Incentive Spend: Real World Efficiency
Part Three: An example of Spend Efficiency in practice
Over the last two years or so, trading at decentralized exchanges has gone from a very niche concept to competing on a level with the largest traditional exchanges in many markets. With the rapid growth of volumes and revenues at decentralized exchanges, participants have developed new metrics to quantify their economics and understand how to run them more efficiently. Unlike a traditional exchange where users trade against each other in an order book, users at decentralized exchanges trade against Automated Market Makers (AMMs), which are smart contracts that execute their trades based on a preset formula. As explained in more detail in a past article, the core business model of an AMM is to attract assets from Liquidity Providers (LPs) to make them available for user trading. Though newer venues have developed ways to attract LP assets by offering price input, most AMMs pay their LPs an incentive for providing liquidity. As end customers and arbitrageurs trade against the venue to adjust their positioning or take advantage of small price deviations, they generate fee revenue to recoup the venue costs. To frame the discussion with some concrete numbers, let’s look at data from a real decentralized exchange in operation today. Since this is meant only as an illustrative example, we will stick to normalized figures and omit any specific dollar amounts and identifiable venue details.
The chart above shows the average liquidity provided over the trailing 30 days at this decentralized exchange, divided by the average daily incentive paid over the same period. A ratio of 1000, for example, would mean that over the past 30 days the venue was paying its LPs an average of 0.1% per day on their assets for making them available for trading. While this metric is a product of many individual pools that can all be adjusted to improve spend efficiency, having an aggregate view is useful in judging the overall trend and getting a sense of the economics of the exchange as a whole. In this case, the venue has attracted more liquidity per unit of spending over time, which suggests an improvement in spend efficiency at this level of the value chain.
However, attracting liquidity alone is not enough to run an efficient exchange. For fee-paying customers to trade with an AMM, the assets they actually want to trade need to be available. Similar to how a retail store would order inventory of items based on turnover, a decentralized exchange needs to ensure that the liquidity being attracted is utilized at a reasonable rate. While there are tradeoffs to utilization being too high, such as possible slippage and price deviations during high volume periods, declining utilization is a drag on efficiency as it means the venue is paying for more assets that are sitting idle.
In this chart, we see the average volume over the trailing 30 days at the example decentralized exchange, divided by the average liquidity available over the same time period. A reading of 0.1 on this metric would mean that over the last 30 days an average of 10% of the available liquidity was transacted daily. Excluding some occasional spikes for brief periods of high activity, utilization at this venue has mostly stayed in the range of 10–15% turn over on a daily basis. Though how much utilization is reasonable is ultimately a subjective question, having a quantifiable view of it can help exchanges understand how the mix of assets they are attracting fits with consumer needs.
To get a full view of the end-to-end Spend Efficiency of the exchange, we can combine the two metrics into a ratio of volume per unit of spending. This final chart from the sample venue shows the average daily volume divided by the average daily spend on incentives for the trailing 30 days. Since at most AMMs revenue is directly proportional to volume, this measure can tell us to what extent an exchange is recouping its incentive costs. For example, an exchange that charges a 1% fee on trades would operate at breakeven at a ratio of 100 on the chart, or a ratio of 200 for a 0.5% fee, and so on. This is important information for DAOs to know as paying more incentive than earned revenue dilutes the value of the entire operation. To be sustainable in the longer term, venues need to be aware of their Spend Efficiency and manage their operations to avoid wasteful spending.
With this problem in mind, Gauntlet launched its Incentive Optimization product this year, aiming to provide quantitative insights and recommendations on how to maximize Spend Efficiency. Through detailed off-chain modeling, Gauntlet can provide granular data on the expected impact of changes and help communities decide between them with the benefit of a rigorous forecasting framework. While incentive optimization has to date been mostly a trial-and-error process, the rapid growth of AMM activity has made errors more costly and the rewards of a forecasting approach more compelling. As decentralized exchanges start to plan for the future more strategically, we look to provide the tools they need to reduce excess spending and redirect incentives to where they generate the most desirable growth.
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If you’re interested in learning more about Gauntlet’s Incentive Optimization product, contact sales@gauntlet.network
