering Committee, which had the authority to recommend changes or halt the program. When results failed to meet expectations, the team chose to stop emissions early, distributing only 45 million of the planned 325 million ZK tokens. This saved resources and avoided the scenario where the full program runs its course without delivering the intended outcomes.

For Arbitrum, the takeaway is simple: Incentive programs should not operate on autopilot. They must include performance dashboards accessible to the DAO, operational teams responsible for monitoring results, and clear authority to pause or reallocate incentives midstream. Iteration is not a bonus feature; it is a basic requirement for deploying capital responsibly.

Without these structures in place, there is a high risk of capital being spent without generating meaningful retention or ecosystem growth. Monitoring is not just good practice; it is what separates experimental spending from strategic deployment.

1.4 Start With a Clear North Star — Then Align Objectives and KPIs

One of the most persistent weaknesses across STIP, STIP Bridge, and even LTIPP was the lack of a clearly articulated, program-wide goal. While individual protocols often defined their own KPIs, the incentive frameworks lacked thematic cohesion and strategic direction. This made it difficult to compare impact, assess success, or drive ecosystem-wide progress.

STIP operated on a first-come, first-served basis with minimal filtering by vertical, maturity, or ecosystem relevance. STIP Bridge largely repeated these flaws, functioning as an extension mechanism rather than a coordinated next phase. LTIPP introduced improvements, including vertical-based reviews and stricter KPI alignment. However, its effectiveness was undermined by the overlapping rollout of STIP Bridge, which diluted its ability to test scalable, coherent incentive models.

In contrast, zkSync Ignite began with one of the most disciplined designs of any recent incentive program. It was structured around a clearly defined North Star, expressed as Freedom, Progress, and Prosperity. This was then operationalised into four primary goals:

• Expanding onchain assets
• Increasing active builders
• Strengthening the zkSync community
• Securing the protocol

Ignite focused on the first three, translating them into tangible targets such as attracting new liquidity, deepening existing DeFi markets, onboarding high-quality protocols, and engaging power users and liquidity providers with prior histories on other chains.

The program introduced a hybrid objective- and metric-based approach. Protocols had to meet eligibility criteria such as minimum TVL thresholds, mainnet deployment, and integration with tools like DeFiLlama, Merkl, and OpenBlock Labs. Leading platforms, including Aave, Uniswap, Maverick, and PancakeSwap, participated. Incentivised user actions included liquidity provision, lending, trading, and bridging, all aimed at stimulating meaningful onchain activity.

A robust KPI framework guided the program:

• Targeting $5 to $10 in new liquidity per $1 of incentives
• Reducing slippage and improving trading efficiency
• Generating $3 in trading fees per $1 of incentives
• Retaining 60 cents of liquidity for every $1 gained
• Sector-specific metrics such as borrowing volume and trading depth

A bi-weekly reallocation process, managed by the DeFi Steering Committee (DSC), allowed for responsive adjustments based on real-time data. Rewards were shifted toward higher-performing protocols, and underperformers were defunded. In theory, this dynamic governance model gave Ignite the tools to manage capital efficiently and prevent waste.

However, the program’s structural strengths were insufficient to overcome its core limitation: the absence of meaningful product-market fit across much of the zkSync ecosystem. While TVL briefly grew during the incentive period, peaking with a reported $17.6 in liquidity per $1 spent, this figure was not sustained. Once incentives ended, most protocols experienced rapid outflows, and some ended with net negative TVL growth relative to their funding. Liquidity and users exited as quickly as they came, revealing that the underlying products were not compelling enough to retain them.

That said, Ignite’s operational design allowed for faster course correction. The program’s structure made it possible to track underperformance early and ultimately sunset the initiative entirely. While the outcome fell short, the program’s execution remained accountable and responsive.

The lesson is clear. A well-designed incentive program can optimise for efficiency, reduce waste, and exit early if needed. However, no operational precision can compensate for the lack of real user demand. Incentives must amplify what is already working. They cannot create product-market fit from scratch.

