Forse is a data and intelligence platform built by StableLab, designed to help DAOs analyze the impact of governance decisions on protocol growth, key metrics, and operational efficiency.
We integrate onchain user and network metrics with offchain data, such as community sentiment and governance operations, to deliver actionable insights and in-depth impact analyses. Leveraging our deep expertise in governance operations, we empower stakeholders, including the Community Treasury Board (CTB), Grant Allocators (GAs), and the broader Polygon ecosystem, to optimize grant allocations and measure program effectiveness. Additionally, by enhancing transparency and demonstrating the real-world impact of governance initiatives, Forse helps build trust within the community and strengthen overall support for the program.
We work closely with protocol teams to understand their unique objectives and tailor our analyses using extensive cross-DAO datasets. Our experience with leading DAOs, including Sky (FKA MakerDAO), Arbitrum, Optimism, and Uniswap, positions Forse as the ideal partner for delivering a robust, data-driven analytics solution customized for the Polygon CTB’s needs.
Technical Approach:
Forse will deliver the Polygon Impact Terminal, a dedicated analytics space designed to provide monitoring, analytics, and actionable insights for Polygon’s Community Grants Program. This platform will enable the CTB, GAs, grantees, and the broader community to assess program effectiveness and its impact on ecosystem growth.
Core Features & Architecture
Dedicated Polygon Impact Terminal
A branded, interactive platform featuring:
- Season Summary Analytics: Aggregated insights into key program KPIs, including TVL growth, user activity, transaction volumes, funding allocations, social sentiment, and those specified by the CTBs. (Time-Series Analysis, Regression modeling)
- GA Performance Tracking: Evaluation of individual GA execution, funding disbursement efficiency, and grantee impact. (Casual inference)
- Comparison Tools: Track GA vs. Direct Track performance, analyzing which funding models deliver the highest impact per dollar spent. (Difference-in-Difference)
- Cross Season Comparisons: Compare KPIs across seasons and track the effectiveness of changes across seasons (Causal Time Series, Difference-in-Difference)
For all analytics we use jupyter notebooks in python with the respective frameworks on big-data servers.
Onchain & Offchain Data Aggregation
Onchain Metrics
Integration with Polygon’s main and test networks to track:
- Transaction volume, wallet growth, and TVL trends.
- Smart contract interactions for grant fund flows and user behavior tracking.
- Developer activity via smart contract deployments.
Offchain Data Sources
Incorporate:
- Social Sentiment Analysis: Measure community engagement, brand perception, and share of voice.
- Grantee and GA Self-Reporting & Surveys: Structured data intake from grantees and GA to validate performance and measure experience.
- Community Feedback Tracking: Analysis of forum/Discord discussions to identify feedback or critiques and refine grant allocation strategies.
GA & Grantee Performance Evaluation
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GA Profiles: Tracking onchain metrics, execution timelines, and project outcomes.
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Grantee Impact Metrics: Engagement tracking (user growth, retention, activity trends).
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ROI & Cost-Efficiency Analysis: Measuring impact per dollar spent.
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Post-Funding Monitoring: Long-term impact analysis beyond initial funding.
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Credential & Wallet-Gated Access: Restrict sensitive data to authorized users on request.
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Scalable Infrastructure: Designed to onboard future Grant Allocators and support future grants with minimal setup.
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Modular Design: Adaptable for new grant frameworks and evolving analytics needs.
Analytical Framework & Key Metrics
Our analysis spans 4 core dimensions:
GA & Grantee Performance
- GA funding efficiency: How well GAs allocate resources.
- Per-dollar impact tracking: Ecosystem growth per dollar spent.
- User segmentation & engagement tracking: Long-term retention & activity metrics, broken down by segments.
GA & Grantee Experience
- Most common issues & challenges: Identifying friction points in the grant process.
- Satisfaction scores (NPS): Measuring the experience of GAs and grantees.
Grant Effectiveness & ROI
- GA vs. Direct Track comparisons: Determine the most effective funding model.
- Vertical-based impact analysis: Evaluate which sectors (DeFi, AI, gaming, etc.) drive the most sustainable growth.
Sentiment & Ecosystem Awareness
- Polygon’s brand perception & visibility shifts: Analyze sentiment trends, frequent discussion topics, and share of voice.
- Community Engagement: Measure social media activity, mentions, and broader sentiment shifts.
Methodology
Our approach will combine onchain analytics, offchain data, and qualitative insights to provide a holistic view of grant effectiveness.
