Aaaand we’re back with another one. How does good data measurement affect the success of your points program? Well, significantly. Keeping a strong, iterative mindset is crucial. The need for iteration becomes clear when underlying customer behaviors evolve. Sometimes these behaviors shift due to incentives; other times, they change as your product adapts to new demands.

Today, we’re diving into additional metrics to help gauge the health of your incentive distribution. At Absinthe, we care about genuine growth, and here are some numbers our customers find invaluable, complete with examples tailored for web3 loyalty programs:

CPA - Cost Per Acquisition

Definition: Measures the cost of acquiring a new user through promotional activities, calculated by dividing total promotional costs by the number of new users acquired.

Web3 Example: In web3, CPA can measure the costs associated with driving users to complete a fixed on-chain action, such as minting an NFT, doing a swap or providing liquidity. For example, if you run a campaign with influencers and incentivize users with points for completing on-chain quests, CPA will help quantify how much in points and dollars you’re spending per active user. If CPA becomes too high, it might suggest that the promotional strategy needs rethinking, potentially shifting to more cost-effective methods like AMAs or tweak the amount of points being given out.

TFKA - Time to First Key Action

Definition: Tracks the average time it takes for users to complete a critical onboarding step after engaging with your points program.

Web3 Example: In a blockchain loyalty program, the “first key action” might be as simple as interacting with any protocol contract. If you’re running a campaign to onboard new users, tracking TFKA could help you determine how intuitive your onboarding process is. For example, if the TFKA to perform a single swap is significantly longer than expected, users may be facing friction points—such as confusing steps or lack of guidance—that could lead to drop-off. Shortening this metric indicates a smoother onboarding experience, critical for retention.

UU - Usage Uplift

Definition: Measures the additional revenue generated compared to if no points program had been setup

Web3 Example: Usage Uplift in web3 could track increased activity, like transaction volume or staking, following a points-based incentive campaign. For instance, if a DEX offers users points for every transaction made during the campaign, Usage Uplift helps determine the revenue boost from higher transaction fees. To quantify this, compare the transaction volume during the promotional period against the baseline period before and after the promotion. This can reveal whether the promotion’s effect lasted and contributed to sustained user engagement.

PU - Price Uplift

Definition: Tracks any increase in token price and volume resulting from a promotional activity compared to baseline levels.

Web3 Example: Suppose your loyalty program includes a campaign rewarding token holding or staking with bonus points. Price Uplift would measure whether this campaign positively impacted your token’s market metrics. By analyzing the token price, trading volume, and market cap before, during, and after the promotion, you can assess if the activity generated hype that positively influenced the token’s value. This insight is crucial to understanding whether campaigns drive actual token utility or merely speculative spikes.

NPS - Net Promoter Score (Customer Satisfaction Rate)

Definition: Measures customer sentiment around your project, capturing FUD (Fear, Uncertainty, and Doubt) versus HYPE.

Web3 Example: In web3, tracking community sentiment is vital, as the community is often deeply invested in the project’s success. Tools like Kaito can help monitor social media, forums, and other platforms for sentiment analysis, creating a score based on positive versus negative mentions of your project. If NPS scores drop after introducing a new points scheme, it may indicate dissatisfaction or confusion about the program, prompting adjustments. High NPS scores, meanwhile, show that users feel positively engaged, driving loyalty and long-term interest in your token and product.

In summary, effective data measurement and iteration in loyalty programs play a vital role in adapting to user behavior, refining your approach, and sustaining genuine growth. These metrics, especially in a web3 context, offer powerful insights into both the immediate and long-term health of incentive-based programs. By leveraging these data points, you can craft strategies that not only enhance user engagement but also align with the broader goals of the project, ensuring that your points program is truly impactful.