1.5 An Incentive Program Needs to Have a Taper in Rewards and Not a Sudden Drop

One of the recurring issues across Arbitrum incentive programs was the sharp cliff effect that followed their conclusion. Programs like STIP, the Backfund, and STIP Bridge ended abruptly, leading to fast declines in usage, liquidity, and trust. These hard stops left no room to observe whether protocols could retain users without continued emissions, a key test of product stickiness and long-term ecosystem value.

While tapering is often framed as a way to avoid disruption, its real strength lies in what it reveals. When emissions begin to decline, protocols can observe whether users continue engaging out of genuine interest or leave the moment rewards diminish. This allows the DAO to separate short-term extractive behavior from sustainable usage and informs better capital allocation going forward.

Tapering is especially important in:

• Programs designed to build long-term retention or user habits
• Protocols that reach a performance plateau mid-program and need to test their baseline
• Situations where incentive ROI is unclear, and tapering helps reveal marginal utility

In contrast, early-stage programs focused on bootstrapping may benefit more from clear, time-boxed emissions. But even then, clarity and predictability in timelines matter. Users in our surveys noted that sudden endings discouraged loyalty, while uncertainty around program duration led to disengagement:

• “It’s often unclear how long the rewards will last, and they just suddenly stop.”

When asked why they leave ecosystems after incentives end, responses included:

• “Because when the airdrop happened, there was no reason to stay.”
• “I think when I was farming Metis on Aave, I just took the money and left.”
• “If I’m just there for a quick bag, it doesn’t make sense to stay after rewards end.”

A more structured approach could look like:

• Defined emissions schedules with a built-in taper phase
• Tapering triggered by performance plateaus or market signals
• Clear, upfront communication to users and protocols about reward trajectories

Used strategically, tapering isn’t just about smoothing the curve — it’s about learning who stays, why, and what it takes to build retention. That makes it a valuable diagnostic tool in the DAO’s long-term incentive design arsenal.

1.6 Targeted Bootstrapping Outperforms Vague Growth Mandates

One of the clearest patterns we observed is that incentive programs perform best when structured around narrow, well-defined objectives. Vague or generic ambitions like “grow the ecosystem” or “support builders” are challenging to measure, and even harder to execute against. When the objective is clear, such as onboarding a specific type of user, bootstrapping liquidity in a thin vertical, or targeting a known inter-chain opportunity, the design becomes more focused, execution improves, and outcomes are easier to track.

In programs like STIP, goals were left to protocols to define. The result was a fragmented mix of initiatives with no common benchmark or overarching strategic direction. STIP Bridge doubled down on this by continuing without a revised purpose or shared KPIs. LTIPP began to introduce more alignment through vertical groupings and clearer KPI expectations, which was diluted by STIP Bridge running concurrently and diverting attention.

The DAO should not default to protocol-led planning. Generalized programs that put pressure on protocols to define their own success tend to underperform, especially when those protocols lack the operational expertise to design effective incentive models. Instead, incentives should be used to strategically bootstrap outcomes that the DAO can measure and learn from.

For future programs, this means defining specific, vertically aligned goals. These should:

• Be measurable against ecosystem-level benchmarks, such as Arbitrum’s market share relative to other chains in a given category.
• Be focused on bootstrapping activities that are showing early organic growth or represent structural expansion opportunities.
• Target user cohorts that Arbitrum has not yet captured but show active engagement elsewhere.

That said, goals like these should only be pursued if there is evidence that growth is possible. If a vertical is saturated or shows no signs of user interest, then bootstrapping may simply create noise. A robust framing of objectives must include a clear rationale for why this target matters now, what data supports its viability, and how success will be measured against realistic benchmarks.

Ultimately, specificity unlocks three benefits:

1. Tighter execution — protocols apply to a shared objective, not a blank slate.
2. Better tracking — metrics can be standardized, and performance can be compared meaningfully.
3. More meaningful iteration — failures teach something, because they were designed around a testable hypothesis.

By anchoring incentive programs around well-scoped, strategically relevant goals, the DAO can allocate capital more effectively, reduce waste, and develop structured experimentation that improves over time.

