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announced upcoming changes to its policies regarding the advertising of financial products in June. The new policy will see ads “initial coin offerings, cryptocurrency exchanges, cryptocurrency wallets, and cryptocurrency trading advice” blocked from being served to viewers. Despite the estimation that the new policy will come into effect in June, anecdotal evidence suggests that Google may already be making efforts to prevent crypto ads from being served.

Do you think that more companies will restrict cryptocurrency advertising on their platforms? Share your thoughts in the comments section below!

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Samuel Haig
Samuel Haig is a journalist and entrepreneur who has been completely obsessed with bitcoin and cryptocurrency since 2012. Samuel lives in Tasmania, Australia, where he attended the University of Tasmania and majored in Political Science, and Journalism, Media Communications. Samuel has written about the dialectics of decentralization, and is also a musician and kangaroo riding enthusiast.

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Figure 2: At 5 percent, an SLRT using the delete-a-group jackknife variance resulted in a far lower rate of false positives than the sequential likelihood ratio test with independence assumption.

Since our initial testing, this staged rollout framework has proven indispensable to many feature deployments at Uber. It has been widely adopted by different product teams and has caught regressions caused by features at a low rollout stage, thereby reducing their impact on users.

One such regression occurred during a login feature rollout that led to an outage in one small population of users. A new feature that allows users to login using their phone numbers as opposed to their usernames was deployed in a single city as a staged rollout. When we deployed this new feature for app users in this geographic area, we noticed a drop in their trip rates; through some investigation, we found many users in the treatment group were not able to successfully sign in to the app using their phone numbers. Thankfully, our XP’s staged analysis functionality was able to detect this before it was deployed to all of our users in the country and prevent a full-blown outage.

As Uber continues to expand to new cities and grow its operations in existing ones, the need to deliver real-time experiment results has become more important than ever before. For instance, city operation management teams frequently use the XP to fine tune new features, including testing out new SMS text messages and tailoring the driver onboarding experience for each city.

Due to the difficulties of working in different time zones, it previously took around a few days for the city operation managers to collaborate with the XP team to run an analysis report following a rollout. With our new framework in place, we have been able to cut down on execution time by a magnitude of days, delivering the experiment results to city operation teams no matter their location with very short latency.

To optimize the speed of our analytics, we initially pre-computed the statistical value of our business metrics in . However, this method of computing statistical values did not afford our end users enough flexibility to customize the definitions of their metrics.

The new analysis tool does not pre-compute the data of the metrics, which cut down on our data storage spend and reduced our analysis generation time. Now, when the data is ready for analysis, we use a query file to generate metrics on the fly whenever people make a request on the WebUI. After that, we use Scala as our service engine to compute the probability ( p-value ) that the treatment group mean is significantly different than the control group mean, determining if the experiment reached the target sample size.

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