Persistence (pStake) — Competitive Benchmarking and On-Chain Analytics

Product

Persistence runs pStake, a liquid staking protocol. Users stake a base asset (BNB, ETH, ATOM, XPRT) and receive a liquid staking token (LST) that keeps earning staking rewards while remaining usable elsewhere in DeFi - as collateral, in a lending market, or in a yield vault. The product only works if two things hold: the staking side is trustworthy, and the LST has somewhere liquid to go afterward.

Problem

pStake had headline numbers like TVL but no granular visibility into who its users actually were, whether deployed capital was being put to real use once issued as an LST, or how it genuinely stacked up against more established Western competitors. A one-off competitive glance would not answer that last question honestly - a real comparison needed every protocol measured the exact same way, or a difference in the numbers could just as easily be a difference in method. Underneath all of it sat a narrower, easy-to-miss risk: a shared tri-asset liquidity pool where one of the three legs could quietly drain while attention followed the two actively traded assets, with no visibility until a user hit a failed trade.

My role

Independent consulting engagement - on-chain analytics, competitive intelligence, and business development, owned end to end across roughly one year and 6-8 distinct projects.

Scope

Protocol deep-dives on pStake's own user base, vault and yield-integration analytics, a standardized competitive benchmarking framework across three protocols, tri-asset pool risk monitoring, and direct business development.

Approach

Went beyond total value staked to track wallet-level behavior - new versus repeat wallets, staking duration, and typical position size - so growth could be read as more than one rising line.

Broke deployed capital out by yield-integration partner (Beefy, Alpaca, OpenLeverage) and by underlying asset, surfacing which partnerships actually converted idle LSTs into active yield-seeking behavior and which were mostly unused.

The benchmarking framework

The centerpiece was a standardized benchmarking framework: the same query battery - pool metrics, volume, TVL, user counts, average and median staked - run identically across pStake, Stader, and ANKR, and across the same five shared DEX venues where their tokens traded. Every protocol measured the same way means a difference in the output is a real difference between the protocols, not an artifact of measuring each one differently.

Pool risk monitoring

A tri-asset liquidity pool added a specific technical risk: three assets can drift out of balance independently, and if trading skews toward two of them, the third can quietly thin out unseen. Built a near-real-time reserve-depth monitor on that pool, turning a problem the team would otherwise learn about from a user's failed trade into one they could see coming and rebalance ahead of.

The finding

The clearest finding - stated directionally, not as a precise measured percentage - was that average investment per user ran lower than the more established competitors. The read: newer protocols carry a real trust discount in DeFi, so the strategic move was courting capital that matches a smaller, staged ticket size rather than fighting a yield war against better-funded incumbents.

That insight led directly to a business-development outcome: an introduction to Cult DAO, a decentralized treasury fund whose staged-allocation profile matched exactly what the data pointed toward, which turned into a working partnership and a real liquidity inflow into pStake's products.

Outcome

• Standing analytics partnership - roughly one year, 6-8 delivered projects
• Repeatable 3-protocol benchmarking framework, apples-to-apples across pStake, Stader, and ANKR
• Tri-asset pool risk monitor - real-time reserve-depth visibility that did not exist before
• New-versus-repeat wallet and duration tracking surfaced a retention signal that a rising topline number was hiding
• Direct BD result: introduced and closed a partnership with Cult DAO, bringing fresh liquidity into pStake's products - the analysis pointed at treasury capital, and the follow-through actually brought some in

The trust-discount explanation for the ticket-size gap was inferred, not measured - a thinner secondary market for exiting large LST positions is an equally plausible driver, and the two point to different fixes. The pool-level data existed to start separating the two; instrumenting LST exit depth and slippage directly against ticket size would have grounded the recommendation in a measured cause rather than the more convincing explanation.