Etsy Engineering | Unveiling the Collective Impact of Experiments
Introduction:
Stephane Shao and Clare Burke, from Etsy, discuss the importance of autonomous experimentation in the product development cycle. They explain how each team at Etsy conducts experiments to assess user response to new features and ideas. However, they also highlight the potential for interactions and dependencies when multiple teams experiment on shared pages or related products. To overcome this challenge, they introduce the concept of persistent experiments, where a holdout group is excluded from all experiments and then exposed to the combination of all successful treatments. They explain the benefits of this approach and the infrastructure and coordination required to implement it effectively.
Full Article: Etsy Engineering | Unveiling the Collective Impact of Experiments
The Role of Autonomous Experimentation at Etsy
Etsy, the popular online marketplace for unique and handcrafted goods, relies heavily on autonomous experimentation as part of its product development cycle. Each team within the company designs and conducts its own experiments to gauge user response to various changes and additions. This includes testing improved designs, added functionality, novel algorithms, and more. By measuring the impact of these user-facing changes, Etsy can continuously enhance the customer experience and inform strategic discussions and financial planning.
The Challenges of Isolated Experiments
While autonomous experimentation allows teams at Etsy to make decisions and learn independently, it also creates a complex ecosystem. Multiple teams often run experiments simultaneously on shared pages or closely related products. This can lead to interactions and dependencies that may render isolated experiment results misleading. To truly understand the overall business impact of changes made across all teams, it is important to view experiments as part of a larger common system and analyze their results in a holistic manner.
Understanding the Impact of Changes
Typically, teams at Etsy conduct A/B tests, where a random group of users is exposed to a new experience (treatment variant) and compared to a group of users exposed to the old experience (control variant). While these individual experiments provide valuable data to each team, it is crucial for the business and engineering organization as a whole to validate the collective impact of all changes. It is naive to assume that the impact of the whole is simply the sum of its isolated parts.
The Potential for Interactions
When different treatments are shown to a user simultaneously or within the same workflow, there is potential for interactions between them. Negative interactions occur when two or more treatments, each with a positive impact individually, have a lesser impact when presented together. For example, changing the background color of a navigation bar and the color of a cart icon individually may seem sensible, but combining both changes can create a barrier for users trying to retrieve their cart, potentially leading to cart abandonment. These interactions can be difficult to anticipate and quantify, especially in a complex ecosystem like Etsy’s.
Estimating Collective Impact with Holdout Groups
To estimate the joint impact of multiple changes, Etsy runs persistent experiments. These experiments consist of two stages conducted throughout a quarter. In the first stage, a small portion of online traffic is excluded from experimentation and forms a holdout group of users. The remaining eligible traffic is used to experiment and make decisions on which treatments to adopt and which to abandon. At the end of the quarter, a dedicated experiment called the comparison period is conducted, where half of the holdout group is exposed to the combination of all newly adopted treatments. This allows Etsy to measure the total incremental impact of the changes made during the quarter.
Challenges and Infrastructure
While persistent experiments make intuitive sense, there are practical challenges in defining, running, and analyzing them. Etsy needs a solid infrastructure to seamlessly transition between quarters and protect the comparison period’s results from contamination. Flexibility is also necessary to accommodate changes that fall outside the scope of persistent experiments and have to be presented to all users. Additionally, infrastructure and coordination are crucial for data engineering and experimental setup.
In conclusion, autonomous experimentation is a vital part of Etsy’s product development cycle. By understanding the overall impact of changes through persistent experiments and analyzing interactions and dependencies, Etsy can continuously enhance the customer experience and make informed strategic decisions.
Summary: Etsy Engineering | Unveiling the Collective Impact of Experiments
Etsy utilizes autonomous experimentation in its product development process, allowing teams to run their own experiments to gauge user response to new features and ideas. However, running multiple experiments concurrently can lead to interactions and dependencies that may skew individual results. To address this, Etsy is adopting persistent experiments, which involve excluding a portion of online traffic from experimentation throughout a quarter and running a dedicated comparison period at the end. This allows for a more holistic assessment of the collective impact of multiple changes, helping Etsy make informed decisions and improve its customer experience. Implementing persistent experiments requires a solid infrastructure and coordination among various stakeholders.
Frequently Asked Questions:
Q1: What is Machine Learning?
A1: Machine Learning is a subset of artificial intelligence that focuses on enabling computer systems to learn and improve from experience without being explicitly programmed. It involves the development of algorithms and models that allow computers to automatically analyze and interpret complex data, ultimately making predictions or decisions based on patterns and trends.
Q2: How does Machine Learning work?
A2: Machine Learning algorithms work by training on a large volume of data, which can be labeled or unlabeled. During the training phase, the algorithms learn to identify patterns and relationships within the data. Once trained, the models can then make accurate predictions or take actions when new data is introduced for evaluation.
Q3: What are the different types of Machine Learning?
A3: Machine Learning can be broadly classified into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled data, where the algorithm learns by mapping input data to correct output labels. Unsupervised learning deals with analyzing and clustering unlabeled data to discover patterns and relationships. Reinforcement learning involves training an agent that interacts with an environment and learns by receiving rewards or punishments based on its actions.
Q4: What are some real-world applications of Machine Learning?
A4: Machine Learning has a wide range of applications across various industries. Some common examples include:
1. Fraud detection in financial institutions
2. Recommendation systems in e-commerce platforms
3. Speech and image recognition
4. Predictive maintenance in manufacturing
5. Personalized healthcare diagnosis and treatment
Q5: What are the challenges of Machine Learning?
A5: While Machine Learning has tremendous potential, there are a few challenges that practitioners often face. Some of these include:
1. Data quality and availability issues
2. Overfitting or underfitting of models
3. Interpreting and explaining complex model predictions
4. Selecting the right algorithms and parameters for a given task
5. Ethical considerations surrounding bias and fairness in ML models
Overall, Machine Learning offers exciting opportunities, but it requires a thoughtful approach and continuous improvement to ensure its successful implementation.
