Back

A/B Testing

A/B Testing is a method of comparing two versions of a webpage or app against each other to determine which one performs better. By randomly showing users either version A or B, businesses can analyze which version leads to higher engagement, conversions, or other desired outcomes, enabling data-driven decisions.



Use Case

A digital agency wants to increase the conversion rate of a client's landing page. Currently, the page has a conversion rate of 2% with 10,000 monthly visitors, resulting in 200 conversions. The agency designs a new version of the landing page (Version B) with a different call-to-action button and headline.


Implementation:

  1. Traffic Split: 50% of visitors see Version A (original), and 50% see Version B (new).
  2. Duration: The test runs for one month to gather sufficient data.

Results:

  1. Version A: Maintains a conversion rate of 2%, resulting in 100 conversions from 5,000 visitors.
  2. Version B: Achieves a conversion rate of 3%, resulting in 150 conversions from 5,000 visitors.

Analysis:

  1. Improvement: Version B shows a 50% increase in conversions compared to Version A.
  2. Impact: If implemented, the new design could potentially increase monthly conversions from 200 to 300, leading to a significant boost in revenue or user engagement.

Conclusion: The agency recommends adopting Version B based on the A/B test results, demonstrating the power of data-driven design decisions.

Share: