Last time, we shared our not-so-neutral thoughts on how points programs are often run today. Points, coupons, and credits have thrived in the digital space since the ’80s because their value is clear and quantifiable. If your new scheme isn’t working, it’s not because users don’t enjoy loyalty programs as a concept. Lol, no. It’s because the design might need improvement.

Of course, building a system to validate and track every incentivized action is a massive engineering feat. That’s one-third of the story - and you can just use Absinthe for that. The other two-thirds involve quantifying results and iterating accordingly.

While user bases in crypto share broad traits, they’re made up of people with distinct behaviors. Projects need to adapt incentives to meet these needs while understanding that introducing incentive-driven behaviors will change that user composition. It’s like coffee—sure, you’re more productive when you drink it, but if you quit, expect headaches. And yes, some loyalty points may have addictive qualities that’ll cause withdrawal — so don’t mess up the design.

Now, let’s discuss some metrics to fine-tune that last third and iterate on your loyalty program’s impact. At Absinthe, we care about genuine growth, and here are some metrics our customers find invaluable, along with examples tailored for web3 loyalty programs.

PS - Product Stickiness

Definition: Gauges how frequently customers return to your product, indicating its perceived value and engagement.

Formula: Divide the number of daily active users (DAUs) by the number of monthly active users (MAUs)

Web3 Example: Product stickiness might track how often users engage in repeat interactions, like voting on governance proposals, minting NFTs, or staking assets. Suppose a user initially joins a DAO and stakes tokens to earn points. PS can help track whether users return to re-stake or vote again, showing whether the product’s core features maintain ongoing engagement or need reworking to stay relevant.

Event Bucketing Ratio

Definition: Track tags for each event that earns points or is otherwise monitored. Then look at which tags are being interacted by users more.

Web3 Example: In a web3 loyalty program, every tracked on-chain activity—like asset transfers, wallet interactions, or contract calls—can be bucketed with metadata (e.g., token type, event time, wallet address). For example, users who make swaps on a DEX might earn points based on the token’s volatility or the projects social following. Event bucketing lets programs sort these activities by type, making it easy to analyze user preferences or reward higher-value actions.

Point Portal Funneling

Definition: Tracks the sequence of events from a user’s initial click through to onboarding and beyond.

Web3 Example: In a blockchain-powered loyalty portal, funneling could follow the path users take from joining the program, linking accounts, completing their first action, and redeeming points. If users consistently drop off before completing account linking or onboarding, funnel data may indicate that the process is too complicated or unintuitive. Making adjustments based on these insights can improve the onboarding journey, boosting the chances users will stay engaged.

Event Funneling

Definition: Analyzes the sequence of user actions from initial interaction to specific milestones in the customer journey, identifying points where users drop off.

Web3 Example: For on-chain programs, event funneling could track users from initial actions—like participating in quests or engaging socially—through to higher-value actions like staking or swapping or providing liquidity. If users frequently stop short of swapping, it may indicate a lack of clarity or perceived risk around swapping. Adjustments can include clearer instructions, incentives for swapping sooner, or gamified rewards to encourage users to progress through milestones

FPC - Free-to-Paid Conversion

Definition: Measures the rate at which users move from free interactions to paid actions within a defined time period.

Formula: Divide the number of users performing at least one paid action by the total user count during a time period, then multiply by 100.

Web3 Example: In web3, “paid” actions might mean transactions that involve a gas fee or providing liquidity with a minimum number of tokens. For instance, a points program might track users who earn points for “free” by social interactions, quests or claiming free NFTs versus those who actively mint NFTs, complete swaps, or provide liquidity. Monitoring FPC helps highlight how effectively a program’s onboarding demonstrates product value, encouraging users to move beyond free interactions toward actions that require token commitment.

In essence, while creating a points-based loyalty program requires significant setup, continuously measuring and iterating on these metrics enables genuine user growth and engagement. By taking a close look at these behaviors, web3 loyalty programs can ensure they’re driving valuable interactions, boosting retention, and creating a truly rewarding user experience. Our advice is to start simple and build a good loyalty program over time!