1.7 Incentive Programs Require Real Marketing and Real Dashboards

STIP and LTIPP both struggled to translate protocol-level incentives into meaningful user participation. Many users didn’t understand how to engage, how long programs would last, or how they were progressing. A lack of clear infrastructure, dashboards, trackers, FAQs meant even strong incentives were underutilized or misunderstood.

This isn’t just a communication issue. It’s a fundamental design flaw. Incentives don’t work unless users know how to use them.

Future programs must prioritize public-facing infrastructure from the start. A high-quality dashboard should clearly show users:

• Which protocols are participating
• What actions are being rewarded
• How long do incentives last
• How performance is being tracked

Additionally, coordinated messaging, led by a central operating team and supported by the Foundation, should ensure consistency across protocols and verticals. This reduces user confusion and helps protocols fit into a unified narrative.

Ignite’s use of both internal analytics and public dashboards is a helpful model. But visibility is not just for monitoring. It drives participation, improves transparency, and enhances credibility with users and the DAO.

Designing incentives without user-facing infrastructure is like setting up a race without telling anyone where the track is. Programs need to be visible, navigable, and explained. Otherwise, the incentives don’t reach their audience, and the program fails to achieve its potential.

1.8 Account for Market Cycles — Don’t Let Bear Markets Kill Momentum

Even the best-designed incentive programs are shaped by external conditions. Programs launched during bearish or stagnant periods tend to underperform on surface-level metrics, not necessarily due to poor design, but because users are less willing to take risks or move capital when market sentiment is low.

However, this does not mean incentive programs should be avoided during downturns. Rather, user acquisition becomes more expensive, and the bar for engagement is higher. In this context, success looks different: the goal isn’t necessarily explosive growth, but strategic positioning, acquiring mindshare, bootstrapping presence in key verticals, and preparing for the next cycle.

zkSync Ignite is a useful case study. Although well-structured and actively monitored, the program was sunset early due to poor market conditions. Yet its infrastructure, dashboards, steering committee, and adaptive emissions allowed it to limit waste and cut losses before distributing the full 325M ZK.

The lesson: programs must be flexible in scope and timing. Emissions, KPI thresholds, and success expectations should be calibrated to prevailing conditions. In bear markets, the DAO should focus on:

• Gaining market share in shrinking verticals, while competitors retrench
• Acquiring users from new segments or other ecosystems
• Building stickiness, even if topline metrics are modest

Downtrending, risk-off markets are not a time to go silent; they are an opportunity to build, test, and position. Incentives launched in these periods can yield strong long-term ROI if their scope is adapted to the moment.

1.9 Focus Incentives on Verticals With Product-Market Fit and Market Share Growth Potential

User surveys and program outcomes suggest that the most effective incentive strategies support products with existing product-market fit (PMF) and realistic paths to capturing greater market share. Users consistently report that they stay engaged after incentives only when there is long-term value, typically in the form of sustained yield, reliable user experience, or sticky utility.

This has two important implications for future program design:

First, incentives should not be used to prop up weak products or fill demand gaps where none exist. Programs like zkSync Ignite demonstrated that even well-executed incentive infrastructure cannot generate durable traction if underlying protocols do not resonate with users. Instead, incentives should amplify products and verticals already showing early signs of organic adoption or those with clear upside in underserved user segments.

Second, programs should focus on acquiring users in areas where Arbitrum can expand its market share or where demand is growing across the broader crypto ecosystem. For example, verticals like stablecoin liquidity, LRT infrastructure, or high-volume trading products with strong demand on other chains, but are underrepresented on Arbitrum, are better candidates for bootstrapping. Supporting these segments offers a more efficient path to ecosystem growth than spreading incentives thinly across saturated or declining markets.

In short, incentives work best when they accelerate momentum, not manufacture it. Targeted programs should prioritize verticals where the DAO can either (1) attract users away from other ecosystems by offering competitive experiences, or (2) reinforce retention in products that already deliver value. This approach reframes incentives as a tool for expanding market reach, not subsidizing product development.