Data Collection & Integration
- Index onchain grant transactions, smart contract interactions, and wallet activity to monitor fund allocation, its usage, and end-user activity.
- Aggregate offchain metrics such as social sentiment, forum activity, and self-reported grantee updates.
- Establish automated data pipelines for continuous updates.
Analytics & Visualization
- Design interactive terminals for real-time monitoring of key program KPIs.
- Use comparative benchmarking (e.g., GA vs. Direct Track, Polygon vs. competitor programs) to identify high-impact funding models.
- Develop ROI assessment models to track efficiency per dollar spent.
Program Performance & Impact Evaluation
- Short-term Analysis: Track grant-driven user acquisition, TVL fluctuation, and social engagement.
- Mid-term Analysis: Assess grant allocator performance, funding efficiency, and program-wide growth trends.
- Long-term Analysis: Evaluate sustained user retention, post-funding project impact, and broader network effects.
Actionable Recommendations
- Identify best-performing funding strategies and areas for improvement based on quantitative and qualitative findings.
- Provide granular insights for GA optimization, community engagement, and ecosystem development strategies.
- Deliver customized reports with data-backed recommendations to refine future grant cycles.
Collaboration with CTB & GAs to Define Success Metrics & Impact Measurement
To maximize value, Forse will work closely with the CTB, GAs, and other key stakeholders to define success metrics, impact measurement frameworks, and tailored analysis methods that align with Polygon’s broader growth strategy. This ensures that the impact of grant funding is measurable, comparable, and actionable.
How We Ensure Effective Collaboration:
- Defining Success Metrics: Work with the CTB & GAs to establish key performance indicators (KPIs) and ecosystem-specific benchmarks.
- Impact & Performance Framework: Align with stakeholders to determine how to best assess GA execution, grantee impact, and ecosystem-wide growth.
- Custom Reporting & Analysis Definition: Develop tailored methodologies to track grant efficiency, incentive-driven behavior, and long-term sustainability.
- Iterative Refinement: Leverage ongoing feedback loops from GAs, grantees, and the broader community to refine measurement frameworks.
Leveraging StableLab’s Grant & Incentives Expertise
Forse will leverage StableLab’s deep expertise in grant and incentive management to ensure the success of the program and inform analytical design choices. This includes:
- Best practices from leading Web3 grant programs: Bringing insights from major governance ecosystems.
- Framework design for effective incentive distribution: Ensuring that Polygon’s funding mechanisms are optimized for maximum impact.
- Governance and operational guidance: Helping to align data insights with practical decision-making in grant allocation and incentive adjustments.
Deliverables and Timeline:
Our approach balances delivering high-value insights to Polygon while maintaining flexibility to refine and adapt based on ongoing learnings. Given the scale of Season 1, we propose an iterative execution strategy that prioritizes analysis first, followed by phased implementation of the Polygon Impact Terminal, additional analytics modules, and the definition of a General Analytics Framework to guide subsequent seasons.
Phase 1: Season One Impact Assessment & Initial Terminal Setup (Month 1 & 2)
- Establish access to relevant data sources (onchain + offchain). Secure necessary permissions from CTB, GAs, and other relevant Polygon teams.
- Conduct a deep analysis of Season 1 outcomes. Focus on key KPIs, including TVL growth, new wallet creation, transaction volumes, and GA effectiveness. Parallel qualitative assessment of grantee experience.
- Build the foundational Polygon Impact Terminal with core analytics, enabling initial monitoring. Stakeholder review of early insights through a staging version.
Deliverables:
- Structured analysis of Season 1 (forum updates & write-ups with early insights, avoiding extensive formal reports).
- Staging release of the Polygon Impact Terminal with initial data integrations and key program KPIs.
- Early engagement with key Polygon stakeholders (CTB, GAs) to validate metrics and framework.
Phase 2: General Analytics Framework & Terminal Expansion (Month 3)
- Define a scalable analytics framework based on Season 1 insights. Work with CTB and GAs to identify KPIs for continuous grant program tracking.
- Expand the Polygon Impact Terminal to support GA-specific tracking, sentiment analysis, and broader ecosystem growth indicators.
- Secure stakeholder feedback and iterate on the framework before further automation.
Deliverables:
- General Analytics Framework proposal based on Season 1 findings.
- Expansion of the Polygon Impact Terminal to support GA tracking and sentiment analysis.
- Frequent updates through forum posts with refined insights, avoiding time-consuming formal reports.
Optional: Phase 3: Data Automation & Self-Reporting System (Month 4)
- Coordinate with GAs to establish a streamlined data intake mechanism, allowing grantees and GAs to self-report.
