Analytics - a 5-Minute Video Overview
Analytics - Modeling Your Users’ Search Behavior
Algolia’s Analytics engine offers search-related metrics that track your users’ actual search activity. By using specialized search metrics - like popular searches, no result rates, or click and search-to-conversion rates - you can ensure that your Algolia implementation is optimized to meet your business’ needs.
Our search analytics enables you to discover real search patterns, and to see which search terms, results, and filters follow or deviate from those patterns.
We see two broad benefits:
- Improve the relevance of your search
- Generate business insights into
- Your products and services
- Your inventory - less waste, better product availability
Additionally, search analytics can lead to discovering unexpected or hidden inefficiencies in both your Algolia implementation and your business.
The Search Bar as Feedback
Essentially, the search bar is being used as a feedback form or an impromptu user survey. Looking at it from this perspective, the search bar provides daily loads of useful feedback that offer invaluable search and business insights. Capturing this feedback in a meaningful and timely manner, and in an easy to read format, is critical to the success of our Analytics feature.
Two Kinds of Analytics: Search and Click
We provide two different kinds of analytics: one that looks closely at your users’ search behavior (search analytics), and another that measures what they do after a search (click analytics):
- Search Analytics facilitates modeling by breaking down user searches into numerous categories of data, like popular searches, number of “no results”, and filter usage. We’ll go into greater detail here. The goal of search analytics is to ensure that your search experience be data-driven.
- Click Analytics takes that data further, providing insight into what significant actions a user takes after performing a search: you can analyze click rates as well as define a single significant event - a conversion point - to see which searches lead most often to that event. Any catchable web event can be chosen as a conversion point: you choose the one you wish to track, and we start building a dataset around that event. See more on click analytics here.
To give a preview of how these metrics can help, let’s compare 3 queries that use different words to convey the same intent (to find an eco-friendly refrigerator):
- “refrigerator efficient” => Lots of results, click rate 50%
- “eco refrigerator” => Less results, click rate 10%
- “refrigerator saves energy” => No results
What Can We Learn?
Lesson #1 => Add synonyms between “eco refrigerator” and “refrigerator efficient”, or rename your products in your catalog to handle both terms equally
Lesson #2 => Same for “saves energy”
Lesson #3 => Your marketing and merchandising departments should use these keywords more often
Lesson #4 => Maybe you should create a filter/category for “eco-friendly”?
Lesson #5 => What do we learn from the different click rates? Maybe users prefer the word efficient over eco, so you can use the word “efficient” more often in your product descriptions, and/or promote products that describe themselves as “efficient”.
Now, if we make some of those changes, and therefore we get more or less the same results, what can we learn with conversion rates?
- “refrigerator efficient” => Conversion 10%
- “eco refrigerator” => Conversion 5%
- “refrigerator saves energy” => Conversion 5%
Lesson #6 => We can see that we were right to equalize the terms and to emphasize “efficient” over the others: given the higher conversion rate on the “efficient” query, it seems to be the preferred term.
Lesson #7 => Another lesson we could have learned, had “no results” appeared more often, or if you had low click rates, is that if none of your refrigerators are eco-friendly, and yet this is a popular search, you might want to consider adding these to your product line. Or If you have the items but not enough of them, you might want to improve your inventory. Search and Click analytics can therefore help you spot and follow buying trends.
Quick Implementation Summary
What you need to do:
- For Search Analytics, nothing. This is already being done during every search.
- For Click Analytics, you’ll need to add a new parameter to every search (clickAnalytics = true), which will setup the query as a starting point for click and conversion events.
- To capture click events, you’ll need to use the API to send the click event.
- To capture the conversion, you’ll need to use the API to send the conversion event.
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