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  • Writer's pictureRafael Machado

The Power of A/B Testing: Why You Should Give It a Shot

Companies are always looking for ways to stay ahead of the pack. One strategy that's gained popularity in recent years, mostly because of bigtechs, is A/B testing, a method of exposing customer to two or more versions of a product, web page, or application to see which one performs better. By using A/B testing, companies can make data-driven decisions, boost conversions, and create a better customer experience.

The Benefits of A/B Testing

So, what's the big deal about A/B testing? Here are some of the perks:

  • Data-driven decisions: A/B testing helps you make informed decisions based on real data, rather than relying on gut feelings or assumptions.

  • Continuous improvement: A/B testing encourages a culture of experimentation and continuous improvement, helping you stay ahead of the competition.

  • Reduced risk: A/B testing allows you to test new ideas and features in a controlled environment, reducing the risk of launching something that might not work out.

  • Company knowledge: Experimenting a lot helps companies to build a a great knowledge base of what works and what doesn't, which helps to generate new hypothesis and jumpstart a flywheel of improvement.

  • Adaptability: Experimenter companies tend to be more innovative and to adapt quicker to new technology and market trends

  • Leverage: When testing a in a sample of transactions you risk only a limited loss, but the gains from a successful test apply to your whole customer base.

Beyond Web Development: A/B Testing for Business Rules

A/B testing isn't just for web development and user experience optimization, as it was a few years ago. You can also use it to improve a huge deal of business rules. Some examples:

  • Credit granting: Testing different credit scoring models to optimize decisions for conversion, financial/trading margins, default rate;

  • Underwriting: Considering different risk assessment models to improve revenues, reduce variance and improve claim rate;

  • Pricing: Testing different pricing strategies and dynamic pricing models to maximize revenue

  • Anti-fraud: Identifying the most effective fraud detection logic to minimize losses

  • Next best offer: Iterating over NBO rules to optimize conversion and customer segmentation

Business Learning Cycle using A/B Testing

So, how do you get started with A/B testing? By adopting a systematic continuous improvement cycle, preferably supported by a good software platform to help you:

  1. Analyze historical data: Look at your past data to identify areas for improvement.

  2. Formulate hypothesis: Develop alternative solutions to improve results, and back test them in historical data if possible.

  3. Setup a version for testing: Create a version of the implementation for testing.

  4. Determine metadata: Define the test's metadata, including the hypothesis, expected results, sampling size, period of testing, bucketing strategy, shadowing, and potential risks.

  5. Deploy: Launch the test and start collecting data.

  6. Follow results: Monitor the test's progress, understanding that metrics should be observed statistically, with enough certainty to validate the results.

  7. Decide on success or failure: Determine whether the test was successful or not.

  8. Register learnings: Document learnings from both successes and failures.

  9. Promote new version: If the test is successful, promote the new version as the main version.

  10. Rinse and repeat: Continuously iterate and refine the process to drive continuous improvement.

Experimentation Budget and Risk Management

When deploying an experimentation setup it's essential to consider the maximum exposure the company is willing to take (percentage of customers to be exposed to experiments simultaneously). This can be understood as the amount of risk the company is willing to take with its experimentation strategy. By limiting the exposure, you can mitigate potential risks and ensure that experimentation doesn't negatively impact the business as a whole.

Beyond A/B Testing: Exploring Other Experimentation Methods

A/B testing is just one kind of experimentation method. Other approaches are gaining ground fast, expanding use cases for experimentation. For example:

  • Multi-arm bandit methods: Ideal for smaller sample sizes, these methods allocate traffic to multiple variants based on their performance.

  • Reinforcement learning (e.g., PPO): Allows for the creation of automatic improvement loops while maintaining the ability to explore new ways of doing things.

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