We are listing here all the key opportunities we see we could seize with BigQuery ML for e-shops based on Google Analytics 4, Google Ads, the E-shop and Boxalino tracking / requests data.

Factor analysis of parameters affecting the performance of the PDP

Identify automatically what are the factors of a good performance or a bad performance of a PDP (product detail page) (especially for the case of visitors landing on the PDP from Google Shopping).

These parameters can be related to the product data, or the session data (device, …) or the combination of both (e.g.: if the users are on the mobile, a long textual description is not a good thing)

Data Available:

• Product properties
  the item brand and categories are indicated in GA4 view_item events, but Boxalino has a lot more product attribute from the e-shop, including (the properties would be provided as an associate array with property names and values: ARRAY<STRUCT<name, ARRAY<string> values>>):

  • brand

  • category (several levels)

  • price (before or after discount)

  • number of stars

  • state of discount (in discount and how much)

  • pictures (number of pictures, urls, …)

  • description (to see if the length or other aspects could play a role)

  • delivery state (in stock, …)

  • …

• GA4 data

  • view_item indicating that it is a PDP

  • page_views (to see what page view happens before / after, because it is maybe very different if it is a landing page)

  • traffic source

  • device

  • geo

  • …

Performance metrics:

• Engagement rate / Bounce rate

• Number of page views

• session conversion rate

• in scope conversion rate (not only there is a conversion, but there is a conversion with this product)

• session $ value (average value per session)

• AOV (averge order value)

Factor analysis of parameters affecting the performance of product recommendations on the PDP

Identify automatically what are the factors of a good performance or a bad performance of a product recommendation

These parameters can be related to the product data of either the PDP or of the recommended products, as well as the session data (device, …) or the combination of them (e.g.: on products of the brand X recommending products of the brand Y doesn’t work)

Data Available:

• Product properties (for both the PDP and the recommended products)
  the item brand and categories are indicated in GA4 view_item events, but Boxalino has a lot more product attribute from the e-shop, including (the properties would be provided as an associate array with property names and values: ARRAY<STRUCT<name, ARRAY<string> values>>):

  • number of stars

  • state of discount (in discount and how much)

  • pictures (number of pictures, urls, …)

  • description (to see if the length or other aspects could play a role)

  • delivery state (in stock, …)

  • …

• Boxalino tracking data

  • the display of each recommended product

  • the click on each recommended product

  • the add-to-basket following a click on the product

  • the purchase following a click and an add-to-basket on the product

  • test variant (it is often the case that there are several algorithms tested as different test variant, we could use the test variant as a factor to see if it makes a different (positive or negative) for specific products)

• GA4 data

  • view_item indicating that it is a PDP

  • page_views (to see what page view happens before / after, because it is maybe very different if it is a landing page)

  • traffic source

  • device

  • geo

  • …

Performance metrics:

• Click-Through-Rate (the number of times the recommended product is clicked compared to the number of times it is shown)

• Add-To-Basket-RAte (the number of times the recommended product is clicked and then added to the basket compared to the number of times it is shown)

• Buy-Through-Rate (the number of times the recommended product is clicked and then added to the basket and then bought compared to the number of times it is shown)

• Display $ Value (the average value bought by the Buy-Throughs compared to the number of times the product is shown, so the average value of showing the product as a recommendation)

• Engagement rate / Bounce rate

• Number of page views

• session conversion rate

• session $ value (average value per session)

• AOV (averge order value)