21 Nov 2018

Search Analytics

Search Analytics - Looking at the Query

Without any extra effort on your side, Algolia Analytics gathers large amounts of search-related information, capturing data with every search.

Technically, these metrics will get you thinking more objectively about how to index your product data, and how to configure your ranking and relevance; they can also lead you to use more advanced API settings that can improve your users’ search experience.

For your business, there are an unlimited number of benefits - You can see whether your products are properly described or represented, whether the correct products show up in your search results, and whether, based on the searches, you have too much of one product and not enough of another.

Specific search-related metrics include:

  • Overall search counts
  • A “No search result” rate
  • Top searches
  • Top searches with no results
  • Top results
  • If a filter is provided, top searches associated with that filter
  • Top filter attributes and filter values
  • Distinct count of IP addresses / Users
  • Top countries

Here’s an example of what you can expect to measure with Search Analytics:

Search analytics

Note that we are not yet talking about click analytics, which is described here.

Algolia collects Search Analytics for every customer, offering a detailed view of their end-users’ search activities. All customers can view this data on the Algolia Dashboard. However, to fetch it programmatically, using the Algolia Analytics API, you’ll need to be on a Business or Enterprise plan.

Let’s now take a look at a few of these metrics in more detail.

  • Improving your catalog and inventory. If most of your top searches involve products that fall outside your current product-line, or are under-stocked, you’ll want to review your inventory and catalog. For example, it is clearly business-useful to know what kinds and brands of shoes people are looking for.
  • Comparing products. You can see which product features are searched for most, and which features convert the most.
  • Re-examining product vocabulary. Which words do your users use? You can align their vocabulary with that of your database. An example of this would be sofa vs. couch: if “sofa” is typed more often than “couch”, your database needs to adapt to this usage to support both sofa and couch equally (by using additional attributes or synonyms, for example).

Specific Queries

  • Desirable results? You can see the most common results for any given set of search terms. Are these the results that you would have expected? Do you want these results? Is the order of the results correct? Is there an item missing? Or improperly appearing?
  • Useful filters? You can also examine the filters used in combination with searches. Is color-filtering more popular with shirts and dresses than with pants? What does filter usage say about your relevance settings? Can you better align your relevance with the most commonly-used filtering?
  • Are items you want to promote visible enough through search?
  • Are items you want to demote too visible in search?
  • Which categories of products are showing up the most?
  • How can you better describe the item?
  • What kind of search would you have expected to generate this result? And how did the search even generate this result?

No Results

  • Identify missing items in your catalog
  • Synonyms that you need to add
  • Improve your filters
  • The “overall no result” rate gives you a global barometer of your search implementation


  • Filter attributes: See which filter attributes are consistently used. If people are using size and color but not brand, maybe you should remove brand or put it lower on your page.
  • Filter Values: The same for filter values: if the colors red and yellow are used more often than purple, you can adjust your inventory by stocking less purple items, or adjust your index settings by favoring or promoting red and yellow over purple.

Geo Data

Metrics that tie search to location can help you learn about who your users are and where they come from. Geo data can help you assess the search and business needs for each country. Additionally, linking performance / latency with user location can help you better choose where to place your servers and DSNs.

Implementing Search Analytics

This is done automatically. There is nothing you need to do for Algolia to build your search analytic data. Collecting this data has no impact on the speed of search.

Reporting and Click Analytics using the API

Nonetheless, you can retrieve the results of your search analytics for reporting, and extend your analytics with Click and Conversion Analytics using our REST API. Go here for more about our REST API endpoints.

Additionally, we have a tutorial that explains how to use the Rest API.

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