By tying capital deployment to observed PMF and measurable share growth potential, the DAO can increase the likelihood of post-incentive stickiness, improve capital efficiency, and ensure that its funding supports ecosystem growth that endures beyond the incentive window.

2 Recommendations

Section 2 converts these lessons into a comprehensive incentive architecture: a blueprint for how Arbitrum can design and execute effective, efficient, and measurable programs going forward. This section covers five core design areas:

• Measurement and KPI frameworks
• Program structure and incentive delivery
• Governance and adaptability
• User targeting and retention
• Ecosystem coordination and marketing

Each recommendation reflects clear, testable ideas that build on past experience, with an emphasis on aligning incentives to strategic ecosystem goals.

2.1 Measurement, KPIs, and Data Infrastructure

Effective incentive programs require a shared understanding of success, how to measure it, and how to respond in real time. Arbitrum’s past programs fell short not because data was unavailable but because they were fragmented, inconsistently applied, and rarely tied to decision-making. Future programs must treat measurement as a core component of program design, not a reporting task bolted on after launch.

1. Define a Strategic KPI Hierarchy Before Launch

Every program should establish a tiered KPI structure that aligns ecosystem goals with protocol execution:

• Anchor top-level metrics to DAO-defined targets (e.g., grow LST market share by 15%)
• Translate these into protocol-level outputs (e.g., TVL or volume growth for LST protocols)
• Define expected user behaviors (e.g., 30-day retention, share of wallet, cross-app engagement)

This structure improves comparability across protocols and ensures capital is deployed to meet clearly stated goals.

2. Normalize KPIs to Evaluate Capital Efficiency

Avoid raw metrics. Programs should use normalized KPIs that allow benchmarking across protocols and ecosystems:

• TVL or volume retained per $ of ARB spent
• Market share gains at the chain, vertical, or subvertical level
• Incentive-adjusted retention and monetization metrics (e.g., return on emissions, liquidity stickiness)

These metrics contextualize performance and reduce the distortion caused by macro trends or vanity activity.

3. Fund Strategically Aligned Data Infrastructure

All programs require consistent monitoring and structured reporting. The DAO should fund a dedicated, strategically aligned data provider working under the oversight of the incentives operating unit. This infrastructure should:

• Track program KPIs and enable real-time cohort analysis
• Allow for vertical and cross-chain comparison
• Produce standardized, reproducible data exports that other researchers can build on

The operating unit should decide on a case-by-case basis whether to publish to a public dashboard, but all data must be shareable and structured around open standards to enable downstream analysis.

4. Build in Feedback Loops and Decision Hooks

Programs must include formal triggers for adaptation, not just post-mortem reviews. This includes:

• Mid-program checkpoints based on predefined thresholds
• Operating teams empowered to taper, pause, or redirect funds
• Dashboards and data tools that support live adjustments, not just retrospective reporting

Programs that embedded midstream feedback loops were better able to adapt in-flight, improving capital efficiency and outcome clarity.

2.2 Program Design and Incentive Architecture

To be effective, incentive programs must match their design to the problem they are trying to solve. Arbitrum’s most successful outcomes—both internally (e.g., STIP standouts) and externally (e.g., Sonic, Avalanche Rush)—came when design, distribution, and demand were tightly aligned. Programs failed when they lacked targeting, used static distribution, or funded products without organic traction.

1. Focus on High-Intent Bootstrapping, Not Broad Subsidies

Incentives work best when helping new protocols, markets, or user segments reach escape velocity. Programs should avoid “spray and pray” funding of established or unfocused projects.

• Prioritize verticals where Arbitrum can gain market share or meet underserved demand (e.g., stablecoin liquidity, high-volume trading, or LRTs)
• Align bootstrapping efforts with what users already do elsewhere: bridging familiar assets, using protocols with PMF on other chains, or testing verticals with rising momentum

Targeted bootstrapping tied to user demand and market share opportunity proves more effective than broad-based grants.

