Why do A/B testing?

A/B testing has many uses, helps your business, improves your product line, gives you insights into best index and query configurations.

Relevance is the difference between having a search, and having a great search. The vast majority of our API methods and settings, as well as many of our dashboard features, are devoted to giving you direct control over the relevance of your results.

Once your search index is live and returning results, your next job is to make those results useful. That means evaluating relevance: whether what shows up first is what users really expect.

To get better results, you can:

• Tweak Algolia’s default settings (such as typo tolerance and language-handling).
• Set up your data and attributes differently.
• Add synonyms, rules and filters.
• Re-order your results with custom ranking and alternative sorting strategies.

Relevance tuning can quickly get quite advanced, but it’s necessary if you want a great search experience.

But how do you know what to change? Though there are many ways to improve relevance, A/B testing is one of the best. These structured, controlled tests ensure that your experiments are carefully set up, that they make sense, and that the conclusions you draw from them are sound, reliable, and data-driven.