05 Oct 2018

Analytics Introduction

Analytics - A 5-Minute Video

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.

Analytics overview

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.

Analytics scales according to your plan

The subscription plan for Search Analytics differs from that of Click Analytics.

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.

Click Analytics are only available to Business and Enterprise customers. They can be viewed in the Dashboard or fetched programmatically via the Analytics API.

An Example

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.

In summary

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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