2. Incentivize Utilization Over Raw Growth

High TVL or volume doesn’t always reflect impact. Programs should reward usage and efficiency, not vanity metrics.

• Emphasize utilization metrics (e.g., volume-to-TVL ratio, fee-to-liquidity efficiency)
• Incentivize actions with clear user value (e.g., lending, bridging, staking), not idle deposits

Programs that reward meaningful user activity tend to outperform those driven by superficial TVL or volume targets.

3. Build for Adaptability

Programs must be flexible enough to respond to changing market conditions, protocol performance, or emergent opportunities.

• Design emissions schedules with mid-program adjustment hooks
• Use dynamic allocation models, e.g., reserve pools for outperformers or live redistribution away from laggards

Programs with built-in levers to respond to real-time performance were better at course-correcting and reallocating capital efficiently.

4. Taper Rewards to Test Retention

Avoid hard cutoffs. Use tapering to observe which protocols and user cohorts stick around after rewards diminish.

• Encourage protocols to gradually phase incentives down over time
• Require post-incentive metrics (e.g., 30-day retention, liquidity decay rates) as part of the evaluation

Tapering rewards creates space to observe genuine user interest and measure stickiness beyond incentive-driven usage.

5. Centralize Design Execution, Don’t Burden Protocols

Protocol teams should focus on shipping products, not designing emissions strategies. While some larger players may have internal capabilities, most protocols lack the expertise or bandwidth to craft effective, data-informed incentive plans. Misalignment here leads to wasted capital and inconsistent program outcomes.

• Delegate incentive design and iteration to a dedicated DAO incentive operations team or service provider
• Ensure all emissions strategies align with DAO-wide goals and KPI structures
• Let protocols opt into vertical-specific frameworks rather than building custom proposals from scratch

Execution was strongest when the incentive strategy sat with dedicated operators, not fragmented across protocol applicants.

2.3 Governance, Flexibility, and Strategic Alignment

Programs cannot be agile or capital efficient without dedicated operational leadership and clearly scoped execution paths. Arbitrum’s past incentive programs relied too heavily on fragmented governance processes, which limited responsiveness and blurred accountability. Future programs must prioritize clarity of mandate, execution authority, and alignment to DAO-wide strategy.

1. Establish a Dedicated Incentives Operations Unit

All programs should be designed and managed by a persistent team or vendor with expertise in incentive structuring, execution, and real-time monitoring. This unit should:

• Own continuity across cycles
• Ensure incentive design aligns with DAO priorities
• Manage protocol engagement, data pipelines, and performance reviews

Delegates and broader governance should define goals, not micromanage operations.

2. Use Structured Kill Switches and Revision Windows

Programs should include formal mechanisms to revise or halt emissions if key thresholds are not met. Avoid giving governance the task of real-time course correction. Instead:

• Build in predefined checkpoints for review (e.g., every 30 or 60 days)
• Empower the operating unit to recommend extensions, tapering, or early shutdowns based on performance
• Include kill switches in cases of abuse or persistent underperformance

This allows flexibility without unpredictable governance overhead.

3. Align Programs to DAO-Wide Strategic Goals

Incentive cycles should explicitly advance ecosystem priorities, whether increasing LST market share, onboarding native infra, or expanding into new user segments. Each program should:

• Define vertical-specific KPIs and ecosystem benchmarks upfront
• Tie reward logic to measurable ROI and marginal ecosystem impact
• Avoid overlapping, fragmented programs with unclear purpose

A clear through-line between DAO goals and program execution helps maximize coordination and comparability.

4. Include Sunset Conditions and Clear Extensions Logic

Programs must set expectations for how they end and under what conditions they are extended. Extensions should not be the default. Every round should:

• Define what success looks like in advance
• Require new approvals for continuation
• Avoid de facto rollovers without a clear performance justification

This avoids the pattern seen in STIP Bridge, where inertia drove funding extensions without fresh goals or structure.

