What is Bucket Testing? Examples & Best Practices
Bucket testing, also known as split testing or A/B testing, compares two or more variations of a web page, app, or other element to determine which one performs better.
To build a company that lasts, experimentation can't be just a tactic — it needs to be part of your core strategy.
That’s the through line that connects huge companies like Meta, Microsoft, and even OpenAI: They all got to where they are because they experimented.
One of the many ways a business conducts experiments is bucket testing. Let’s explore how it can improve key company metrics.
We’ll cover:
What is bucket testing?
Some examples of bucket testing
A step-by-step guide to conducting bucket tests
Bucket testing dos and don’ts
The importance of using specialized software for these experiments
What is bucket testing?
Bucket testing, also known as split testing or A/B testing, compares two or more variations of a web page, app, or other element to determine which one performs better.
The goal is to optimize the user experience and boost key metrics like revenue and retention. This means you can test ideas without doing any guesswork. Instead, you run rigorous tests and use data to evaluate your changes' effectiveness.
Bucket testing examples
Let’s look at 3 example scenarios where bucket testing can be useful:
Example scenario #1: Customer support channels
Your SaaS company wants to improve customer satisfaction and potentially ease the load on your support team. Currently, you rely on an email-based ticketing system (Variation A).
You decide to run a bucket test, where a portion of your customers continue with the existing support method (Variation A), while another group gets access to live chat during business hours and an expanded self-service knowledge base (Variation B).
You'll track customer satisfaction scores, how quickly issues get resolved, and whether the new options reduce email ticket volume.
Suppose the test group (Variation B) significantly improves these metrics. In that case, you'll know that live chat and a more robust knowledge base are valuable additions to your customer support strategy.
Example scenario #2: Agile sprint lengths
A software development team experiments with sprint lengths to improve efficiency. They compare the current four-week sprints (Variation A) to two-week sprints (Variation B), aiming to identify which accelerates development without reducing quality.
The key metric is the number of completed features. After testing, two-week sprints show a higher feature completion rate without increasing bugs, leading the team to adopt the shorter cycle for faster turnaround.
Example scenario #3: Pricing pages
For a subscription-based fitness app, you test different pricing strategies to find the one that maximizes sign-ups.
Variation A is your current pricing model, offering a single monthly subscription rate. Variation B introduces a new bundle with a yearly subscription option at a discounted rate and a 7-day free trial.
The key metric for success is the number of new subscriptions. After analyzing the data, you notice the bundled option with the free trial leads to a higher conversion rate, indicating customers favor the flexibility and perceived value of the new pricing structure.
How to run a bucket test in eight steps
Let’s go through each of the eight steps of running bucket tests:
Step 1: Define your goal — Clearly articulate your objective for the bucket test, such as increasing sign-ups to your SaaS, enhancing engagement, or boosting conversion rates.
A well-defined goal guides the design of your experiment and allows you to focus on improving overall user experience. A good specific example could be: Increase the percentage of free trial users who convert to paid subscriptions by 15% within 30 days.
Step 2: Formulate a hypothesis — Develop a theory on how making specific changes could positively affect your key metrics.
This hypothesis, which should be rooted in your understanding of your customers’ behavior patterns and current market trends, will direct your experiment's setup, underscoring the importance of aligning with user needs and expectations.
A good hypothesis could be: By adding an interactive onboarding guide within the product during the free trial period, we believe more trial users will understand the product’s value, leading to more revenue.
Read more: A Comprehensive Guide To Creating an Experiment Plan
Step 3: Choose your metrics — Identify specific, measurable metrics that directly relate to your goal, like higher revenue margins and retention. The right metrics are crucial for determining if your test was successful or not.
Step 4: Create your variations — Construct at least two variations of your webpage or user interface (though it doesn’t need to be a web-centric variable) and alter just the specific element you're testing.
This approach guarantees that any observed differences in performance can be attributed directly to the changes made, minimizing the influence of external variables.
Step 5: Segment your audience — Divide your audience into segments to experience the different variations, typically using a 50/50 split, though adjustments may be necessary based on the scale of your test and the expected impact.
Use feature management or experimentation software tools for segmentation, carefully considering the potential impact of external factors like seasonal changes or market trends on your results.
Step 6: Run the test — Launch and let your experiment run until you achieve a statistically significant sample size, checking your metrics and audience segmentation regularly to ensure the test's integrity.
The duration of your test should be long enough to account for natural variations in user behavior and external influences, ensuring the results are reliable.
Step 7: Analyze the results — After collecting enough data, evaluate which variation performed best according to your chosen metrics, focusing on statistical significance, to make informed decisions.
This data-driven analysis should consider not only the immediate metrics but also the potential long-term impact on customer retention and revenue generation.
Step 8: Implement the findings — Adopt the winning variation, integrating it into your site or user experience. Post-implementation, continue monitoring your key metrics to confirm the sustained success of the changes.
This step marks the beginning of an iterative process, where you use what you've learned to run more tests and keep making continuous improvements.
Bucket testing dos and don'ts
Running a bucket test isn’t always a simple task. To make sure you’re avoiding common pitfalls, here are some dos and don’ts:
Dos
Change one thing at a time: Keep your focus sharp by altering just one element per test. This strategy ensures you can clearly see what's making a difference.
Pick metrics that really count: Zero in on metrics that truly reflect your experiment's success. Choosing the right ones — like revenue, margins, and retention — is key to understanding your test's impact.
Go where the action is: Begin testing in sections of your site or product that see high user activity for quicker, more significant data collection.
Give It time to tell the tale: Stick with your test long enough to collect solid data. Rushing can lead you astray, so patience is crucial.
Commit to continuous testing: See bucket testing as an ongoing journey of refinement, leveraging each test's learnings for future experiments. Think of the long-term benefits of the refinement and iteration process.
Don’ts
Throw everything at the wall to see what sticks: Simplify your approach by focusing on testing one change at a time. This helps in accurately identifying what impacts user behavior, rather than getting lost in a sea of variables.
Jump the gun on results: It’s tempting to get excited when you start seeing early positive trends, but patience is key. Allow your test to fully run its course to confirm the data you gather is statistically significant and truly reflective of user behavior.
Settle for a winner too early: Today’s optimal solution may not hold up tomorrow. Markets evolve and so do user preferences. Make it a habit to periodically re-test successful changes to confirm they continue to perform well under new conditions.
Hit pause on testing: Avoid falling into complacency after a few rounds of tests. Continuous testing and experimentation foster a culture of continuous improvement. Keep challenging your hypotheses and refining your strategy to stay ahead.
Supercharge your bucket tests with Eppo
Eppo is an all-encompassing experimentation and feature management platform that simplifies the process of A/B tests.
Eppo simplifies testing with feature flagging and powerful analysis tools, helping you quickly understand what changes improve your product.
Catering primarily to data-driven teams in high-experimentation environments, Eppo enables organizations to conduct, analyze, and manage experiments with little to no risk and high precision.
Here’s a quick look at how Eppo can help with bucket testing:
Simplifies your workflow: Eppo provides one platform for the whole process: Setting up tests (with feature flags), gathering data, and analyzing the results.
Gets you data faster: Eppo helps you run tests quickly and see how they affect your key metrics, so you can make changes and improve based on real results.
Makes sure your data is reliable: Eppo uses advanced statistics to give accurate results, so you can trust your data when making decisions.
Gives you the insights you need: Detailed reports and analysis help you truly understand how customers react, leading to better product decisions.
Encourages experimentation across your team: Eppo's tools are easy for anyone to use, helping you build an experimentation culture where everyone uses data to improve the product.
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