Implementing a Bandit Algorithm in a Multiple-payout Scenario
Implementing a Bandit Algorithm in a Multiple-payout Scenario
There’s been a lot of recent excitement in the tech blogosphere about multi-armed bandit algorithms. Read this article to check out how Lucid Software tried their hand at implementing one.
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A/B tests—whether you love them or hate them, if you’ve ever tried to sell something on your website, you’ve probably heard of them. That’s because they’re a straightforward way to find out what’s profitable and what’s not. However, the word testing tends to make some people (*cough* management *cough*) understandably worried about losing revenue.
About Bandit Algorithms
Because of this, there’s been a lot of recent excitement in the tech blogosphere about multi-armed bandit algorithms. These are a family of algorithms that aim to strike a balance between exploration (pure testing, A/B style) and exploitation (garnering maximum revenue by using the best test arm) in order to minimize revenue loss (known as regret) during testing. Understandably, this idea has attracted a lot of fans.
However, bandit algorithms are not simply a patch for A/B tests. Rather, the two approaches are solutions to two different, closely related problems. A/B tests are designed for gathering data and determining statistical significance, whereas bandit algorithms are designed for minimizing regret. In many situations, however, bandit algorithms can actually converge to “significance”1 faster. From a human perspective, though, bandit algorithms are comparatively complex, and potential problems with the source data can be more clearly seen through an A/B test.
The functional idea behind a bandit algorithm is that you make an informed decision every time you assign a visitor to a test arm. Several bandit-type algorithms have been proved to be mathematically optimal; that is, they obtain the maximum future revenue given the data they have at any given point. Gittins indexing is perhaps the foremost of these algorithms. However, the trade-off of these methods is that they tend to be very computationally intensive.
Thompson sampling offers a very simple, elegant solution while remaining near-optimal. While it was first developed over 80 years ago, it was never widely implemented until about five years ago. The method is fairly simple:
- Keep track of the conversions and failures on each arm
- Use those values to generate a distribution of the probability of each arm to convert
- Sample each distribution
- Assign the next user to the arm with the highest sample
For each new user, new samples are taken from the updated distributions.
Our First Shot at a Bandit Algorithm
This summer at Lucid Software, we decided to try our hand(s) at implementing a Thompson sampling algorithm to test the taglines we show on our level selection page. The idea was that we could start the test, and then go hands-off and let the algorithm run its course, automatically selecting for the strongest taglines, or continuing to select from the strongest of them if no one tagline emerged as victor.
In practice, Thompson sampling is implemented using a beta distribution. This is because the beta distribution gives the theoretical estimate of the probability of a single event to occur, given its record of occurring and not occurring. It’s a common enough distribution that it’s included in virtually every stats package out there—including Apache Commons Math, which we used in our Scala implementation.
This works great when you’re only concerned with one event. However, in our case, we wanted to optimize each arm assignment for revenue—and there are lots of paths to revenue. Specifically, there are several different subscription levels that visitors can select, all with different conversion and revenue yields. To get an idea of how much the different conversion levels were worth relative to each other, we calculated an estimated Customer Lifetime Value (CLV) for each level.
Our first idea was to simply increment the number of conversions by a number more than one for each conversion above a free subscription. We scaled all the CLVs down by the CLV of a Free subscription, so that a Free would still correspond to 1, and used those scaled values to increment the conversion totals each time a user converted.
However, as we prepared to implement this idea, we realized that there was no theoretical basis for it, and that it destroyed the statistical integrity of our method. Specifically, it caused our algorithm’s confidence in a given arm to increase too quickly when any conversion above Free occurred. We were essentially equating the success of testing one user and having them convert at a premium paid level to the success of testing hundreds of users and having every single one convert to a Free subscription. While the monetary value might be the same in both scenarios, the latter would lead us to have much higher confidence in the arm’s effectiveness than the former.
To solve this problem, we decided to keep track of conversions for each subscription level independently on each arm, as well as total number of users assigned to that arm. From this data, we can derive the record of success that the arm has in converting to each different level and use that to initialize a handful of beta distributions representing the respective success of each level. We called these different conversion levels ‘fingers’, since each arm of the test had several of them.
Now, when a new user needs to be assigned to an arm, we sample each finger’s distribution, multiply each sample by the CLV of its corresponding subscription level, and add all the results together. (Although this result is not exactly the same as a random sample from the distribution of potential revenue for that arm, you could think of it like that.) We repeat this finger-summing process for every arm, and the user is assigned to whichever arm has the highest total sample value.
This multi-fingered, multi-armed approach allowed us to incorporate both the benefit of higher-value conversions and the higher likelihood of lower-value conversions into our assignment decision, while retaining the computational simplicity of the Thompson sampling algorithm.
Here’s an example of how our method changed its assignments dynamically. Here’s a plot of the distribution of assignments a couple weeks into the test:
And here’s a plot of the same distribution a few weeks later:
You can see that the D and F arms of the test started out as losing arms, but caught up as the test went on. The reverse happened to arm E. The I arm, however, stayed strong throughout the test.
We were excited that the test worked, since this was our first time trying out a multi-armed bandit algorithm. Additionally, the test demonstrated the feasibility of using many different payout levels to run and assess a conversion test.
To evaluate the benefit of dynamic optimization, we compared the total CLV of all the customers gained during the test with the hypothetical total CLV of having run the same test with a standard A/B/n assignment, given the historical conversion ratios during the test. Even though none of the taglines have really stood out as a winner or loser, so far, we’ve garnered $1800 more in CLV than we would have with an A/B test!
This was our first time including a bandit algorithm in our testing strategy. Not only did we successfully implement it, but we also tweaked it to meet our needs. We found a way to include different payouts in our algorithm by using multiple probability distributions, estimating each payout, and combining samples from each distribution according to the weight of its payout. We then demonstrated this on a 12-arm test. We’re excited to continue iterating on this approach and hope that this motivates others to give bandit algorithms a try.
Published at DZone with permission of Tanner Thompson . See the original article here.
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