- Integrate self-reported data into the Polygon Impact Terminal and automate reporting pipelines for ongoing analysis.
Deliverables:
- Mechanism for GAs & grantees to submit key performance data.
- Automated dashboards tracking live grant program impact.
- Iterative improvements to the Polygon Impact Terminal based on new data sources.
Optional: Phase 4: Monitoring & Future Recommendations (Month 4+)
- Ongoing monitoring over the course of 3-months after final delivery, refinement, and iteration based on real-time data. Work with Polygon stakeholders to evaluate future grant cycles.
Final Deliverables
- Fully functional Polygon Impact Terminal with real-time tracking and reporting.
- Data-driven recommendations to optimize future grant cycles.
- Defined framework for long-term grant analytics within the Polygon ecosystem.
Expertise:
Forse has successfully worked with leading protocols and grants programs, providing analytics and governance insights to projects, including:
SKY (MakerDAO)
Forse developed custom terminals for SKY to streamline reward management and accurately track integrator activity. Updated weekly, the terminal provides program participants with a transparent view of their performance, displaying USDS contributions, activity trends, and eligible rewards. Additionally, Forse conducted an in-depth analysis of user growth within the Sky and Spark ecosystems, offering valuable insights into user behavior, including engagement in active farm pools and migration trends. This comprehensive approach enables a deeper understanding of the key metrics driving protocol expansion and how various Sky components influence user participation.
Arbitrum Incentives
StableLab played a key role as program manager for Arbitrum’s incentive programs (STIP, LTIPP, STIP.B), overseeing the allocation of over 100 million ARB tokens to nearly 120 protocols across the ecosystem. Following the initial STIP program, we identified critical inefficiencies and designed the next iteration to enhance operational effectiveness, enabling the DAO to make more informed funding decisions and implement better incentive mechanisms.
Although Forse was not the data provider for these programs, our management experience showed us the importance of close collaboration with analytics teams. The existing data provider offered only basic visibility dashboards without deeper insights or methodologies, leaving us and the council members without the necessary data to make well-informed decisions.
To address this gap, we developed a comprehensive terminal after the program’s conclusion to better inform future iterations. This terminal tracked Discord and Discourse communications to assess how transitions from STIP to LTIPP improved the DAO and grantees’ experience while refining incentive structures. Additionally, we analyzed user segments and retention rates to determine which incentive models attracted different user types and how likely they were to remain engaged after the program ended.
Uniswap
Forse is developing a specialized terminal to analyze the impact of Uniswap’s Revitalization Incentives program across four chains. The terminal will provide key insights into liquidity provider (LP) behavior, tracking metrics like TVL growth, trading volume, ROI on incentives, and user retention. We are conducting a comparative analysis between incentivized and non-incentivized pools, assessing LP clustering based on activity and fund movements, and visualizing liquidity and reward flows. These insights will enable Uniswap to optimize their incentive strategies, improve capital efficiency, and refine future reward distributions for sustainable ecosystem growth.
Thrive
Forse partnered with Thrive to analyze multiple grant programs, including ThankARB and their role as a GA in Season 1 of the Polygon CGP through PolyMatch, delivering key insights into their impact. Through PolyMatch, we played a pivotal role in developing Thrive’s GA Impact Report for Season 1. We assessed grantee success by tracking funding distribution across sectors, milestone completion rates, and overall user engagement. We analyzed unique impressions and user interactions and identified whether grantee users were existing Polygon participants or new ecosystem members while distinguishing top-performing and underperforming projects. These insights enabled Thrive to refine its grant strategy, ensuring more effective funding decisions for Season 2 of Polygon’s GCP.
Team Leads
Dr. Christian Ziegler is the Chief Technology Officer of StableLab, analyzing Decentralized Autonomous Organizations. Previously, he worked as a researcher at the Technical University of Munich (TUM), where he earned his doctorate (Dr.) with summa cum laude for his research on Decentralized Autonomous Organizations. In 2018, he co-founded Blockcurators GmbH, which specializes in social wallets and social media marketing scaling to 60k MAU, single-handedly developing the software, before EOL. His previous peer-reviewed published scientific research includes a A Taxonomy of DAOs, scoring methodologies for DAOs, a network analysis of DAOs, a classification of DAO proposals using LLMs, a token based communication system, and a research agenda for DAOs.
Johannes Loewe is the AI and Data Lead at StableLab, where he focuses on all stages of AI and ML development, from experimentation to deployment. Before joining StableLab, he was a Freelance AI & Blockchain Software Engineer. He also has experience with DAOs, being a founding member of PretzelDAO in Munich. He holds a Bachelor’s degree from Radboud University in the Netherlands and a Master’s degree in Machine Learning from NUI-Galway in Ireland.
