Concepts / Getting insights and analytics / Analytic Metrics, Reports
Jan. 07, 2019

Analytic Metrics, Reports

The Dashboard contains a series of reports that give you a full account of your end users’ search-related activity.

We have several overviews regarding how Search and Click Analytics can help you adjust and improve your search solution. This page goes into far more detail.

Dashboard - Overview Metrics Explained

Analytics metrics

Total Searches

  • How many searches were performed.
  • As-you-type searches are aggregated (eg. i → ip → ipa → ipad : count as one search).
  • Aggregation time frame : 30 seconds (eg. i → ip → ipa → ….30 seconds… → ipad : counts as a search for ipa and a search for ipad).
  • Searches passed with the param “analytics = false” are not counted (the browse tab in the Algolia dashboard includes analytics = false by default).

Users

  • How many unique users performed a search. If you implemented search from your back end, you need to forward the IP address of the user.
  • 1 user = 1 IP address.

No results rate (%)

  • Percentage of search that returned no results.
  • No results means that the Algolia API sent a JSON response containing no hits.

Click-Through-Rate (%)

  • Percentage of tracked searches (searches with clickAnalytics = true) where at least one result was clicked on by the user.
  • From XXX tracked searches : tracked searches are searches passed with the param “clickAnalytics = true”.
  • The number of click events received by Algolia / number of tracked searches by Algolia.

Conversion rate (%)

  • Percentage of tracked searches (searches with clickAnalytcs = true) where you signaled to Algolia that it led to a successful conversion (by sending the conversion event with the associated queryID).
  • Conversion events and clicks events are independent. A conversion is not linked to a click, only a queryID. Hence you can have conversions without clicks
  • Conversion time frame : 1 hour maximum from the search: number of conversion events received by Algolia / number of tracked searches in Algolia
  • The conversion window is not only related to conversion, but to all events. Events must be done (not received) within 1 hour of its search. Meaning after a user searches, we will consider the click and conversion provided they are done within the next hour. Furthermore, events must be received by the insights API within 4 days of their occurrence.

Click position

Average position of the clicks performed on the search results. Smaller values are better

Example: For a query “ipad”, if there was the following clicks:

  • 2 clicks on the 1st result
  • 1 click on the 3rd result
  • 1 click on the 10th result

The click position will be (1 + 1 + 3 + 10) / 4 = 3.75

Dashboard - Each Tab Explained

Analytics tabs

  • 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

Lost opportunities

By looking at click rates (as well as conversion, as discussed in more detail next), you can discover and avoid lost opportunities - for example, best-selling items which are not clicked or are too low in the results. This is crucial to your business. The benefit is always the same - improving relevance, or rewriting product descriptions, or updating your catalog.

Click position

Same as above: Average position of the clicks performed on the search results. Smaller values are better.

Filters

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.

General Discussion on Search Metrics

  • 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?

General Discussion on Click and Conversion Metrics

Click-Through-Rate (CTR)

The first of these measures is click rate. With search analytics, we discussed the usefulness of popular results. One step further is to look at click rates:

  • Did a user click-on one of the results? This is CTR - click through rate. You can test whether a search is successful by seeing how often one or more of its results are clicked. If you see that a CTR for a given search is 20%, this means that 20% of your users have clicked on at least one result of that search.

  • Did the results appear in the best order? This is the Average Click Position. If you see that the average click of an important search is on the 1st or 2nd result, this is clearly better than the 5th or 10th position.

Analyzing clicks tells you what is working and what is not, and justifies taking actions to rewrite descriptions, reconfigure index settings (ranking, relevance), or update your catalog.

Conversion

We often need to go beyond the next click to understand our users. We make this possible by assigning a unique tracking number to every search, so that you can trace that search to a single subsequent user action. Using the query tracking number (the “Query” ID), you can choose any single subsequent click, scroll, or other catchable web event to be your company’s global conversion point. Analyzing a global conversion point creates a benchmark - a realistic metric to gauge overall success as well better shape individual searches in the interests of your business.

Conversion is a simple yes/no metric

We measure whether individual queries lead your users to take a particular action. Let’s say you define the conversion as: “to put an item on the shopping cart”. How often does a search for “t-shirt red dyed” lead to placing an item in the shopping cart? And does “t-shirt” on its own work better or worse?

Several things to keep in mind:

  • You cannot define more than one event as a conversion point: whether you wish to define “buy a product” as the conversion point or “watch a video” - or whatever the choice - this choice becomes the company-wide conversion point for all search-to-conversion analysis.

  • If conversion=buy, and 1 search has generated 10 results, in which 5 of those lead to a buy, this is still considered to be 1 conversion not 5. The goal here is to see if a single search has reached at least one conversion.

Here is a small list of possible conversion points:

  • To buy
  • To build a wish list, a shopping cart
  • To watch a video, read an article
  • To make further inquiries, contact support
  • To explore further
  • To come to the store
  • What else? It depends on your business needs. Conversion points are agnostic.

Well-chosen conversions can feed back into your business decision-making. Done properly, Click Analytics should impact:

  • The kinds of products and services you offer
  • Your inventory
  • Diversification of products
  • Customer Service
  • Levels of Support
  • Website UI / UX

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