2.4 User Targeting, Participation, and Retention

User acquisition is only half the battle. Retention is where incentives deliver real ROI, but most programs fail to plan for it. Arbitrum’s past cycles over-indexed on top-line growth, with little regard for which users stayed, why they came, or what they needed to continue engaging. Future programs must tailor incentives to high-value user segments and build around real participation, not raw numbers.

1. Target ROI-Driven Behavior Realistically

Most users optimize for yield. Programs should:

• Accept that many participants are profit-seeking
• Design rewards to attract these users, then convert them into longer-term participants
• Align incentives with user behaviors that create durable ecosystem value

Not all mercenary users are bad, but programs must give them a reason to stay.

2. Segment and Customize Incentives by User Type

Different users respond to different value propositions. Programs should:

• Use clear personas (e.g., first-time users, power LPs, builders, cross-chain migrators)
• Tailor onboarding incentives to each group’s needs and behaviors
• Avoid one-size-fits-all designs that dilute capital across misaligned use cases

User segmentation turns generic incentives into targeted acquisition funnels.

3. Support Habit Formation and Re-Engagement

Incentives are most effective when they reinforce sticky behaviors. Programs should:

• Use progressive milestones, lock-in periods, and re-engagement triggers
• Reinforce engagement via features like quests, streaks, or loyalty tiers
• Complement onchain incentives with offchain or community-driven nudges

Programs must teach users to return, not just visit once and vanish.

4. Design Participation Beyond the Incentive Window

Most incentive programs die when emissions stop. Programs should:

• Plan the user journey beyond the end of the reward period
• Connect initial actions to protocols with good UX, community, or long-term opportunity
• Track success based on continued user activity post-reward

Incentives should be an entry point, not a crutch.

5. Discourage Short-Term Extraction Without Penalizing High-Value Users

Over-concentration in whales or opportunists distorts incentives. Programs should:

• Use tiered rewards, claim limits, or anti-sybil heuristics to limit extraction
• Avoid designs that punish committed, high-capital users without reason
• Focus on mechanisms that reward long-term participation, not wallet size

The goal isn’t to block whales, it’s to make mercenary farming less attractive.

6. Plan the Post-Incentive UX

A seamless user journey ensures that incentives lead to sustained usage. Programs should:

• Direct users into protocols with clear use cases, good UX, and sustainable fees
• Highlight paths to continued value (e.g., native token rewards, governance, integrations)
• Design incentives to naturally hand users off to the next source of utility

If there’s no reason to stay, users won’t, even if they liked what they saw.

2.5 Ecosystem Collaboration, Marketing, and Communication

A good incentive program is only as effective as its distribution. Many Arbitrum programs underperformed not because the rewards were wrong, but because communication, tooling, and collaboration were missing. To scale participation, the DAO must treat marketing and communication as core infrastructure, not an afterthought.

1. Design with Marketing Built-In

Programs should launch with:

• A clear brand identity and message
• Defined distribution channels (X/Twitter, Discord, newsletters)
• A communications strategy supported by Offchain Labs and the Foundation

Even the best-designed incentives fall flat if users don’t know they exist or understand how to participate.

2. Build Unified Dashboards to Support Participation

Every program should provide real-time, user-friendly tracking:

• A single location for users to monitor program eligibility, rewards, and status
• Standardized KPI and retention metrics for DAO and delegate transparency
• Templates that protocols can reuse or build on

Dashboards support user decision-making, build trust, and reinforce program accountability.

3. Coordinate DAO and Foundation Messaging

To reach users effectively, messaging should be:

• Consistent across the DAO, Foundation, and involved service providers
• Anchored in the same goals, milestones, and outcomes
• Backed by timelines and media support that reinforce each program’s lifecycle

Fragmented communication erodes trust and obscures impact.

4. Encourage Cross-Protocol Coordination (When Aligned)

Composability can multiply the effect of incentives, but should not be forced. Programs should:

• Allow for stackable incentive strategies across related protocols

• Encourage joint submissions when vertically aligned (e.g., LST + bridge, or stablecoin + app)

• Avoid pushing protocols into artificial collaboration

Where ecosystem logic supports it, composable programs can create powerful synergies.