Marcos Miranda is the Head of Product at StableLab. With over 6 years of experience in Product Management, focusing on Web3 and Analytics products, he is also experienced in building DeFi protocols, having previously worked for other protocols in the space.
Cost
Our pricing structure aligns with Polygon’s preference for higher upfront investment while keeping ongoing costs low and predictable. Additionally, we utilize scalable designs and systems to ensure that as the number of Grant Allocators (GAs) and Grantees stabilizes, costs decrease over time.
We’ve structured our pricing around three key components:
1. One-time upfront cost for setup & foundational analytics
2. Per-season cost for ongoing analysis, with decreasing costs over time
3. Variable costs based on the number of new GAs onboarded and additional Grantees integrated
Initial Setup & Season 1 Impact Analysis
Cost: $125,000 USD (over the course of 4 months, at $31,250 USD per month - includes a $55,000 USD total discount)
This initial investment covers:
- Phase 1 - Season One Impact Assessment & Initial Terminal Setup ($70,000 USD - includes a $25,000 USD discount)
- Comprehensive evaluation of Season 1’s impact, informing key metrics and methodologies.
- Development of an interactive analytics platform with onchain/offchain data pipelines, visual dashboards (Forse Terminals), and KPI tracking.
- Phase 2 - General Analytics Framework & Terminal Expansion ($25,000 USD - includes a $10,000 USD discount)
- Standardized methodologies for measuring program, GA, and Grantee performance.
- Phase 3 - Data Automation & Self-Reporting System ($20,000 USD - includes a $15,000 USD discount)
- Development of data intake mechanism for GA and Grantee self reporting and subsequent integration to the bespoke analytics platform.
- Phase 4 - 3-months long Monitoring & Future Recommendations ($10,000 USD - includes a $5,000 USD discount)
- Ongoing monitoring, refinement, and iteration based on real-time data.
- Lite-reports and write-ups offering updates and recommendations.
This initial investment ensures Polygon has a high-quality analytics infrastructure from the start, eliminating the need for costly long-term maintenance fees.
Seasonal Analysis & Terminal Updates
Base Cost: $65,000 USD per season (with decreasing costs over time)
This includes:
- Grant Allocator & Grantee Data Processing – Impact tracking, onchain/offchain data ingestion, and detailed performance analytics.
- Terminal Enhancements – Upgrading and refining dashboards based on new insights and stakeholder feedback.
- Self-Reporting & Automation Features – Reducing manual workload for GAs & grantees, increasing operational efficiency.
As the number of new GAs and Grantees stabilizes, the cost per season reduces over time.
| Season |
Cost per Season |
Rationale |
| Season 2 |
$65,000 USD |
Full analysis & onboarding of all GAs & Grantees. Includes 3-months long Monitoring & Future Recommendations |
| Season 3 |
$55,000 USD |
Minor updates, assuming stable GA count and grantee pool. Includes 3-months long Monitoring & Future Recommendations |
| Season 4+ |
$45,000 USD |
Further cost reduction as automation reduces manual effort. Includes 3-months long Monitoring & Future Recommendations |
Variable Costs
To ensure fairness and scalability, we apply variable pricing only when additional integration work is required:
| Item |
Cost |
Justification |
| Per New Grant Allocator (GA) Onboarded |
$10,000 USD per GA |
Covers additional onboarding, data integration, and dedicated analysis. |
| Per Additional Grantee Integrated (beyond 100 per season) |
$1,000 USD per grantee |
Ensures that data processing scales with program growth. |
If the GA and Grantee count remains stable, these costs are not included in the seasonal cost.
References:
Rune Christensen - Cofounder Sky (fka MakerDAO):
“For over 7 years, the StableLab team has been contributing to the success of the Sky Ecosystem, consistently delivering strategic insights and impactful results.”
Daniel Jacobs - Founder, CEO Thrive Protocol:
“Forse has helped us bring critical data and insights to our initiatives. Their work isn’t just about the numbers, it’s about providing a clear, compelling story about what’s really happening on-chain. They are an important part of the web3 analytics stack.”
Greg Di Prisco - CoFounder Ajna Finance, Co-Founder M^0:
“Before working with Forse, we had a general sense of how users interacted with our lending pools, but their deep-dive analysis brought everything into focus. They uncovered trends we might’ve missed and helped us spot new opportunities for growth.”
