Analysing internal search functionality can bring valuable insights into user behaviour, preferences, and the effectiveness of our website's search functionality and search terms. By understanding how users interact with the internal search feature, we can tailor the content, optimise user experience, conversions, and drive meaningful improvements. Here, we'll explore some use cases for internal search term analysis. Additionally, we'll demonstrate a query using GA4 raw data export in BigQuery to retrieve the basis for underlying data.

Use case 1: Internal Search Impact on Conversion

By examining sessions with internal search and comparing them with sessions without the search function, while emphasising the purchase conversion rate for both groups, we aim to determine if users who use internal search are more likely to make a purchase compared to those who don't use the search feature. By analysing how these segments convert, we can evaluate the impact of the internal search function on driving purchase behaviour and tailor strategies to boost conversions for both groups.

Below is a query that retrieves search data based on the page location where the term 'search' is present (in case there is a search event please use that instead). It also includes querying purchase events. The resulting information is grouped by date and session ID.

SELECT
DISTINCT event_date, (
  SELECT value.int_value
  FROM UNNEST(event_params)
  WHERE KEY = 'ga_session_id' ) AS session_id, MAX(
    CASE WHEN (
      SELECT value.string_value
      FROM UNNEST(event_params)
      WHERE event_name = 'page_view'
      AND KEY = 'page_location' 
    ) LIKE '%search?%' THEN 1
  ELSE 0
  END
) AS SEARCH,
SUM(
  CASE WHEN event_name IN ('purchase') THEN 1
  ELSE 0
  END
) AS purchase
FROM `ProjectID.DatasetID.events_*`
GROUP BY 1, 2

The output table would look like the following (AVG CVR can be calculated):

Use case 2: Attribution of Revenue for Specific Search Terms

Evaluation of the revenue generated by users who engaged with particular search terms and proceeded to make a purchase allows us to understand the effectiveness of these search terms in revenue generation. This analysis enables efficient and thorough revenue tracking, enabling data-driven decisions to improve marketing strategies, product promotions, and internal search results. Moreover, there are a couple of KPIs that play a vital role in extracting actionable insights in this domain. A good example is Average Conversion Rate Per Session associated with the search term. This KPI is a significant indicator for evaluating performance and refining strategies in this context.

To query the data for this analysis, get inspired by the query above, you would need to add the revenue as well as the search term to the query to be able to use it for the analysis.

Both tables can then be exported to Looker Studio for further visualisation and in-depth analysis.

Happy analysis