5. Track and Communicate Learnings Between Rounds

Incentive programs should never start from scratch. The DAO must:

• Require each program to publish a post-mortem or performance review
• Standardize formats for comparing results and refining hypotheses
• Maintain a running library of what worked — and what didn’t

Institutional memory is essential to long-term ecosystem efficiency.

3 Data-Led Reflections

Section 3 presents a set of operational insights from the perspective of those tasked with measuring impact. While Sections 1 and 2 address what should be learned and implemented, this section captures why some of those lessons failed to materialize and what the DAO must do to ensure its programs can be evaluated with clarity, comparability, and trust.

3.1 Combine Retention Metrics with Market Share Context

Topline metrics like TVL or volume can obscure what’s really happening. A protocol may retain users in a shrinking market or grow deposits while losing share to competitors. Without context, growth looks better than it is.

Compound’s LTIPP participation illustrates this well. The spike in deposit volume was driven not by new users, but by reactivated wallets, users already familiar with the protocol. While the surge aligned with BTC rallies, retention data showed these users returned mid-program, suggesting affinity with the product, not just macro noise. Still, without knowing how Compound performed relative to other lending protocols, it’s hard to say whether this growth was meaningful.

Key Insight: User retention metrics (e.g., cohort tracking, returning wallets) must be paired with vertical market share benchmarks to assess true adoption and value creation.

Reflection: Retention tells us who stayed. Market share tells us whether that mattered.

Recommendation: Track retention and market share together to distinguish real growth from surface-level activity.

3.2 Incentive Strategy Shifts Often Went Untracked

Many protocols adjusted their incentive strategies mid-program, changing targeting logic, shifting asset pairs, or transitioning from supply to borrow-side goals. While some shifts were justified, few were formally documented, tracked, or explained. This made it difficult to compare outcomes or learn from deviations.

Key Insight: Strategy evolution is natural, but when changes are undocumented or hidden across fragmented dashboards and forum posts, evaluators can’t identify what happened or why.

Reflection: The issue isn’t strategy drift, it’s the lack of a shared framework for capturing and contextualizing changes.

Recommendation: All material incentive strategy changes must be logged in a central, shareable format.

3.3 Fragmented Reporting Made Analysis Painful

Across STIP and LTIPP, incentive data were scattered across dozens of dashboards, many of which broke or stopped updating. Program timelines, emissions breakdowns, and core KPIs were often inconsistently presented or missing altogether.

Key Insight: Reconstructing program performance should not require forensic work. Analysts spent valuable time reverse-engineering missing datasets instead of producing insight.

Reflection: Inconsistent infrastructure led to duplicated effort, unreliable metrics, and weak continuity between program rounds.

Recommendation: Publish standardized datasets and reporting templates to reduce friction and enable reliable comparison across protocols.

3.4 Data Needs Operational Ownership

One of the most consistent blockers to evaluation was the absence of a dedicated, accountable entity overseeing incentive data pipelines. Whether metrics are self-reported or collected by third parties, the DAO must ensure that definitions, formats, and validation processes are coordinated centrally.

Key Insight: Without a designated team or provider to ensure consistency, protocol reports vary wildly, and comparisons collapse.

Reflection: Ownership ≠ centralization. But someone must define success, verify inputs, and stitch together a usable view.

Recommendation: Every program should pair its operating unit with a designated data lead responsible for metric curation and quality assurance.

3.5 Data Was Collected — But Rarely Used

Programs like STIP and LTIPP generated vast amounts of protocol data, but little of it was analyzed or reused. No shared library of what worked emerged, and reporting formats varied too widely to aggregate findings. With over 80 protocols participating in STIP, the DAO collected more than it could digest.

Key Insight: Analysis is only as good as its context and capacity. If we don’t know what data matters or don’t have time to interpret it, the value is lost.

Reflection: Most protocols reported something. Very few reported something meaningful, and almost none were measured against shared benchmarks.

Recommendation: Scope program size to match analytical capacity, or increase bandwidth and standardization first.