# Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces

DevFeed: [Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces](<https://devfeed.tech/articles/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-1235.md>)

Original publisher: [Read original article](<https://eng.lyft.com/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-9cb06d628f2d?source=rss----25cd379abb8---4>)

Author: Iraklikhorguani

Published: 2026-03-25T13:56:39Z

Content type: article

Language: en

Sources: [Lyft Engineering - Medium](<https://devfeed.tech/sources/lyft-engineering-medium.md>)

Topics: [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>), [App](<https://devfeed.tech/topics/app.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>)

Tags: [a-b-testing](<https://devfeed.tech/tags/a-b-testing.md>), [app](<https://devfeed.tech/tags/app.md>), [cost](<https://devfeed.tech/tags/cost.md>), [drivers](<https://devfeed.tech/tags/drivers.md>), [driving](<https://devfeed.tech/tags/driving.md>), [growth](<https://devfeed.tech/tags/growth.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [payments](<https://devfeed.tech/tags/payments.md>), [pricing](<https://devfeed.tech/tags/pricing.md>), [resources](<https://devfeed.tech/tags/resources.md>), [revenue](<https://devfeed.tech/tags/revenue.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

Lyft describes why standard A/B tests may not capture the long-term and market-mediated effects of pricing, payments, and incentive decisions in its multi-sided marketplace. The article introduces surrogacy and region splits as approaches for measuring those effects.

## Source excerpt

Image generated with Gemini 3 Pro (Google), 2026. Written by Amber Wang and Yoonji Kim at Lyft. Background Whenever you use the Lyft app, there is a complex balancing act happening behind the scenes. Various levers are used to keep the marketplace running smoothly; Base prices and coupons for riders affect demand, while driver pay and bonuses impact the level of available supply. Since every change to prices and payments impacts Lyft's costs and revenue, they lead to key optimization problems, such as: How should we allocate budget between driver incentives and rider incentives? How do we invest resources to achieve x% rides growth, and how much does it cost in terms of short term profit? These are the questions the Foundational Models team at Lyft tries to answer in a systematic way. A key ingredient is understanding the effects of different types of investments -- for instance, what will happen if we increase the total budget for driver incentives by x%? What will happen if we increase the rider price of all rides by y%? It's worth noting that the long term effects of such decisions tend to dominate the short term effects: we may earn more short term profit from a ride if we charge riders more and pay drivers less, but lose riders and drivers in the long run. Estimating the long term effects of resource allocation decisions is challenging in a multi-sided marketplace such as Lyft. Because these decisions tend to be consequential, their effects go beyond first order effects on directly affected users. For example, if we increase driver incentive spending by x% in week 1, drivers will drive more in week 1 (short term effect), and may return to drive a bit more in the following weeks (direct long term effects). But this is not the full picture: in week 1, when there is a positive increase in driver hours as the result of more incentives, riders will enjoy better experiences (e.g. less surge pricing, shorter wait times) and may want to return to Lyft in the future. How